Dissertations / Theses on the topic 'Online social networks'
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Vallapu, Sai Krishna. "Towards Network False Identity Detection in Online Social Networks." Thesis, Southern Illinois University at Edwardsville, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10246101.
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In this research, we focus on identifying false identities in social networks. We performed a detailed study on different string matching techniques to identify user profiles with real or fake identity. In this thesis, we focus on a specific case study on sex offenders. Sex offenders are not supposed to be online on social networking sites in few states. To identify the existence of offenders in social networks, we ran experiments to compare datasets downloaded from Facebook and offender registries. To identify the most suitable string matching technique to solve this particular problem, we performed experiments on various methods and utilized the most appropriate technique, the Jaro-Winkler algorithm. The major contribution of our research is a weight based scoring function that is capable of identifying user records with full or partial data revealed in social networks. Based on our data samples created using metadata information of Facebook, we were able to identify the sex offender profiles with real identity and seventy percent of the sex offenders with partial information.
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Rahman, Mahmudur. "Data Verifications for Online Social Networks." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2299.
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Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but also to curb the distribution of malware. Second, the increasing use of mobile visual data in news networks, authentication and banking applications, raises questions of its integrity and credibility. Third, through proof-of- concept implementations, we show that data reported from wearable personal trackers is vulnerable to a wide range of security and privacy attacks, while off-the-shelves security solutions do not port gracefully to the constraints introduced by trackers. In this thesis we propose novel solutions to address these problems. First, we introduce Marco, a system that leverages the wealth of spatial, temporal and network information gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Second, we propose FairPlay, a system that correlates review activities, linguistic and behavioral signals gleaned from longitudinal app data, to identify not only search rank fraud but also malware in Google Play, the most popular Android app market. Third, we describe Movee, a motion sensor based video liveness verification system, that analyzes the consistency between the motion inferred from the simultaneously and independently captured camera and inertial sensor streams. Finally, we devise SensCrypt, an efficient and secure data storage and communication protocol for affordable and lightweight personal trackers. We provide the correctness and efficacy of our solutions through a detailed theoretic and experimental analysis.
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Webberley, William. "Inferring interestingness in online social networks." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/68758/.
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Information sharing and user-generated content on the Internet has given rise to the increased presence of uninteresting and ‘noisy’ information in media streams on many online social networks. Although there is a lot of ‘interesting’ information also shared amongst users, the noise increases the cognitive burden in terms of the users’ abilities to identify what is interesting and may increase the chance of missing content that is useful or important. Additionally, users on such platforms are generally limited to receiving information only from those that they are directly linked to on the social graph, meaning that users exist within distinct content ‘bubbles’, further limiting the chance of receiving interesting and relevant information from outside of the immediate social circle. In this thesis, Twitter is used as a platform for researching methods for deriving “interestingness” through popularity as given by the mechanism of retweeting, which allows information to be propagated further between users on Twitter’s social graph. Retweet behaviours are studied, and features; such as those surrounding Tweet audience, information redundancy, and propagation depth through path-length, are uncovered to help relate retweet action to the underlying social graph and the communities it represents. This culminates in research into a methodology for assigning scores to Tweets based on their ‘quality’, which is validated and shown to perform well in various situations.
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Baatarjav, Enkh-Amgalan. "Privacy Management for Online Social Networks." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283816/.
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One in seven people in the world use online social networking for a variety of purposes -- to keep in touch with friends and family, to share special occasions, to broadcast announcements, and more. The majority of society has been bought into this new era of communication technology, which allows everyone on the internet to share information with friends. Since social networking has rapidly become a main form of communication, holes in privacy have become apparent. It has come to the point that the whole concept of sharing information requires restructuring. No longer are online social networks simply technology available for a niche market; they are in use by all of society. Thus it is important to not forget that a sense of privacy is inherent as an evolutionary by-product of social intelligence. In any context of society, privacy needs to be a part of the system in order to help users protect themselves from others. This dissertation attempts to address the lack of privacy management in online social networks by designing models which understand the social science behind how we form social groups and share information with each other. Social relationship strength was modeled using activity patterns, vocabulary usage, and behavioral patterns. In addition, automatic configuration for default privacy settings was proposed to help prevent new users from leaking personal information. This dissertation aims to mobilize a new era of social networking that understands social aspects of human network, and uses that knowledge to honor users' privacy.
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Cox, Shirley A. "Online social network member attitude toward online advertising formats /." Online version of thesis, 2010. http://hdl.handle.net/1850/11588.
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Alim, Sophia. "Vulnerability in online social network profiles : a framework for measuring consequences of information disclosure in online social networks." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5507.
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The increase in online social network (OSN) usage has led to personal details known as attributes being readily displayed in OSN profiles. This can lead to the profile owners being vulnerable to privacy and social engineering attacks which include identity theft, stalking and re identification by linking. Due to a need to address privacy in OSNs, this thesis presents a framework to quantify the vulnerability of a user's OSN profile. Vulnerability is defined as the likelihood that the personal details displayed on an OSN profile will spread due to the actions of the profile owner and their friends in regards to information disclosure. The vulnerability measure consists of three components. The individual vulnerability is calculated by allocating weights to profile attribute values disclosed and neighbourhood features which may contribute towards the personal vulnerability of the profile user. The relative vulnerability is the collective vulnerability of the profiles' friends. The absolute vulnerability is the overall profile vulnerability which considers the individual and relative vulnerabilities. The first part of the framework details a data retrieval approach to extract MySpace profile data to test the vulnerability algorithm using real cases. The profile structure presented significant extraction problems because of the dynamic nature of the OSN. Issues of the usability of a standard dataset including ethical concerns are discussed. Application of the vulnerability measure on extracted data emphasised how so called 'private profiles' are not immune to vulnerability issues. This is because some profile details can still be displayed on private profiles. The second part of the framework presents the normalisation of the measure, in the context of a formal approach which includes the development of axioms and validation of the measure but with a larger dataset of profiles. The axioms highlight that changes in the presented list of profile attributes, and the attributes' weights in making the profile vulnerable, affect the individual vulnerability of a profile. iii Validation of the measure showed that vulnerability involving OSN profiles does occur and this provides a good basis for other researchers to build on the measure further. The novelty of this vulnerability measure is that it takes into account not just the attributes presented on each individual profile but features of the profiles' neighbourhood.
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Sáez-Trumper, Diego. "Finding relevant people in online social networks." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/283658.
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The objective of this thesis is to develop novel techniques to find relevant people in Online Social Networks (OSN). To that end, we consider different notions of relevance, taking the point of view of the OSN providers (like Facebook) and advertisers, as well as considering the people who are trying to push new ideas and topics on the network. We go beyond people's popularity, showing that the users with a lot of followers are not necessarily the most relevant. Specifically, we develop three algorithms that allow to: (i) compute the monetary value that each user produces for OSN provider; (ii) find users that push new ideas and create trends; and (iii) a recommender system that allows advertisers (focusing in local shops, like restaurants or pubs) to find potential customers. Furthermore, we also provide useful insights about users' behavior according to their relevance and popularity, showing - among other things - that most active users are usually more relevant than the popular ones. Moreover, we show that usually very popular users arrive late to the new trends, and that there are less popular, but very active users that generate value and push new ideas in the network.L'objectiu d'aquesta tesi és desenvolupar noves tècniques per trobar persones rellevants en les Xarxes Socials a Internet. Així doncs, considerem diferents nocions de rellevància, tenint en compte el punt de vista dels prove ïdors del servei (com Facebook) i dels anunciants, però també de persones que intenten proposar noves idees i temes a la xarxa. La nostra investigació va més enllà de la popularitat de les persones, mostra que els usuaris amb molts seguidors no són necessàriament els més rellevants. Específicament, desenvolupem tres algorismes que permeten: (i) calcular el valor (monetari) que cada usuari produeix per al prove ïdor del servei; (ii) trobar usuaris que proposen noves idees i creen tendències; i (iii) un sistema de recomanació que permet als anunciants (centrant-nos en botigues locals, com ara un restaurant o un pub) trobar clients potencials. Addicionalment, lliurem informació útil sobre el comportament dels usuaris segons la seva rellevància i popularitat, mostrant, entre altres coses, que els usuaris més actius solen ser més rellevants que els populars. A més a més, mostrem que normalment els usuaris molt populars arriben tard a les noves tendències, mentre que usuaris de menor popularitat, però molt actius, generen valor i fomenten noves idees a la xarxa .
El objetivo de esta tesis es desarrollar nuevas técnicas para encontrar personas relevantes en las Redes Sociales en Internet. Para ello, consideramos diferentes nociones de relevancia, tomando el punto de vista de los proveedores del servicio (como Facebook) y de los anunciantes, pero también de las personas que intentan proponer nuevas ideas y temas en la red. Nuestra investigación va más allá de la popularidad de las personas, mostrando que los usuarios con muchos seguidores no son necesariamente los más relevantes. Espeficamente, desarollamos tres algoritmos que permiten: (i) calcular el valor (monetario) que cada usuario produce para el proveedor del servicio; (ii) encontrar usuarios que proponen nuevas ideas y crean tendencias; y (iii) un sistema de recomendación que permite a los anunciantes (centrándonos en tiendas locales, tales como un restaurant o un pub) encontrar potenciales clientes. Adicionalmente, proporcionamos información útil sobre el comportamiento de los usuarios según su relevancia y popularidad, mostrando - entre otras cosas - que los usuarios más activos suelen ser más relevantes que los populares. Más aún, mostramos que normalmente los usuarios muy populares llegan tarde a las nuevas tendencias, y que existen usuarios menos populares, pero muy activos que generan valor y fomentan nuevas ideas en la red.
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Recalde, Lorena. "Modeling users preferences in online social networks." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663756.
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L'objectiu d'aquesta tesi és desenvolupar nous i diversos mètodes per modelar les preferències dels usuaris a les Xarxes Socials Online. Els mètodes proposats tenen com a finalitat ser aplicats en àrees de recerca com la Personalització o Recomanació d'ítems i la Detecció de Grups d'Usuaris amb gustos similars. Aquests mètodes poden ser agrupats en dos tipus: i) mètodes basats en tècniques d'anàlisi de textos (Part I, Capítols del 3 al 5) i ii) mètodes basats en teoria de grafs (Part II, Capítols 6 i 7).
Amb els mètodes plantejats a la Part I és possible determinar el nivell d'interès dels usuaris en temes que són compartits en plataformes de microblogging. Hem pres com a cas d'estudi la participació digital de tweeters a la política. Els mètodes proposats a la Part II busquen definir un paper pels usuaris de les Xarxes Socials, ja sigui com a creadors o generadors de contingut i distribuïdors o consumidors de contingut. Hem plantejat un mètode on usuaris amb interessos similars però amb diferent rols són agrupats en una mateixa comunitat, de manera que els nous continguts es propaguen més ràpidament.El objetivo de esta tesis es desarrollar nuevos y diversos métodos para modelar las preferencias de los usuarios en las Redes Sociales Online. Los métodos propuestos tienen como finalidad ser aplicados en áreas de investigación como la Personalización o Recomendación de ítems y la Detección de Grupos de Usuarios con gustos similares. Dichos métodos pueden ser agrupados en dos tipos: i) métodos basados en técnicas de análisis de texto (Parte I, Capítulos del 3 al 5) y ii) métodos basados en teoría de grafos (Parte II, Capítulos 6 y 7). Con los métodos planteados en la Parte I es posible determinar el nivel de interés de los usuarios en temas que son compartidos en plataformas de microblogging. Hemos tomado como caso de estudio la participación digital de tweeters en la política. Los métodos propuestos en la Parte II buscan definir un rol para los usuarios en Redes Sociales, ya sea como creadores o generadores de contenido y distribuidores o consumidores de contenido. Hemos planteado un método donde usuarios con intereses similares pero con distinto rol, son agrupados en una misma comunidad de forma que nuevo contenido se propague más rápidamente.
