Dissertations / Theses on the topic 'Social prediction'
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1
Hayward, Peter C., and n/a. "From individual to social foresight." Swinburne University of Technology. Australian Graduate School of Entrepreneurship, 2005. http://adt.lib.swin.edu.au./public/adt-VSWT20061108.153623.
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To this point in time humanity has successfully responded to the challenges to its
existence. A viewpoint becoming widespread is that humanity will have to respond
to even greater challenges to its existence in the future. If adequate responses are
not formulated to these emerging challenges then a dystopian future for humanity
is a strong possibility. While experience can teach us how to act in the future it is
the express intent of this research that we should not have to experience dystopia
in order to learn how to prevent it. The innate human capacity for foresight has
played a pivotal role in responding to past challenges, however, a more extensive
form of foresight will need to be developed to respond to these future challenges.
That form of foresight will need to be both individual and social in nature.
Part I of this thesis generates an original theory of how foresight could develop
in individuals beyond our innate capacities. The theory argues that foresight ca-
pacities develop through the expansion of individual consciousness, particularly the
individual's sense of `self'. The theory is synthesised from the work of a num-
ber of psychological researchers including Jean Piaget, Jane Loevinger, Lawrence
Kohlberg, Clare Graves, Susan Cook-Greuter and Ken Wilber.
Part II is a two year study of students undertaking a postgraduate course in
strategic foresight. The study is utilised to add preliminary empirical support to
the theory proposed in Part I.
Part III integrates the previous two parts to further elaborate the attributes and
dynamics of individual foresight development before describing how social foresight
capacity can emerge from individual development. Expanded individual and so-
cial foresight capacities are achievable, but cannot be assumed. The contribution
of this thesis is to give a theoretical base to such development and to outline fur-
ther research. The development of individual foresight and the emergence of social
expressions of foresight can offer preferable, and not dystopian, futures for both
current and future generations.
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Hayward, Peter. "From individual to social foresight." Australasian Digital Thesis Program, 2005. http://adt.lib.swin.edu.au/public/adt-VSWT20061108.153623.
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Thesis (PhD) -- Swinburne University of Technology, Australian Graduate School of Entrepreneurship, 2005.Submitted to the fulfillment of the requirements for the degree of Doctor of Philosophy - Australian Graduate School of Entrepreneurship, Faculty of Business and Enterprise, Swinburne University of Technology, 2005. Typescript. Includes bibliographical references (p. 294-308).
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Pesquita, Ana. "The social is predictive : human sensitivity to attention control in action prediction." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59076.
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Observing others is predicting others. Humans have a natural tendency to make predictions about other people’s future behavior. This predisposition sits at the basis of social cognition: others become accessible to us because we are able to simulate their internal states, and in this way make predictions about their future behavior (Blakemore & Decety, 2001). In this thesis, I examine prediction in the social realm through three main contributions. The first contribution is of a theoretical nature, the second is methodological, and the third contribution is empirical. On the theoretical plane, I present a new framework for cooperative social interactions – the predictive joint-action model, which extends previous models of social interaction (Wolpert, Doya, & Kawato, 2003) to include the higher level goals of joint action and planning (Vesper, Butterfill, Knoblich, & Sebanz, 2010). Action prediction is central to joint-action. A recent theory proposes that social awareness to someone else’s attentional states underlies our ability to predict their future actions (Graziano, 2013). In the methodological realm, I developed a procedure for investigating the role of sensitivity to other’s attention control states in action prediction. This method offers a way to test the hypothesis that humans are sensitive to whether someone’s spatial attention was endogenously controlled (as in the case of choosing to attend towards a particular event) or exogenously controlled (as in the case of attention being prompted by an external event), independent of their sensitivity to the spatial location of that person’s attentional focus. On the empirical front, I present new evidence supporting the hypothesis that social cognition involves the predictive modeling of other’s attentional states. In particular, a series of experiments showed that observers are sensitive to someone else’s attention control and that this sensitivity occurs through an implicit kinematic process linked to social aptitude. In conclusion, I bring these contributions together. I do this by offering an interpretation of the empirical findings through the lens of the theoretical framework, by discussing several limitations of the present work, and by pointing to several questions that emerge from the new findings, thereby outlining avenues for future research on social cognition.Arts, Faculty of
Graduate
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Bahceci, Oktay, and Oscar Alsing. "Stock Market Prediction using Social Media Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166448.
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Stock Forecasting is commonly used in different forms everyday in order to predict stock prices. Sentiment Analysis (SA), Machine Learning (ML) and Data Mining (DM) are techniques that have recently become popular in analyzing public emotion in order to predict future stock prices. The algorithms need data in big sets to detect patterns, and the data has been collected through a live stream for the tweet data, together with web scraping for the stock data. This study examined how three organization's stocks correlate with the public opinion of them on the social networking platform, Twitter. Implementing various machine learning and classification models such as the Artificial Neural Network we successfully implemented a company-specific model capable of predicting stock price movement with 80% accuracy.
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Theriault, Jordan Eugene. "Morality as a Scaffold for Social Prediction." Thesis, Boston College, 2017. http://hdl.handle.net/2345/bc-ir:107624.
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Thesis advisor: Liane L. YoungThesis advisor: Elizabeth A. Kensinger
Theory of mind refers to the process of representing others’ mental states. This process consistently elicits activity in a network of brain regions: the theory of mind network (ToMN). Typically, theory of mind has been understood in terms of content, i.e. representing the semantic content of someone’s beliefs. However, recent work has proposed that ToMN activity could be better understood in the context of social prediction; or, more specifically, prediction error—the difference between observed and predicted information. Social predictions can be represented in multiple forms—e.g. dispositional predictions about who a person is, prescriptive norms about what people should do, and descriptive norms about what people frequently do. Part 1 examined the relationship between social prediction error and ToMN activity, finding that the activity in the ToMN was related to both dispositional, and prescriptive predictions. Part 2 examined the semantic content represented by moral claims. Prior work has suggested that morals are generally represented and understood as objective, i.e. akin to facts. Instead, we found that moral claims are represented as far more social than prior work had anticipated, eliciting a great deal of activity across the ToMN. Part 3 examined the relationship between ToMN activity and metaethical status, i.e. the extent that morals were perceived as objective or subjective. Objective moral claims elicited less ToMN activity, whereas subjective moral claimed elicited more. We argue that this relationship is best understood in the context of prediction, where objective moral claims represent strong social priors about what most people will believe. Finally, I expand on this finding and argue that a theoretical approach incorporating social prediction has serious implications for morality, or more specifically, for the motivations underlying normative compliance. People may be