The objective of this thesis is to develop new and diverse methods to model the preferences of the users in the Online Social Networks. The proposed methods are intended to be applied in areas of research such as Personalization or Recommendation of items and the detection of groups of users with similar tastes. These methods can be grouped into two types: i) methods based on text analysis techniques (Chapters 3 to 5) and ii) methods based on graph theory (Chapters 6 and 7). With the methods proposed in i) it is possible to determine the level of interest of users on topics that are shared on microblogging platforms. We have taken as a case study the digital participation of tweeters in politics. The methods proposed in ii) seek to define a role for users in social networks, whether as creators or content generators and distributors or content consumers. We have proposed a method where users with similar interests but with different roles, are grouped in the same community so that new content spreads more quickly.
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Bhardwaj, Shally. "Personality Assessment Using Multiple Online Social Networks." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31734.
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Personality plays an important role in various aspects of our daily life. It is being used in many application scenarios such as i) personalized marketing and advertisement of commercial products, ii) designing personalized ambient environments, iii) personalized avatars in virtual world, and iv) by psychologists to treat various mental and personality disorders. Traditional methods of personality assessment require a long questionnaire to be completed, which is time consuming. On the other hand, several works have been published that seek to acquire various personality traits by analyzing Internet usage statistics. Researchers have used Facebook, Twitter, YouTube, and various other websites to collect usage statistics. However, we are still far from a successful outcome. This thesis uses a range of divergent features of Facebook and LinkedIn social networks, both separately and collectively, in order to achieve better results. In this work, the big five personality trait model is used to analyze the five traits: openness to experience, conscientiousness, extroversion, agreeableness, and neuroticism. The experimental results show that the accuracy of personality detection improves with the use of complementary features of multiple social networks (Facebook and LinkedIn, in our case) for openness, conscientiousness, agreeableness, and neuroticism. However, for extroversion we found that the use of only LinkedIn features provides better results than the use of only Facebook features or both Facebook and LinkedIn features.
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Ahmad, Waqar, and Asim Riaz. "Predicting Friendship Levels in Online Social Networks." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3351.
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Abstract Context: Online social networks such as Facebook, Twitter, and MySpace have become the preferred interaction, entertainment and socializing facility on the Internet. However, these social network services also bring privacy issues in more limelight than ever. Several privacy leakage problems are highlighted in the literature with a variety of suggested countermeasures. Most of these measures further add complexity and management overhead for the user. One ignored aspect with the architecture of online social networks is that they do not offer any mechanism to calculate the strength of relationship between individuals. This information is quite useful to identify possible privacy threats. Objectives: In this study, we identify users’ privacy concerns and their satisfaction regarding privacy control measures provided by online social networks. Furthermore, this study explores data mining techniques to predict the levels/intensity of friendship in online social networks. This study also proposes a technique to utilize predicted friendship levels for privacy preservation in a semi-automatic privacy framework. Methods: An online survey is conducted to analyze Facebook users’ concerns as well as their interaction behavior with their good friends. On the basis of survey results, an experiment is performed to justify practical demonstration of data mining phases. Results: We found that users are concerned to save their private data. As a precautionary measure, they restrain to show their private information on Facebook due to privacy leakage fears. Additionally, individuals also perform some actions which they also feel as privacy vulnerability. This study further identifies that the importance of interaction type varies while communication. This research also discovered, “mutual friends” and “profile visits”, the two non-interaction based estimation metrics. Finally, this study also found an excellent performance of J48 and Naïve Bayes algorithms to classify friendship levels. Conclusions: The users are not satisfied with the privacy measures provided by the online social networks. We establish that the online social networks should offer a privacy mechanism which does not require a lot of privacy control effort from the users. This study also concludes that factors such as current status, interaction type need to be considered with the interaction count method in order to improve its performance. Furthermore, data mining classification algorithms are tailor-made for the prediction of friendship levels.
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Lewis, Makayla. "Cerebral palsy, online social networks and change." Thesis, City University London, 2013. http://openaccess.city.ac.uk/3011/.
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In 2011, 19.2 million households in the United Kingdom had access to the Internet. Online social networks (OSN) such as Facebook, Twitter, MySpace, Bebo and YouTube have proved to be the most popular Internet activity (Office of National Statistics, 2011). 49% of these users have updated or created an OSN profile and are making over 24 million visits a month (Dutton, 2009). These websites are often directed at a broad market i.e. people without disabilities. Unfortunately people with disabilities, especially those with physical impairments, often have a greater risk of experiencing loneliness than people without a disability as a result of their mobility, access and or communication impairments. Conventional communication methods such as face-to-face communication, telephone communication and text message communication are often difficult to use and can limit the opportunities for people with disabilities to engage in successful socialisation with family members and friends (Braithwaiteet al, 1999). Therefore people with disabilities can often see online communication, especially OSNs, as an attractive alternative. Previous studies such as Braithwaite et al(1999), Ellis and Kent (2010) and Dobransky and Hargittai (2006) suggests that OSNs are opening a new world to individuals with disabilities. They help these individuals, especially those exhibiting lifelong physical challenges to carry out social interaction which they would otherwise not be able to do within the analogue world. However due to inaccessible features presented in the technology for example features requiring JavaScript, hard-coded text size and Captcha (AbilityNet, 2008; Cahill and Hollier, 2009 andAsuncion, 2010) access to OSNs is often difficult. The overarching purpose of this PhD research is to understand the experiences and challenges faced when people with the physical disability cerebral palsy (cp) use OSNs. It is estimated that 1 in 400 children born in the UK is affected by cp (Scope Response, 2007). The disability can present itself in a variety of ways and to varying degrees. There is no cure for cp, however management to increase social interaction especially through technological innovations is often encouraged (United Cerebral Palsy, 2001; Sharan, 2005 and Colledge, 2006). Previous studies such as AbilityNet (2008), Cahill and Hollier (2009), and Boudreau (2011) have explored mainstream OSNs use from the perspective of users with disabilities, i.e. blind and visually or cognitively impaired, but have placed great emphasis on investigating inaccessibility of OSNs without involving these users. Other studies such as Manna (2005) and Belchiorb et al (2005) have used statistical methods such as surveys and questionnaires to identify Internet use among people with unspecified disabilities. Conversely Asuncion (2010) has taken a broader approach involving OSN users using high-level taxonomies to classify their disabilities, and Marshall et al (2006) focused on a specific disability type, cognitive impairments, without considering the variety of limitations present within the disability. Other studies such as Pell (1999) have taken a broader yet more specific approach and looked at technology use, especially computer and assistive technology among people with physical disabilities, where only 7 out of 82 surveyed had cp. Whereas Braithwaiteet al (1999) focused on individuals with disabilities, where most were classified has having a physical disability. However the study does not explicitly look at OSNs but rather at online social support within forums for people with disabilities. Studies such as these have not involved the users; defined what constitutes disability or focused on cp without encompassing other disabilities, making it impossible to identify the requirements of OSN users with cp. Initially this PhD research explored the experiences and challenges faced when individuals with cp use OSNs. Fourteen interviews were carried out consisting of participants with variations of the disability. The study identified the reasons for OSN use and non-use and also discovered key themes together with challenges that affected their experiences. This work was followed by an in-context observational study that examined these individuals context of use. The study identified the OSNs and assistive technology used, tasks carried out and users feelings during interaction. As a result of these studies it was determined that changing OSNs prevented and or slowed down these users ability to communicate online. Previous work within human-computer interaction and other disciplines such as software engineering and management science, change is often discussed during software development and is restricted to identifying scenarios and tools that assist change management within information technology (Jarke and Kurkisuonio, 1998). Studies such as these have not considered change deployment or its affect on users, though within HCI such an understanding is limited. Other disciplines i.e. psychology and social sciences have looked at change deployment. Theorists such as Lewin (1952), Lippett (1958) and Griffith (2001) attempt to offer solutions. However no one theory or approach is widely accepted and contradictions, adaptations and exclusions are continually being made. Conversely Woodward and Hendry (2004) and By (2007) have attempted to contend with these difficulties specifically stress as a result of change, believing that if change agents are aware of what an affected individual is thinking during the on set of change it will help to minimise or prevent damage. Studies such as these have focused on software development or organisational change from the perspective of developers or employees, they have not considered OSNs or individuals with cp. To fill this gap a longitudinal OSN monitoring and analysis study was carried out. The study identified how OSN changes are introduced, their affect on users, and the factors that encourage change acceptance or non-acceptance. The study was divided into three studies: two studies investigating realworld examples of OSN change by observing the actions of change agents (Twitter.com and Facebook.com) and their users reactions to the change process. A third study that asked OSN users about their experiences of OSN change was also carried out. A by product of these studies was a unique way of displaying OSN change and user acceptance on a large scale using a infographic and an inductive category model that can be used to examine OSN change. The findings from the five studies were then distilled alongside identified change management approaches and theories to develop an five-stage process for OSN change for change agents to follow. The process defined the requirements for OSN change including the change agent responsibilities before, during and after the change.
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Yang, Yile, and 楊頤樂. "Noncooperative information diffusion in online social networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206693.
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Information diffusion in online social networks has received attention in both research and actual applications. The prevalence of online social networking sites offers the possibility of mining for necessary information. However, existing influence maximization algorithms and newly proposed influence diffusion models do not distinguish between seed nodes (or pilot users) and nonseed nodes and assume all nodes are cooperative in propagating influence. This thesis investigates models and heuristics for noncooperative information diffusion in online social networks. It consists of three parts: tragedy of the commons in online social search (OSS), influence maximization in noncooperative social networks under the linear threshold model (LTM), and influence maximization in noncooperative social networks under the independent cascade model (ICM).
Firstly, the tragedy of the commons problem in OSS is considered. I propose an analytical model that captures the behavior of OSS nodes, and, from a gaming-strategy point of view, analyze various strategies an individual node can utilize to allocate its awareness capacity. Based on this I derive the Pareto inefficiency in terms of the system cost. An incentive scheme which can lead selfish nodes to the “social optimal” state of the whole system is also proposed. Extensive simulations show that the strategy with our proposed incentive mechanism outperforms other strategies in terms of the system cost and the search success rate. The second part of the thesis presents the first detailed analysis of influence maximization in noncooperative social networks under the LTM. The influence propagation process is structured into two stages, namely, seed node selection and influence diffusion. In the former, I introduce a generalized maximum-flow-based analytical framework to model the noncooperative behavior of individual users and develop a new seed node selection strategy. In the latter, I propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey-Clarke-Groves-like (VCG-like) scheme to incentivise cooperation. Then I study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. The proposed schemes are evaluated on large coauthorship networks, and the results show that the proposed seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative.
Finally, I study the influence maximization problem in noncooperative social networks under the ICM using the same two-stage framework originally proposed for LTM. For the seed selection stage, a modified hierarchy-based seed node selection strategy which can take node noncooperation into consideration is introduced. The VCG-like incentive scheme designed for the influence diffusion stage under LTM can also be utilized for ICM in a similar manner. Then I also study the budget allocation problem between the two stages. The evaluation results show that the performance of the hierarchy-based seed node selection scheme is satisfactory in a noncooperative social network and the VCG-like scheme can effectively encourage node cooperation.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Marks, Christopher E. (Christopher Edward). "Analytic search methods in online social networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112012.
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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 175-185).
This thesis presents and evaluates methods for searching and analyzing social media data in order to improve situational awareness. We begin by proposing a method for network vertex search that looks for the target vertex by sequentially examining the neighbors of a set of "known" vertices. Using a dynamic programming approach, we show that there is always an optimal "block" search policy, in which all of the neighbors of a known vertex are examined before moving on to another vertex. We provide a precise characterization of the optimal policy in two specific cases: (1) when the connections between the known vertices and the target vertex are independent, and (2) when the target vertex is connected to at most one known vertex. We then apply this result to the problem of finding new accounts belonging to Twitter users whose previous accounts had been suspended for extremist activity, quantifying the performance of our optimal search policy in this application against other policies. In this application we use thousands of Twitter accounts related to the Islamic State in Iraq and Syria (ISIS) to develop a behavioral models for these extremist users. These models are used to identify new extremist accounts, identify pairs of accounts belonging to the same user, and predict to whom a user will connect when opening an account. We use this final model to inform our network search application. Finally, we develop a more general application of network search and classification that obtains a set of social media users from a specified location or group. We propose an expand -- classify methodology which recursively collects users that have social network connections to users inside the target location, and then classifies all of the users by maximizing the probability over a factor graph model. This factor graph model accounts for the implications of both observed user profile features and social network connections in inferring location. Using geo-located data to evaluate our method, we find that our classification method typically outperforms Twitter's native search methods in building a dataset of Twitter users in a specific location.
by Christopher E. Marks.
Ph. D.
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Laraqui, Jawad. "Activity based interfaces in online social networks." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41658.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 51).
The goal of the project is to explore how activity-based interfaces can create more meaningful experiences for the users and builders of online social networking sites. Medina, a social-networking site based on the idea of exchanging knowledge, explores new interfaces for visualizing connections between people and ideas. The site constantly measures interactions between people and their interests in order to create a more accurate picture of what relationships and information are important.
by Jawad Laraqui.
M.Eng.
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Xu, Hailu. "Efficient Spam Detection across Online Social Networks." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.
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Hong, Dan. "Sharing private data in online social networks /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20HONG.
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Fidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.
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Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e FormaçãoThis research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.
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Chiu, Wei-Yi. "The analysis of social capital in online social communities." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46995/1/Wei-Yi_Chiu_Thesis.pdf.
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Social networks have proven to be an attractive avenue of investigation for researchers since humans are social creatures. Numerous literature have explored the term “social networks” from different perspectives and in diverse research fields. With the popularity of the Internet, social networking has taken on a new dimension. Online social communities therefore have become an emerging social avenue for people to communicate in today’s information age. People use online social communities to share their interests, maintain friendships, and extend their so-called circle of “friends”. Likewise, social capital, also known as human capital, is an important theory in sociology. Researchers usually utilise social capital theory when they investigate the topic relating to social networks. However, there is little literature that can provide an explicit and strong assertion in that research area due to the complexity of social capital.
This thesis therefore focuses on the issue related to providing a better understanding about the relationship between social capital and online social communities. To enhance the value within the scope of this analysis, an online survey was conducted to examine the effects of the dimensions of social capital: relational capital, structural capital, and cognitive capital, determining the intensity of using online social communities. The data were derived from a total of 350 self-selected respondents completing an online survey during the research period. The main results indicate that social capital exists in online social communities under normal circumstances.
Finally, this thesis also presents three contributions for both theory and practice in Chapter 5. The main results contribute to the understanding of connectivity in the interrelationships between individual social capital exchange within online social networks. Secondly, social trust was found to have a weak effect in influencing the intensity of individuals using online social communities. Third, the perpetual role of information sharing has an indirect influence on individual users participating in online social communities. This study also benefits online marketing consultants as marketers can not only gain consumer information easier from online social communities but also this understanding assists in designing effective communication within online social communities. The cross-sectional study, the reliability of Internet survey data, and sampling issues are the major three limitations in this research. The thesis provides a new research model and recommends that the mediating effects, privacy paradox, and social trust on online social communities should be further explored in future research.
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Lei, Siyu, and 雷思宇. "Online influence maximization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/210187.
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Social networks, such as Twitter and Facebook, enable the wide spread of information through users’ influence on each other. These networks are very useful for marketing purposes. For example, free samples of a product can be given to a few influencers (seed nodes), with the hope that they will convince their friends to buy it. One way to formalize marketers’ objective is through the influence maximization problem, which is to find the best seed nodes to influence under a fixed budget so that the number of people who get influenced in the end is maximized. Recent solutions to influence maximization rely on the knowledge of the influence probability of every social network user. This is the probability that a user influences another one, and can be obtained by using users’ history of influencing others (called action logs). However, this information is not always available.
We propose a novel Online Influence Maximization (OIM) framework, showing that it is possible to maximize influence in a social network in the absence of exact information about influence probabilities. In our OIM framework, we investigate an Explore-Exploit (EE) strategy, which could run any one of the existing influence maximization algorithms to select the seed nodes using the current influence probability estimation (exploit), or the confidence bound of the estimation (explore). We then start the influence campaign using the seed nodes, and consider users’ immediate feedback to the campaign to further decide which seed nodes to influence next. Influence probabilities are modeled as random variables and their probability distributions are updated as we get feedback. In essence, we perform influence maximization and learning of influence probabilities at the same time. We further develop an incremental algorithm that can significantly reduce the overhead of handling users’ feedback information. We validate the e↵ectiveness and efficiency of our OIM framework on large real-world datasets.published_or_final_version
Computer Science
Master
Master of Philosophy
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Noe, Nyala. "Personality homophily and social-spatial characteristics in online social networks." Thesis, Cardiff University, 2018. http://orca.cf.ac.uk/118510/.
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Do birds of a feather flock together or do opposites attract? The aim of this thesis is to consider this question in the context of online social networks. Humans, unlike birds, can flock together based on a wide variety of characteristics, such as age, gender, or political affiliation. The tendency of people to assort based on a common trait is referred to as homophily. Research into homophilous traits has often overlooked psychological characteristics. In particular, while personality is studied extensively in the context of social media use, it has received little attention in the homophily literature, which is a gap this work endeavours to bridge. Online social networks have become ubiquitous in our daily lives and understanding their dynamics gives valuable insight into this new form of social interaction. This thesis highlights the importance of personality homophily in shaping online social networks, while also considering the inherent geographic constraints. In offline social networks, geographic proximity allows for frequent face-to-face interactions, which are essential for the formation and maintenance of friendships. Online networks often reflect offline networks, meaning that people still tend to cluster with others who are geographically close. Using datasets from Facebook and Foursquare, we explore the relationship between personality homophily and geographic distance in detail by considering the distance between similar and dissimilar people, and how they differ in their co-location patterns. We find that people assort based on their personality in both social and spatial contexts, although not all aspects of our personality are equally homophilous. Openness to experience and Conscientiousness emerged as the personality facets with the strongest homophilic tendencies, while Neuroticism appeared to be less homophilous; Agreeableness and Extraversion fall somewhere in the middle. In other words, birds of a feather do seem to flock together, but this depends on the personality facet considered.
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Musial, Katarzyna. "Recommendation system for online social network." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4105.
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Although there has been much work done in the industry and academia on developing the theory and application of social networks as well as recommender systems, the relation between these research areas is still unclear. An innovative idea, which enables to integrate these areas, and applies recommendation systems to the online social network systems, is proposed in this thesis. Recommendation systems for social networks differ from the typical kinds of recommendation solutions, since they suggest human beings to other ones rather than inanimate goods. Thus, conventional recommendation methods should be enhanced by social features of the networks and their members. This thesis presents the result of the study on the recommendation framework for virtual communities. It also contains an overview of recent approaches to recommendation systems and social networks, as well as description of the online social network systems.
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Ikhalia, Ehinome. "A malware threat avoidance model for online social network users." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16039.
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The main purpose of this thesis is to develop a malware threat avoidance model for users of online social networks (OSNs). To understand the research domain, a comprehensive and systematic literature review was conducted and then the research scope was established. Two design science iterations were carried out to achieve the research aim reported in this thesis. In the first iteration, the research extended the Technology Threat Avoidance Theory (TTAT) to include a unique characteristic of OSN - Mass Interpersonal Persuasion (MIP). The extended model (TTAT-MIP), focused on investigating the factors that needs to be considered in a security awareness system to motivate OSN users to avoid malware threats. Using a quantitative approach, the results of the first iteration suggests perceived severity, perceived threat, safeguard effectiveness, safeguard cost, self-efficacy and mass interpersonal persuasion should be included in a security awareness system to motivate OSN users to avoid malware threats. The second iteration was conducted to further validate TTAT-MIP through a Facebook video animation security awareness system (referred in this thesis as Social Network Criminal (SNC)). SNC is a Web-based application integrated within Facebook to provide security awareness to OSN users. To evaluate TTAT-MIP through SNC, three research techniques were adopted: lab experiments, usability study and semi-structured interviews. The results suggest that participants perceived SNC as a useful tool for malware threat avoidance. In addition, SNC had a significant effect on the malware threat avoidance capabilities of the study participants. Moreover, the thematic analysis of the semi-structured interviews demonstrated that the study participants' found SNC to be highly informative; persuasive; interpersonally persuasive; easy to use; relatable; fun to use; engaging; and easy to understand. These findings were strongly related to the constructs of TTAT-MIP. The research contributes to theory by demonstrating a novel approach to design and deploy security awareness systems in a social context. This was achieved by including users' behavioural characteristic on the online platform where malware threats occur within a security awareness system. Besides, this research shows how practitioners keen on developing systems to improve security behaviours could adopt the TTAT-MIP model for other related contexts.
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Irani, Danesh. "Preventing abuse of online communities." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44895.
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Online communities are growing at a phenomenal rate and with the large number of users these communities contain, attackers are drawn to exploit these users. Denial of information (DoI) attacks and information leakage attacks are two popular attacks that target users on online communities. These information based attacks are linked by their opposing views on low-quality information. On the one hand denial of information attacks which primarily use low-quality information (such as spam and phishing) are a nuisance for information consumers. On the other hand information leakage attacks, which use inadvertently leaked information, are less effective when low-quality information is used, and thus leakage of low-quality information is prefered by private information producers.
In this dissertation, I introduce techniques for preventing abuse against these attacks in online communities using meta-model classification and information unification approaches, respectively. The meta-model classification approach involves classifying the ``connected payload" associated with the information and using the classification result for the determination. This approach allows for detection of DoI attacks in emerging domains where the amount of information may be constrained. My information unification approach allows for modeling and mitigating information leakage attacks. Unifying information across domains followed by a quantificiation of the information leaked, provides one of the first studies on users' susceptibality to information leakage attacks. Further, the modeling introduced allows me to quantify the reduced threat of information leakage attacks after applying information cloaking.
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Makinde, Oghenefejiro Winnie. "Assessing the credibility of online social network messages." Thesis, University of Derby, 2018. http://hdl.handle.net/10545/622367.
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Information gathered socially online is a key feature of the growth and development of modern society. Presently the Internet is a platform for the distribution of data. Millions of people use Online Social Networks daily as a tool to get updated with social, political, educational or other occurrences. In many cases information derived from an Online Social Network is acted upon and often shared with other networks, without further assessments or judgments. Many people do not check to see if the information shared is credible. A user may trust the information generated by a close friend without questioning its credibility, in contrast to a message generated by an unknown user. This work considers the concept of credibility in the wider sense, by proposing whether a user can trust the service provider or even the information itself. Two key components of credibility have been explored; trustworthiness and expertise. Credibility has been researched in the past using Twitter as a validation tool. The research was focused on automatic methods of assessing the credibility of sets of tweets using analysis of microblog postings related to trending topics to determine the credibility of tweets. This research develops a framework that can assist the assessment of the credibility of messages in Online Social Networks. Four types of credibility are explored (experienced, surface, reputed and presumed credibility) resulting in a credibility hierarchy. To determine the credibility of messages generated and distributed in Online Social Networks, a virtual network is created, which attributes nodes with individual views to generate messages in the network at random, recording data from a network and analysing the data based on the behaviour exhibited by agents (an agent-based modelling approach). The factors considered for the experiment design included; peer-to-peer networking, collaboration, opinion formation and network rewiring. The behaviour of agents, frequency in which messages are shared and used, the pathway of the messages and how this affects credibility of messages is also considered. A framework is designed and the resulting data are tested using the design. The resulting data generated validated the framework in part, supporting an approach whereby the concept of tagging the message status assists the understanding and application of the credibility hierarchy. Validation was carried out with Twitter data acquired through twitter’s Application Programming Interface (API). There were similarities in the generation and frequency of the message distributions in the network; these findings were also recorded and analysed using the framework proposed. Some limitations were encountered while acquiring data from Twitter, however, there was sufficient evidence of correlation between the simulated and real social network datasets to indicate the validity of the framework.
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Pérez-Solà, Cristina. "Towards understanding privacy risks in online social networks." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/386415.
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Les xarxes socials en línia (en anglès, Online Social Networks o OSNs) són avui en dia un dels serveis més populars a Internet. En el moment d’escriure aquestes línies, quatre de les deu primeres pàgines del rànquing global Alexa corresponien a xarxes socials i les xarxes més utilitzades tenien centenars de milions d’usuaris actius cada dia.
Les persones fem servir xarxes socials per compartir tot tipus de continguts: des d’atributs personals (com noms, edat o sexe), a ubicacions, fotos o comentaris. D’altra banda, les xarxes socials es caracteritzen per permetre que els usuaris puguin crear relacions de manera explícita (per exemple, relacions d’amistat). A més, les xarxes socials inclouen no només la informació que els usuaris publiquen conscientment sobre si mateixos, sinó també la informació que es genera a partir de la interacció dels usuaris de la plataforma.
Tant el nombre d’usuaris com el volum de dades compartides fan que la privacitat en xarxes socials sigui crítica. Aquesta tesi se centra en l’estudi de la privacitat en xarxes socials en dos contextos diferents: l’adquisició de dades de manera automatitzada (crawling) i l’aprenentatge. En primer lloc, s’estudia la relació entre crawling i privacitat, un tema que fins al moment ha rebut una atenció limitada. Aquest escenari és interessant ja que és assequible fins i tot per a un atacant de baix pressupost. En segon lloc, s’estudia com extreure informació de les relacions que formen els usuaris de xarxes socials. Les tècniques desenvolupades s’estenen després al tractament d’altres problemes que, com les xarxes socials, es poden modelar en forma de grafs.Online Social Networks (OSNs) are now one of the most popular services on the Internet. When these lines were written, there were four OSN sites in the Alexa's top ten global ranking and the most used OSNs were having hundreds of millions of daily active users. People use OSNs to share all kinds of contents: from personal attributes (like names, age, or gender), to location data, photos, or comments. Moreover, OSNs are characterized by allowing its users to explictly form relationships (e.g. friendship). Additionally, OSNs include not only information the users conscientiously post about themselves, but also information that is generated from the interaction of users in the platform. Both the number of users and the volume of data shared make privacy in OSNs critical. This thesis is focused on studying privacy related to OSNs in two different contexts: crawling and learning. First, we study the relation between OSN crawling and privacy, a topic that so far received limited attention. We find this scenario interesting because it is affordable for even a low-budget attacker. Second, we study how to extract information from the relationships OSN users form. We then expand our findings to other graph-modeled problems.
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Alzamzami, Fatimah. "Towards Multimedia-Based Storytelling in Online Social Networks." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32521.
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Human activities can now be captured in real-time using sensor technology. The growth in sensor applications and smart mobile phones that come equipped with built-in sensors has led to the integration of sensors with social networks. These days, people are heavily dependent on online social networks (OSNs); they migrate their real-life activities online through various types of multimedia such as photos, videos, text, etc., which turns OSNs into a soft-sensory resource about users' events. The users use these forms of multimedia to tell their friends about their daily lives. This social network data can be crawled to build personal context-aware stories about individuals. However, the number of social users and the quantity of multimedia that is produced on social media are both growing exponentially, which leads to the challenge of information overload on OSNs. The information needed for stories, such as events and their locations, is not fully available on user's own profile. It is true that part of the information can be retrieved from the user's timeline, but a large number of events and related multimedia information is only available on friends' profiles. In this thesis, we focus on identifying a subset of close friends in order to enrich the content of the story. The amount of time people spend together has been proven to play a key role in determining close ties between people. We propose a DST (Days Spent Together) algorithm to find a user's closest friends based on the days they spent together interacting face-to-face. With the closest friends information, we are able to find additional information to complement what was found on the user's own profile, as well as to personalize the stories to ensure that they are only about the users and their closest friends. Due to the possibility of multimedia (photos in this thesis) overload for events, we propose to use the duration of events measured by DST, to determine the number of representative photos for each event. Our experiments show that the proposed approach could recognize the close friends of users and rank them from the strongest to the weakest. The results also show that with the proposed method we get days-spent-together values that are close to the corresponding true values provided by users.
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Aloufi, Samah. "Trust-aware Link Prediction in Online Social Networks." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23303.
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As people go about their lives, they form a variety of social relationships, such as family, friends, colleagues, and acquaintances, and these relationships differ in their strength, indicating the level of trust among these people. The trend in these relationships is for people to trust those who they have met in real life more than unfamiliar people whom they have only met online. In online social network sites the objective is to make it possible for users to post information and share albums, diaries, videos, and experiences with a list of contacts who are real-world friends and/or like-minded online friends. However, with the growth of online social services, the need for identifying trustworthy people has become a primary focus in order to protect users’ vast amounts of information from being misused by unreliable users. In this thesis, we introduce the Capacity- first algorithm for identifying a local group of trusted people within a network. In order to achieve the outlined goals, the algorithm adapts the Advogato trust metric by incorporating weighted social relationships. The Capacity-first algorithm determines all possible reliable users within the network of a targeted user and prevents malicious users from accessing their personal network. In order to evaluate our algorithm, we conduct experiments to measure its performance against other well-known baseline algorithms. The experimental results show that our algorithm’s performance is better than existing alternatives in finding all possible trustworthy users and blocking unreliable ones from violating users’ privacy.
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Kershaw, Daniel. "Language change and evolution in Online Social Networks." Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/129787/.
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Language is in constant flux, whether through the creation of new terms or the changing meanings of existing words. The process by which language change happens is through complex reinforcing interactions between individuals and the social structures in which they exist. There has been much research into language change and evolution, though this has involved manual processes that are both time consuming and costly. However, with the growth in popularity of osn, for the first time, researchers have access to fine-grained records of language and user interactions that not only contain data on the creation of these language innovations but also reveal the inter-user and inter-community dynamics that influence their adoptions and rejections. Having access to these osn datasets means that language change and evolution can now be assessed and modelled through the application of computational and machine-learning-based methods. Therefore, this thesis looks at how one can detect and predict language change in osn, as well as the factors that language change depends on. The answer to this over-arching question lies in three core components: first, detecting the innovations; second, modelling the individual user adoption process; and third, looking at the collective adoption across a network of individuals. In the first question, we operationalise traditional language acceptance heuristics (used to detect the emergence of new words) into three classes of computation time-series measures computing the variation in frequency, form and/or meaning. The grounded methods are applied to two osn, with results demonstrating the ability to detect language change across both networks. By additionally applying the methods to communities within each network, e.g. geographical regions, on Twitter and Subreddits in Reddit, the results indicate that language variation and change can be dependent on the community memberships. The second question in this thesis focuses on the process of users adopting language innovations in relation to other users with whom they are in contact. By modelling influence between users as a function of past innovation cascades, we compute a global activation threshold at which users adopt new terms dependent on exposure to them from their neighbours. Additionally, by testing the user interaction networks through random shuffles, we show that the time at which a user adopts a term is dependent on the local structure; however, a large part of the influence comes from sources external to the observed osn. The final question looks at how the speakers of a language are embedded in social networks, and how the networks' resulting structures and dynamics influence language usage and adoption patterns. We show that language innovations diffuse across a network in a predictable manner, which can be modelled using structural, grammatical and temporal measures, using a logistic regression model to predict the vitality of the diffusion. With regard to network structure, we show how innovations that manifest across structural holes and weak ties diffuse deeper across the given network. Beyond network influence, our results demonstrate that the grammatical context through which innovations emerge also play an essential role in diffusion dynamics - this indicates that the adoption of new words is enabled by a complex interplay of both network and linguistic factors. The three questions are used to answer the over-arching question, showing that one can, indeed, model language change and forecast user and community adoption of language innovations. Additionally, we also show the ability to apply grounded models and methods and apply them within a scalable computational framework. However, it is a challenging process that is heavily influenced by the underlying processes that are not recorded within the data from the osns.
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Botha, Leendert W. "Modeling online social networks using Quasi-clique communities." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/17859.
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Thesis (MSc)--Stellenbosch University, 2011ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously.
AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.
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Othman, Salem. "Autonomous Priority Based Routing for Online Social Networks." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1526481500145998.
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Zhen, Yufeng. "A NOVEL SPAM CAMPAIGN IN ONLINE SOCIAL NETWORKS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3290.
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The increasing popularity of the Online Social Networks (OSNs)\nomenclature{$OSNs$}{Online Social Networks} has made the OSNs major targets of spammers. They aim to illegally gather private information from users and spread spam to them. In this paper, we propose a new spam campaign that includes following key steps: creating fake accounts, picking legitimate accounts, forming friendships, earning trust, and spreading spam. The unique part in our spam campaign is the process of earning trust. By using social bots, we significantly lower the cost of earning trust and make it feasible in the real world. By spreading spam at a relatively low speed, we make the life span of our fake accounts much longer than in traditional spam campaigns. This means the trust we have earned can be used multiple times instead of only one time in traditional spam campaigns. We evaluate our spam campaign by comparing it with the traditional campaign, and the comparison shows that our spam campaign is less detectable and more efficient than the traditional campaign.
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Wei, Wei. "Improving Security and Privacy in Online Social Networks." W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623628.
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Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users' private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs.
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Coletto, Mauro. "Analysis of Polarized Communities in Online Social Networks." Thesis, IMT Alti Studi Lucca, 2017. http://e-theses.imtlucca.it/204/1/Coletto_phdthesis.pdf.
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Increasingly, people around the globe use Social Media (SM)
- e.g. Facebook, Twitter, Tumblr, Flickr, Youtube - to publish
multimedia content (posting), to share it (retweeting, reblogging
or resharing), to reinforce it or not (liking, disliking,
favoriting) and to discuss (through messages and comments)
in order to be in contact with other users and to get informed
about topics of interest. The world population is ≈ 7:4 billion
people, among them ≈ 2:3 billion (31%) are active social
media users (GlobalWeb Index data, Jan 2016). In fact, these
virtual contexts answer the human need of aggregation that
nowadays is translated into digital bonds among peers all
over the world, in addition to the traditional face-to-face
relationships. Online Social Networks (OSNs), then, provide
a space for user aggregation in groups, expressing opinions,
accessing information, contributing to public debates, and
participating in the formation of belief systems.
In this context, communities are built around different topics
of interaction and polarized sub-groups often emerge by clustering
different opinions and points of view. Such polarized
sub-groups can be tracked and monitored over time in an automatic
way and the analysis of their interactions is interesting
to shed light on the human social behavior. Even though many
studies have been devoted to understand different aspects of
the social network structure and its function, such as, community
structure (For10), information spreading (BRMA12),
information seeking (KLPM10), link prediction (LNK07), etc.,
much less work is available on analyzing online discussions,
user opinion and public debates.
In this doctoral dissertation we analyze the concept of polarization by looking at interactions among users in different Online
Social Networks. Polarization is a social process whereby a
social group is divided into sub-communities discussing different
topics and having different opinions, goals and viewpoints,
often conflicting and contrasting (Sun02; Ise86). We are interested
in studying how and to what extend it is possible to extract
information about polarized communities by automatically
processing the data about interactions created in Online Social
Networks. We present the state of the art and we propose a
novel detecting method which allows to identify polarized
groups, track them and monitor the topic evolution in the
discussion among users of an OSN over time by classifing the
keywords used in the messages exchanged. We show that
it improves the state of the art and we describe case studies
conducted particularly on Twitter (CLOP16; CGGL17).
The benefits in understanding user opinions are detailed in
the first chapters. Moreover, we use the proposed methodology
and alternatives in different application contexts: misinformation
(BCD+14a; BCD+14b; BCD+15), politics (CLOP16;
CLOP15; CLO+15), social behaviors (CALS16a; CALS16b),
and migrations (CLM+16).
A further application of opinion mining is the task of predicting
user behavior. We discuss the limitations and the
challenges related to this research area by looking at the context
of political elections and by digging into a case study of
electoral prediction. We believe that the analysis of polarized
communities is OSNs can be used to predict collective social
behavior, but major improvements in the field can be achieved
by integrating several sources of information, such as traditional
surveys, multiple Online Social Networks, demographic
data, historical information, events, cyber-physical data.
Therefore, polarization is integrated in a framework of analysis
with other dimensions (time, location) to explore social
phenomena from a social media perspective. In particular, we look at the possibility to understand European perception of
the political refugees’ crises by mining OSN data.
The concept of polarization is related to that of controversy. Controversy
describes the interaction among two or more opponent
polarized communities that discuss together, often with heated
tones. For some highly controversial topics (e.g., politics,
religion, ethics) even though users prefer to get informed
though polarized content originated in the communities they
belong to, they like to share their affiliations, believes, ideals,
convictions with external users in order to persuade them in
joining their belief system or supporting, criticizing an event,
a group, a party or a specific person. Highly polarization does
not always imply controversy and vice versa. We describe the
recent literature about controversy detection and we propose a
machine learning approach which takes into account features
related to the social network and to conversational interaction
patterns. The model is able to identify controversy in a
conversation without any feature related to the content of the
interaction. The features are deeply analyzed and the accuracy
of the model is discussed.
We finally explore two opposite situations. The first is the
formation of echo chambers, where a user gets informed and
gives opinions in a self-contained group, whose members
share a similar point of view. By analyzing communities
in Facebook which consume news from scientific pages and
from pages focused on conspiracy theories we confirm the
hypothesis of cognitive closure of the users, weakening the
idea of Social Media as a space for democratic collective intelligence.
The second is the presence of deviant communities.
Those are communities that emerge around what are usually
referred to as deviant behaviors (CM15), conducts that are commonly
considered inappropriate because they violate society’s
norms or moral standards. An example of deviant behavior
is the pornography consumption, that is the focus of our examination looking at content dissemination in Online Social
Networks. Deviant communities are commonly considered
segregated but we show that instead their content might
spread far away in the Online Social Network. We analyze
both situations with real case studies using Facebook, Flickr,
and Tumblr data.
Our work is an initial study of opinion polarization on Online
Social Networks with some in-depth analyses of specific topical
user communities. It brings novel contributions in: i)
characterizing communities through the perspective of user
polarization; ii) proposing a novel method to classify polarized
users and topic evolution over time; iii) understanding user
behavior from a social media perspective; iv) integrating polarization
with other variables (time, space) with the purpose of
analyzing a social phenomenon; v) defining controversy and
how to detect it regardless of the content; vi) describing how
people aggregate and share information in various contexts.
Different topical communities and several OSNs are described
in the dissertation, providing a general overview of the investigation
field and proposing contributions to the discussion
and solutions. Our research questions are part of a broader
research area which is called Computational Social Science.
This new discipline - which is the frame of our thesis - is a
new approach to social studies by mean of novel large-scale
computational tools, merging Social Science with Computer
Science and Machine Learning.
34
Vandersluis, Kelly S. "Creating social action through Facebook." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3008.
Full textAbstract:
Thesis (M.A.)--George Mason University, 2008.Vita: p. 61. Thesis director: Byron Hawk. Submitted in partial fulfillment of the requirements for the degree of Master of Arts in English. Title from PDF t.p. (viewed July 2, 2008). Includes bibliographical references (p. 54-60). Also issued in print.
35
Shaikh, Sajid S. "Computations in social network." [Kent, Ohio] : Kent State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1185560088.
Full text
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Kong, Chenguang, and 孔臣光. "Collaborative streaming in mobile social networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47849897.
Full textAbstract:
Mobile social applications have emerged in recent years. They explore social
connections among mobile users in a variety of novel scenarios, including
friend finding, message routing, and content sharing. However, efficiently supporting
resource-demanding delay-sensitive streaming applications on the mobile
platform remains a significant challenge. Research on such topics will naturally
widen the usage of mobile social applications. The solutions to the challenges
will provide suggestion on many related work. It is interesting and valuable to
explore the system performance and users’ experience in such scenarios.
Furthermore, users’ concern about social network is also significant to develop
a mobile social network application. It is important to detect users’ strategies
to communicate with others. That influences the network topologies and
provides biased connections. The strategy consists of various of aspects, most of
which are the user preference and user social attributes.
Focusing on this meaningful research field, we study collaborative VoD-type
streaming of short videos among small groups of mobile users, so as to effectively
exploit their social relationships. Such an application can be practically set in
a number of usage scenarios, including streaming of introductory video clips of
exhibition items to visitors’ mobile devices, such as in a museum. We analyze
users’ behavior strategies based on their social preference and social attributes.
We design SMS, an architecture that engineers such Streaming over Mobile Social
networks. SMS constructs a collaborative streaming overlay by carefully
inspecting social connections among users and infrastructure characteristics of
Bluetooth technologies. To improve the performance, we analyze the scatternet
structure of Bluetooth technology and propose appropriate scatternet structure
in our system. We evaluate our design based on prototype implementation on
the Android platform, as well as on a large emulation testbed. The results obtained
indicate that we are able to achieve a well-performed streaming system in
a mobile social network.published_or_final_version
Computer Science
Master
Master of Philosophy
37
Mega, Giuliano. "On Social Overlays and Their Application to Decentralized Online Social Networks." Doctoral thesis, Università degli studi di Trento, 2013. https://hdl.handle.net/11572/367684.
Full textAbstract:
Over the last decade, Online Social Networks (OSNs) have attracted hundreds of millions of users worldwide, establishing themselves as one of most successful communication tools to date. Yet, the business model adopted by current centralized approaches makes them inherently prone to privacy issues and hostile to openness, as service providers rely on the commercial exploitation of their userbases' private data as their means of survival. We believe, as others do, that decentralization could represent a solution to this fundamental problem.
In this work, we propose a novel P2P approach to decentralized OSNs in which peers are organized as a social overlay (SO): an overlay network that effectively mirrors an underlying social network by constraining communication to pairs of peers whose owners are friends. SOs are special in two ways. First, by embodying friendship in their links, SOs can help us either solve or mitigate fundamental trust-related issues that arise in P2P systems. Second, SOs exhibit an inherent compatibility towards OSNs, a result of the former being shaped after human communication, and the latter being human communication tools. These give raise to an inherent potential for synergy, which we propose to reap by means of a simple approach that provides a key functionality of modern OSNs: profile-based communication.
In our approach, nodes cache the profile pages of their friends locally, and updates get proactively disseminated only by trusted nodes, over a user's ego network: the subgraphs of social networks composed by a user, her friends, and the connections among them. The contributions of this thesis then emerge as we tackle this seemingly simple problem of update dissemination over ego networks and, along the way, uncover issues that lead us to progressively deeper problems and understanding, and, ultimately, to effective solutions.
In the first part of this thesis, we explore the use of push gossip protocols as the means to achieve efficient dissemination of updates over ego networks. We show that mainstream gossip protocols cannot be applied in this context, due to the largely non-uniform structure of ego networks. By taking these structural properties into account, we develop a novel gossip protocol that is able to adapt to, and leverage this non-uniformity, providing efficient and timely dissemination of updates.
The study of these dissemination protocols under peer churn leads us to uncover the second problem we tackle in this thesis -- namely, the network-induced communication delays that emerge from the interaction of the social graph with the underlying peer dynamics. By means of a small-scale simulation study, we find that not only these delays can be rather extreme, but that they matter more than the underlying dissemination protocol on the long run. While this realization is in itself a contribution, we also find that evaluating the problem in more depth, as well as identifying opportunities for improvement, cannot be done by simulations alone. This is due to three factors: i) the size of the target networks under study, ii) the large parameter space inherent to availability modelling, and iii) the large number of repetitions required for obtaining good quality estimators. Put together, these translate into prohibitive costs.
We therefore propose a novel hybrid analytical/simulation framework that enables the estimation of dissemination delays at a practical cost. In the third part of this thesis, we show how to further develop this framework by deriving analytical, closed-form expressions that describe delays as a function of a graph and availability parameters, when the underlying availability model is based on a certain class of simpler distributions.
Finally, by putting together the lessons we learnt along the way -- our dissemination protocol and the knowledge we acquired about the workings of communication delays -- we devise the final contribution of this thesis: a hybrid, cloud-assisted P2P architecture that enables efficient dissemination in social overlays under churn. This solution, as we show, provides performance that rivals that of centralized solutions, while incurring modest economical costs.
38
Mega, Giuliano. "On Social Overlays and Their Application to Decentralized Online Social Networks." Doctoral thesis, University of Trento, 2013. http://eprints-phd.biblio.unitn.it/999/1/thesis.pdf.
Full textAbstract:
Over the last decade, Online Social Networks (OSNs) have attracted hundreds of millions of users worldwide, establishing themselves as one of most successful communication tools to date. Yet, the business model adopted by current centralized approaches makes them inherently prone to privacy issues and hostile to openness, as service providers rely on the commercial exploitation of their userbases' private data as their means of survival. We believe, as others do, that decentralization could represent a solution to this fundamental problem.
In this work, we propose a novel P2P approach to decentralized OSNs in which peers are organized as a social overlay (SO): an overlay network that effectively mirrors an underlying social network by constraining communication to pairs of peers whose owners are friends. SOs are special in two ways. First, by embodying friendship in their links, SOs can help us either solve or mitigate fundamental trust-related issues that arise in P2P systems. Second, SOs exhibit an inherent compatibility towards OSNs, a result of the former being shaped after human communication, and the latter being human communication tools. These give raise to an inherent potential for synergy, which we propose to reap by means of a simple approach that provides a key functionality of modern OSNs: profile-based communication.
In our approach, nodes cache the profile pages of their friends locally, and updates get proactively disseminated only by trusted nodes, over a user's ego network: the subgraphs of social networks composed by a user, her friends, and the connections among them. The contributions of this thesis then emerge as we tackle this seemingly simple problem of update dissemination over ego networks and, along the way, uncover issues that lead us to progressively deeper problems and understanding, and, ultimately, to effective solutions.
In the first part of this thesis, we explore the use of push gossip protocols as the means to achieve efficient dissemination of updates over ego networks. We show that mainstream gossip protocols cannot be applied in this context, due to the largely non-uniform structure of ego networks. By taking these structural properties into account, we develop a novel gossip protocol that is able to adapt to, and leverage this non-uniformity, providing efficient and timely dissemination of updates.
The study of these dissemination protocols under peer churn leads us to uncover the second problem we tackle in this thesis -- namely, the network-induced communication delays that emerge from the interaction of the social graph with the underlying peer dynamics. By means of a small-scale simulation study, we find that not only these delays can be rather extreme, but that they matter more than the underlying dissemination protocol on the long run. While this realization is in itself a contribution, we also find that evaluating the problem in more depth, as well as identifying opportunities for improvement, cannot be done by simulations alone. This is due to three factors: i) the size of the target networks under study, ii) the large parameter space inherent to availability modelling, and iii) the large number of repetitions required for obtaining good quality estimators. Put together, these translate into prohibitive costs.
We therefore propose a novel hybrid analytical/simulation framework that enables the estimation of dissemination delays at a practical cost. In the third part of this thesis, we show how to further develop this framework by deriving analytical, closed-form expressions that describe delays as a function of a graph and availability parameters, when the underlying availability model is based on a certain class of simpler distributions.
Finally, by putting together the lessons we learnt along the way -- our dissemination protocol and the knowledge we acquired about the workings of communication delays -- we devise the final contribution of this thesis: a hybrid, cloud-assisted P2P architecture that enables efficient dissemination in social overlays under churn. This solution, as we show, provides performance that rivals that of centralized solutions, while incurring modest economical costs.
39
Smith, Matthew Scott. "Social Capital in Online Communities." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2730.
Full textAbstract:
Social capital is the value of the relationships we create and maintain within our social networks to gain access to and mobilize needed resources (e.g., jobs, moral support). Quantifying, and subsequently leveraging, social capital are challenging problems in the social sciences. Most work so far has focused on analyses from static surveys of limited numbers of participants. The explosion of online social media means that it is now possible to collect rich data about people's connections and interactions, in a completely ubiquitous, non-intrusive manner. Such dynamic social data opens the door to the more accurate measuring and tracking of social capital. Similarly, online data is replete with additional personal data, such as topics discussed in blogs or hobbies listed in personal profiles, that is difficult to obtain through standard surveys. Such information can be used to discover similarities, or implicit affinities, among individuals, which in turn leads to finer measures of social capital, including the often useful distinction between bonding and bridging social capital. In this work, we exploit these opportunities and propose a computational framework for quantifying and leveraging social capital in online communities. In addition to being dynamic and formalizing the notion of implicit affinities, our framework significantly extends current social network analysis research by modeling access and mobilization of resources, the essence of social capital. The main contributions of our framework include 1) hybrid networks that provide a way for potential and realized social capital to be distinguished; 2) the decoupling of bonding and bridging social capital, a formulation previously overlooked which coincides with empirical evidence; 3) the unification of multiple views on social capital, in particular, the seamless integration of resources. We demonstrate the broad applicability of our framework through a number of representative, real-world case studies to test relevant social science hypotheses. Assuming that the extraction of implicit affinities may be useful for community building, we built a large social network of blogs from an active, tech-oriented segment of the Blogosphere, using cross-references among blogs. We then used topic modeling techniques to extract an implicit affinity network based on the content of the blogs, and showed that potential sub-communities could be formed through increased bonding. A widespread assumption in sociology is that bonding is more likely than bridging in social networks. In other words, people are more likely to seek out others who are like them than attempt to link to those they share little or nothing with. We wanted to test that hypothesis, particularly in the context of online communities. Using Twitter, we created an experiment where hand-crafted accounts would tweet at regular intervals and use varied following strategies, including following only those with maximum affinity, following only those with no affinity, following random users, etc. Using the number of follow-backs as a surrogate for social capital, we showed that the assumed physical social behavior is also prevalent online, p < 0.01. There is much interest in computational social science to compare physical and cyber behaviors, test existing hypotheses on a large scale and design novel experiments. The advent of social media is also impacting public health, with growing evidence that some global health issues (e.g., H1N1 outbreak) may be discovered and tracked more efficiently by monitoring the content of social exchanges (e.g., blogs, tweets). In collaboration with colleagues from Health Sciences, we wanted to test whether broadly applicable health topics were discussed on Twitter, and to design and guide the process of discovering such themes. We gathered a large number of tweets over several regions of the United States over a one-month period, and analyzed their content using topic modeling techniques. We found that while clearly not a mainstream topic, health concerns were non-negligible on Twitter. By further focusing on tobacco, we discovered several subtopics related to tobacco (e.g., tobacco use promotion, addiction recovery), which indicate that analysis of the Twitter social network may help researchers better understand how Twitter promotes both positive and negative health behaviors. Finally, in collaboration with colleagues from Linguistics, we wanted to quantify the effect of social capital on second language acquisition in study abroad. Using questionnaire data collected from about 200 study abroad participants, we found that students participating in bridging relationships had significantly higher levels of language improvement than their counterparts, F(1,201) = 12.53, p < .0001.
40
Greschbach, Benjamin. "Privacy Issues in Decentralized Online Social Networks and other Decentralized Systems." Doctoral thesis, KTH, Teoretisk datalogi, TCS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196118.
Full textAbstract:
Popular Online Social Networks (OSNs), such as Facebook or Twitter, are logically centralized systems. The massive information aggregation of sensitive personal data at the central providers of these services is an inherent threat to the privacy of the users. Leakages of these data collections happen regularly – both intentionally, for example by selling of user data to third parties and unintentionally, for example when outsiders successfully attack a provider. Motivated by this insight, the concept of Decentralized Online Social Networks (DOSNs) has emerged. In these proposed systems, no single, central provider keeps a data collection of all users. Instead, the data is spread out across multiple servers or is distributed completely among user devices that form a peer-to-peer (P2P) network. Encryption is used to enforce access rights of shared content and communication partners ideally connect directly to each other. DOSNs solve one of the biggest privacy concerns of centralized OSNs in a quite forthright way – by getting rid of the central provider. Furthermore, these decentralized systems can be designed to be more immune to censorship than centralized services. But when decentralizing OSNs, two main challenges have to be met: to provide user privacy under a significantly different threat model, and to implement equal usability and functionality without centralized components. In this work we analyze the general privacy-problems in DOSNs, especially those arising from the more exposed metadata in these systems. Furthermore, we suggest three privacy-preserving implementations of standard OSN features, i.e. user authentication via password-login, user search via a knowledge threshold and an event invitation system with fine-grained privacy-settings. These implementations do not rely on a trusted, central provider and are therefore applicable in a DOSN scenario but can be applied in other P2P or low-trust environments as well. Finally, we analyze a concrete attack on a specific decentralized system, the Tor anonymization network, and suggest improvements for mitigating the identified threats.Populära sociala nätverkstjänster som Facebook och Instagram bygger på en logiskt centraliserad systemarkitektur. Tjänsteleverantörerna har därför tillgång till omfattande ansamlingar av känsliga personuppgifter,vilket innebär en oundviklig risk för integritetskränkningar. Med jämna mellanrum läcks dessa informationsansamlingar till tredje part – antingen när tjänsteleverantören själv säljer eller ger dem tillexterna aktörer, eller när obehöriga får åtkomst till tjänsteleverantörens datasystem. Decentraliserade sociala nätverkstjänster (eng. Decentralized Online Social Networks, DOSNs) är en lovande utveckling för att minska denna risk och för att skydda användarnas personliga information såväl från tjänsteleverantören som från tredje part. Ett vanligt sätt att implementera ett DOSN är genom en icke-hierarkisk nätverksarkitektur (eng. peer-to-peer network) för att undvika att känsliga personuppgifter samlas på ett ställe som är under tjänsteleverantörens kontroll. Kryptering används för att skydda kommunikationen och för att realisera åtkomstkontrollen av information som ska delas med andra användare. Att inte längre ha en tjänsteleverantör som har tillgång till all data innebär att den största riskfaktorn for integritetskränkningar tas bort. Men genom att ersätta den centrala tjänsteleverantören med ett decentraliserat system tar vi även bort ett visst integritetsskydd. Integritetsskyddet var en konsekvens av att förmedlingen av all användarkommunikation skedde genom tjänsteleverantörens servrar. När ansvaret för lagring av innehållet, hantering av behörigheterna, åtkomst och andra administrativa uppgifter övergår till användarna själva, blir det en utmaning att skydda metadata för objekt och informationsflöden, även om innehållet är krypterat. I ett centraliserat system är dessa metadata faktiskt skyddade av tjänsteleverantören – avsiktligt eller som en sidoeffekt. För att implementera de olika funktioner som ska finnas i ett integritetsskyddande DOSN, är det nödvändigt både att lösa dessa generella utmaningar och att hantera frånvaron av en betrodd tjänsteleverantör som har full tillgång till all data. Användarautentiseringen borde till exempel ha samma användbarhet som i centraliserade system. Det vill säga att det är lätt att ändra lösenordet, upphäva rättigheterna för en stulen klientenhet eller återställa ett glömt lösenord med hjälp av e-post eller säkerhetsfrågor – allt utan att förlita sig på en betrodd tredje part. Ett annat exempel är funktionen att kunna söka efter andra användare. Utmaningen där är att skydda användarinformationen samtidigt som det måste vara möjligt att hitta användare baserad på just denna informationen. En implementation av en sådan funktion i ett DOSN måste klara sig utan en betrodd tjänsteleverantör som med tillgång till alla användardata kan upprätthålla ett globalt sökindex. I den här avhandlingen analyserar vi de generella risker för integritetskränkningar som finns i DOSN, särskilt de som orsakas av metadata. Därutöver föreslår vi tre integritetsskyddande implementationer av vanliga funktioner i en social nätverkstjänst: lösenordsbaserad användarautentisering, en användarsökfunktion med en kunskapströskel och en inbjudningsfunktion för evenemang med detaljerade sekretessinställningar. Alla tre implementationerna är lämpliga för DOSN-scenarier eftersom de klarar sig helt utan en betrodd, central tjänsteleverantör, och kan därför även användas i andra sammanhang såsom icke-hierarkiska nätverk eller andra system som måste klara sig utan en betrodd tredje part. Slutligen analyserar vi en attack på ett specifikt decentraliserat system, anonymitetstjänsten Tor, och diskuterar hur systemet kan skyddas mot de analyserade sårbarheterna.
QC 20161115
41
Libardi, Paula Luciene Oliveira 1980. "Detecção computacional de falecidos em redes sociais online." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267725.
Full textAbstract:
Orientadores: André Franceschi de Angelis, Regina Lúcia de Oliveira MoraesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia
Made available in DSpace on 2018-08-27T04:53:50Z (GMT). No. of bitstreams: 1 Libardi_PaulaLucieneOliveira_M.pdf: 1610224 bytes, checksum: a08b75cd1a30c421927617ee8b6ac8d4 (MD5) Previous issue date: 2015
Resumo: A identificação de usuários falecidos em Redes Sociais Online é um desafio em aberto e, dado o tamanho das principais redes, abordagens que envolvam intervenção manual são impraticáveis. Usuários inativos por longo tempo inviabilizam soluções simples tais como a expiração de um prazo desde o último acesso, o que torna difícil a diferenciação entre inativos e falecidos. Esta pesquisa iniciou-se com o pressuposto de que o problema poderia ser parcialmente resolvido com métodos automáticos e a hipótese era de que dois métodos aqui propostos, um baseado na análise de frequência de mensagens trocadas entre usuários e outro fundamentado na combinação de informações da topologia da rede junto a inspeções de mensagens, poderiam identificar satisfatoriamente parte dos usuários falecidos. Para testar esta hipótese, recorreu-se à simulação computacional, usando topologias livre de escala e aleatória. O programa que simula as redes foi construído de forma a aplicar e testar os métodos de identificação de falecidos, seguindo padrões de projeto que permitem facilmente a troca ou o encadeamento dos algoritmos a validar. Dessa característica, originou-se um terceiro método, que é a combinação das saídas de algoritmos detectores aplicados anteriormente à rede. Os resultados da pesquisa validaram a hipótese, sendo que os dois métodos propostos inicialmente tiveram, cada qual, índices de acerto superiores a 70% na maioria dos casos simulados, independentemente da topologia da rede. Em ambos os métodos, no entanto, é necessária uma calibração de dois parâmetros operacionais, o que exige algum conhecimento da rede examinada e influencia na taxa de detecção. O último método mostrou-se bastante eficiente, com detecção correta superior a 94%, e capaz de absorver flutuações na taxa de detecção dos demais métodos advindas de suas respectivas parametrizações. Portanto, os objetivos da pesquisa foram plenamente atingidos, com a validação da hipótese inicial, a proposta de três métodos para a solução do problema e a geração de um produto tecnológico, o Demortuos, que é o software de simulação da rede e teste dos métodos, atualmente em processo de registro no Instituto Nacional da Propriedade Industrial (INPI). Adicionalmente, foram abertas possibilidades para o desenvolvimento de métodos automáticos para busca de outras classes de usuários
Abstract: Identifying deceased users in Online Social Networks is an open challenge and, given the size of the main networks, approaches involving manual intervention are impractical. Inactive users for a long time prevent simple solutions such as the expiration of a period since the last entry, making it difficult to differentiate between inactive and deceased users. This research began with the assumption that the problem could be partially solved with automated methods and the hypothesis was that two methods proposed here, one based on frequency analysis of messages exchanged between users and the other based on the combination of topology information network with the messages of inspections, could satisfactorily identify the part of deceased users. To test this hypothesis, we used the computer simulation, using free topologies of scale and random, the latter for comparison purposes. The program that simulates the network was constructed to implement and test the deceased identification methods, following design patterns that easily allow the exchange or the chain of algorithms to validate. This characteristic gave up a third method, which is combining the outputs of detectors algorithms previously applied to the network. The survey results validated the hypothesis, and the two proposed methods initially had, each, hit rates of over 70% in most cases simulated, regardless of the network topology. In both methods, however, two operating parameters calibration is necessary, which requires some knowledge of the network and examined influences the detection rate. The last method proved to be very efficient with proper detection above 94%, and able to absorb fluctuations in the detection rate of other methods resulting from their respective parameterization. Therefore, the research objectives were fully achieved, with the validation of the initial hypothesis, the proposed three methods for the solution of the problem and the generation of a technological product, Demortuos, which is the network simulation software and testing methods currently in the registration process at the National Institute of Industrial Property (INPI). Moreover, possibilities are opened for the development of automated methods to search for other classes of users
Mestrado
Tecnologia e Inovação
Mestra em Tecnologia
42
Niu, Guolin, and 牛国林. "Temporal modeling of information diffusion in online social networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206478.
Full textAbstract:
The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization.
Firstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of “sharing video” behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task.
Secondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well.
Finally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Greschbach, Benjamin. "Privacy Analysis and Protocols for Decentralized Online Social Networks." Licentiate thesis, KTH, Teoretisk datalogi, TCS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165377.
Full textAbstract:
Decentralized Online Social Networks (DOSNs) are evolving as a promising approach to mitigate design-inherent privacy flaws of logically centralized services such as Facebook, Google+ or Twitter. Common approaches to implement a DOSN build upon a peer-to-peer (P2P) architecture in order to avoid the central aggregation of sensitive user data at one provider-controlled location. While the absence of a single point of data aggregation strikes the most powerful attacker from the list of adversaries, the decentralization also removes some privacy protection afforded by the provider's intermediation of all communication in a centralized Online Social Network (OSN). As content storage, access right management, retrieval and other administrative tasks of the service become the obligation of the users, it is non-trivial to hide the metadata of objects and information flows, even when the content itself is encrypted. Such metadata is, deliberately or as a side effect, hidden by the provider in a centralized system. Implementing the different features of a privacy-presvering DOSN does not only face these general challenges but must also cope with the absence of a trusted agent with full access to all data. For example user authentication should provide the same usabilty known from common centralized OSN services, such as ease of changing a password, revoking the access of a stolen device or resetting a forgotten password via e-mail or security questions. All this without relying on a trusted third party such as an identity provider. Another example is user search, where the challenge is to protect user data while making user findable at the same time. An implementation of such a feature in a DOSN has to work without assuming a trusted provider having access to all user profiles maintaining a global search index. In this work we analyze the general privacy-problems in a DOSN, especially those arising from metadata. Furthermore, we suggest two privacy-preserving implementations of standard OSN features, i.e., user authentication via password-login and user search via a knowledge threshold. Both implementations do not rely on a trusted, central provider and are therefore applicable in a DOSN cenario but can be applied in other P2P or low-trust environments as well.I dagens populära sociala nätverkstjänster, såsom Facebook, Google+ och Twitter, finns en risk för integritetskränkningar. Risken är en oundviklig konsekvens av den logiskt centraliserade struktur som dessa tjänster bygger på. Decentraliserade sociala nätverkstjänster (eng. Decentralized Online Social Networks, DOSNs) är en lovande utveckling för att minska risken och skydda användarnas personliga information från tjänsteleverantören och dem som leverantören samarbetar med. Ett vanligt sätt att implementera ett DOSN är genom en icke-hierarkisk nätverksarkitektur (eng. peer-to-peer network) för att undvika att känsliga personuppgifter ansamlas på ett ställe under tjäns televerantörens kontroll. Att inte längre ha en tjänsteleverantör som har tillgång till alla data tar bort den största risken för integritetskränkningar. Men genom att ersätta den centrala tjänsteleverantören med ett decentraliserat system tar vi även bort visst integritetsskydd. Integritetsskyddet var en konsekvens av att förmedlingen av all användarkommunikation skedde genom tjänsteleverantörens mellanservrar. När ansvaret för lagring av innehållet, hantering av behörigheterna, åtkomst och andra administrativa uppgifter övergår till användarna själva, då blir det en utmaning att skydda metadata för objekten och informationsflöden, även om innehållet är krypterat. I ett centraliserat system är dessa metadata faktiskt skyddade av tjänsteleverantören - avsiktligt eller som en sidoeffekt. För att implementera de olika funktioner som ska finnas i ett integritetsskyddande DOSN, är det nödvändigt att både lösa dessa generella utmaningar och att hantera frånvaron av ett betrodd tredjepart som har full tillgång till all data. Autentiseringen av användarna, till exempel, borde ha samma användbarhet som finns i centraliserade system. Det vill säga att det är lätt att ändra lösenordet, dra tillbaka rättigheterna för en stulen klientenhet, eller återställa ett glömt lösenord med hjälp av e-post eller säkerhetsfrågor - allt utan att förlita sig på en betrodd tredjepart. Ett annat exempel är funktionen att kunna söka efter andra användare. Utmaningen där är att skydda informationen om användarna samtidigt som det måste vara möjligt att hitta användare baserad på samma information. En implementation av denna funktion i ett DOSN måste klara sig utan en betrodd tjänsteleverantör som med tillgång till alla användares data kan upprätthålla ett globalt sökindex. I den här avhandlingen analyserar vi de generella risker för integritetskränkningar i DOSN, särskilt de som orsakas av metadata. Dessutom föreslår vi två integritetskyddande implementationer av vanliga funktioner i en socialt nätverkstjänst: lösenordbaserad användarautentisering och en användarsökfunktionen med en kunskaptröskel. Båda implementationerna är lämpliga för DOSN-scenarier eftersom de klarar sig helt utan en betrodd, central tjänstleverantör, och kan därför också användas i andra sammanhang: såsom icke-hierarkiska nätverk eller andra system som måste klara sig utan en betrodd tredjepart.
QC 20150428
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Kayes, Md Imrul. "Content Abuse and Privacy Concerns in Online Social Networks." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5967.
Full textAbstract:
Online Social Networks (OSNs) have seen an exponential growth over the last decade, with Facebook having more than 1.49 billion monthly active users and Twitter having 135,000 new users signing up every day as of 2015. Users are sharing 70 million photos per day on the Instagram photo-sharing network. Yahoo Answers question-answering community has more than 1 billion posted answers. The meteoric rise in popularity has made OSNs important social platforms for computer-mediated communications and embedded themselves into society’s daily life, with direct consequences to the offline world and activities. OSNs are built on a foundation of trust, where users connect to other users with common interests or overlapping personal trajectories. They leverage real-world social relationships and/or common preferences, and enable users to communicate online by providing them with a variety of interaction mechanisms.
This dissertation studies abuse and privacy in online social networks. More specifically, we look at two issues: (1) the content abusers in the community question answering (CQA) social network and, (2) the privacy risks that comes from the default permissive privacy settings of the OSNs. Abusive users have negative consequences for the community and its users, as they decrease the community’s cohesion, performance, and participation. We investigate the reporting of 10 million editorially curated abuse reports from 1.5 million users in Yahoo Answers, one of the oldest, largest, and most popular CQA platforms. We characterize the contribution and position of the content abusers in Yahoo Answers social networks. Based on our empirical observations, we build machine learning models to predict such users.
Users not only face the risk of exposing themselves to abusive users or content, but also face leakage risks of their personal information due to weak and permissive default privacy policies. We study the relationship between users’ privacy concerns and their engagement in Yahoo Answers social networks. We find privacy-concerned users have higher qualitative and quantitative contributions, show higher retention, report more abuses, have higher perception on answer quality and have larger social circles. Next, we look at users’ privacy concerns, abusive behavior, and engagement through the lenses of national cultures and discover cross-cultural variations in CQA social networks.
However, our study in Yahoo Answers reveals that the majority of users (about 87%) do not change the default privacy policies. Moreover, we find a similar story in a different type of social network (blogging): 92% bloggers’ do not change their default privacy settings. These results on default privacy are consistent with general-purpose social networks (such as Facebook) and warn about the importance of user-protecting default privacy settings.
We model and implement default privacy as contextual integrity in OSNs. We present a privacy framework, Aegis, and provide a reference implementation. Aegis models expected privacy as contextual integrity using semantic web tools and focuses on defining default privacy policies. Finally, this dissertation presents a comprehensive overview of the privacy and security attacks in the online social networks projecting them in two directions: attacks that exploit users’ personal information and declared social relationships for unintended purposes; and attacks that are aimed at the OSN service provider itself, by threatening its core business.
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Kamal, Noreen. "Designing online social networks to motivate health behaviour change." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45242.
Full textAbstract:
Eating nutritious foods and being more physically active prevents significant illnesses such as cardiac disease, stroke, and diabetes. However, leading a healthy lifestyle remains elusive and obesity continues to increase in North America. We investigate how online social networks (OSN) can change health behaviour by blending theories from health behaviour and participation in OSNs, which allow us to design and evaluate an OSN through a user-centred design (UCD) process.
We begin this research by reviewing existing theoretical models to obtain the determining factors for participation in OSNs and changing personal health behaviour. Through this review, we develop a conceptual framework, Appeal Belonging Commitment (ABC) Framework, which provides individual determinants (Appeal), social determinants (Belonging), and temporal consideration (Commitment) for participation in OSNs for health behaviour change.
The ABC Framework is used in a UCD process to develop an OSN called VivoSpace. The framework is then utilized to evaluate each design to determine if VivoSpace is able to change the determinants for health behaviour change. The UCD process begins with an initial user inquiry using questionnaires to validate the determinants from the framework (n=104). These results are used to develop a paper prototype of VivoSpace, which is evaluated through interviews (N=11). These results are used to design a medium fidelity prototype for VivoSpace, which is tested in a laboratory through both direct and indirect methods (n=36).
The final iteration of VivoSpace is a high fidelity prototype, which is evaluated in a field experiment with clinical and non-clinical participants from Canada and USA (n=32). The results reveal positive changes for the participants associated with a clinic in self-efficacy for eating healthy food and leading an active lifestyle, attitudes towards healthy behaviour, and in the stages of change for health behaviour. These results are further validated by evaluating changes in health behaviour, which reveal a positive change for the clinical group in physical activity and an increase in patient activation. The evaluation of the high fidelity prototype allow for a final iteration of the ABC Framework, and the development of design principles for an OSN for positive health behaviour change.
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Tarbzouni, Abdulrahman I. "SocialRank : ranking users and information in online social networks." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53167.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (leaf 39).
The goal of this project is to explore the design and implementation of SocialRank. SocialRank is a personalized ranking algorithm that provides--for each user--ratings for people in his online social network. Subsequently, these ratings are used to rank incoming information received by the user from those in his social network. We analyze the use of actions on online social networks as proxies for measuring the strength of relationships between users and introduce an action scoring mechanism that uses different factors to evaluate an action's significance. We implement SocialRank in a generic online social network that we build as part of this research project and explore the effectiveness and usefulness of SocialRank.
by Abdulrahman I. Tarbzouni.
M.Eng.
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Chaabane, Abdelberi. "Online Social Networks : Is it the end of Privacy ?" Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM017/document.
Full textAbstract:
Les réseaux sociaux en ligne (OSNs) recueillent une masse de données à caractère privé. Le recueil de ces données ainsi que leur utilisation relèvent de nouveaux enjeux économiques et évoquent plusieurs questionnements notamment ceux relatifs à la protection de la vie privée. Notre thèse propose certaines réponses.Dans le premier chapitre nous analysons l'impact du partage des données personnelles de l'utilisateur sur sa vie privée. Tout d'abord, nous montrons comment les intérêts d'un utilisateur -- à titre d'exemple ses préférences musicales -- peuvent être à l'origine de fuite d'informations sensibles. Pour ce faire, nous inférons les attributs non divulgués du profil de l'utilisateur en exploitant d'autres profils partageant les même ''goûts musicaux''. Notre approche extrait la sémantique des intérêts en utilisant Wikipedia, les partitionne sémantiquement et enfin regroupe les utilisateurs ayant des intérêts semblables. Nos expérimentations réalisées sur plus de 104 milles profils publics collectés sur Facebook et plus de 2000 profils privés de bénévoles, montrent que notre technique d'inférence prédit efficacement les attributs qui sont très souvent cachés par les utilisateurs.Dans un deuxième temps, nous exposons les conséquences désastreuses du partage des données privées sur la sécurité. Nous nous focalisons sur les informations recueillies à partir de profils publics et comment celles-ci peuvent être exploitées pour accélérer le craquage des mots de passe. Premièrement, nous proposons un nouveau « craqueur » de mot de passe basé sur les chaînes de Markov permettant le cassage de plus de 80% des mots de passe, dépassant ainsi toutes les autres méthodes de l'état de l'art. Deuxièmement, et afin de mesurer l'impact sur la vie privée, nous proposons une méthodologie qui intègre les informations personnelles d'un utilisateur afin d'accélérer le cassage de ses mots de passe.Nos résultats mettent en évidence la nécessité de créer de nouvelles méthodes d'estimation des fuites d'informations personnelles, ce que nous proposons : il s'agit d'une méthode formelle pour estimer l'unicité de chaque profil en étudiant la quantité d'information portée par chaque attribut public.Notre travail se base sur la plate-forme publicitaire d'estimationd'utilisateurs de Facebook pour calculer l'entropie de chaque attribut public. Ce calcul permet d'évoluer l'impact du partage de ces informations publiquement. Nos résultats, basées sur un échantillon de plus de 400 mille profils publics Facebook, montrent que la combinaison de sexe, ville de résidence et age permet d'identifier d'une manière unique environ 18% des utilisateurs.Dans la deuxième section de notre thèse nous analysons les interactions entre la plate-forme du réseau social et des tiers et son impact sur à la vie privée des utilisateurs.Dans une première étude, nous explorons les capacités de « tracking » des réseaux sociaux Facebook, Google+ et Twitter. Nous étudions les mécanismes qui permettent à ces services de suivre d'une façon persistante l'activité web des utilisateurs ainsi que d'évaluer sa couverture. Nos résultats indiquent que le « tracking » utilisé par les OSNs couvre la quasi-totalité des catégories Web, indépendamment du contenu et de l'auditoire.Finalement, nous développons une plate-forme de mesure pour étudier l'interaction entre les plates-formes OSNs, les applications sociales et les « tierces parties » (e.g., fournisseurs de publicité). Nous démontrons que plusieurs applications tierces laissent filtrer des informations relatives aux utilisateurs à des tiers non autorisés. Ce comportement affecte à la fois Facebook et RenRen avec une sévérité variable :22 % des applications Facebook testées transmettent au moins un attribut à une entité externe. Quant à, RenRen, nous démontrons qu'il souffre d'une faille majeure causée par la fuite du jeton d'accès dans 69 % des casSharing information between users constitutes the cornerstone of the Web 2.0. Online Social Networks (OSN), with their billions of users, are a core component of this new generation of the web. In fact, OSNs offer innovative services allowing users to share their self-generated content (e.g., status, photos etc.) for free. However, this free access is usually synonymous with a subtle counterpart: the collection and usage of users' personal information in targeted advertisement. To achieve this goal, OSN providers are collecting a tremendous amount of personal, and usually sensitive, information about their users. This raises concerns as this data can be exploited by several entities to breach user privacy. The primary research goals of this thesis are directed toward understanding the privacy impact of OSNs.Our first contribution consists in demonstrating the privacy threats behind releasing personal information publicly. Two attacks are constructed to show that a malicious attacker (i.e., any external attacker with access to the public profile) can breach user privacy and even threaten his online security.Our first attack shows how seemingly harmless interests (e.g., music interests) can leak privacy-sensitive information about users. In particular, we infer their undisclosed (private) attributes using the public attributes of other users sharing similar interests. Leveraging semantic knowledge from Wikipedia and a statistical learning method, we demonstrated through experiments ---based on more than 104K Facebook profiles--- that our inference technique efficiently predicts attributes that are very often hidden by users.Our second attack is at the intersection of computer security and privacy. In fact, we show the disastrous consequence of privacy breach on security by exploiting user personal information ---gathered from his public profile--- to improve the password cracking process.First, we propose a Markov chain password cracker and show through extensive experiments that it outperforms all probabilistic password crackers we compared against. In a second step, we systematically analyze the idea that additional personal information about a user helps in speeding up password guessing. We propose a methodology that exploits this information in the cracking process and demonstrate that the gain can go up to 30%.These studies clearly indicate that publicly disclosing personal information harms privacy, which calls for a method to estimate this loss. Our second contribution tries to answer this question by providing a quantitative measure of privacy. We propose a practical, yet formally proved, method to estimate the uniqueness of each profile by studying the amount of information carried by public profile attributes. To achieve our goal, we leverage Ads Audience Estimation platform and an unbiased sample of more than 400K Facebook public profiles. Our measurement results show that the combination of gender, current city and age can identify close to 55% of users to within a group of 20 and uniquely identify around 18% of them.In the second part of this thesis, we investigate the privacy threats resulting from the interactions between the OSN platform and external entities. First, we explore the tracking capabilities of the three major OSNs (i.e., Facebook, Google+ and Twitter) and show that ``share-buttons'' enable them to persistently and accurately track users' web activity. Our findings indicate that OSN tracking is diffused among almost all website categories which allows OSNs to reconstruct a significant portion of users' web profile and browsing history.Finally, we develop a measurement platform to study the interaction between OSN applications --- of Facebook and RenRen --- and fourth parties. We show that several third party applications are leaking user information to ``fourth'' party entities such as trackers and advertisers. This behavior affects both Facebook and RenRen with varying severity
48
Monk, Adam Joel. "The Diffusion of New Music through Online Social Networks." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337902485.
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Albalawi, Rania. "Toward a Real-Time Recommendation for Online Social Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42255.
Full textAbstract:
The Internet increases the demand for the development of commercial applications and services that can provide better shopping experiences for customers globally. It is full of information and knowledge sources that might confuse customers. This requires customers to spend additional time and effort when they are trying to find relevant information about specific topics or objects. Recommendation systems are considered to be an important method that solves this issue. Incorporating recommendation systems in online social networks led to a specific kind of recommendation system called social recommendation systems which have become popular with the global explosion in social media and online networks and they apply many prediction algorithms such as data mining techniques to address the problem of information overload and to analyze a vast amount of data. We believe that offering a real-time social recommendation system that can understand the real context of a user’s conversation dynamically is essential to defining and recommending interesting objects at the ideal time.
In this thesis, we propose an architecture for a real-time social recommendation system that aims to improve word usage and understanding in social media platforms, advance the performance and accuracy of recommendations, and propose a possible solution to the user cold-start problem. Moreover, we aim to find out if the user’s social context can be used as an input source to offer personalized and improved recommendations that will help users to find valuable items immediately, without interrupting their conversation flow. The suggested architecture works as a third-party social recommendation system that could be incorporated with other existing social networking sites (e.g. Facebook and Twitter). The novelty of our approach is the dynamic understanding of the user-generated content, achieved by detecting topics from the user’s extracted dialogue and then matching them with an appropriate task as a recommendation. Topic extraction is done through a modified Latent Dirichlet Allocation topic modeling method. We also develop a social chat app as a proof of concept to validate our proposed architecture. The results of our proposed architecture offer promising gains in enhancing the real-time social recommendations.
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Agostinelli, Sara. "Impacts of student identity construction in online social networks." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Thesis/Summer2009/s_agostinelli_061809.pdf.
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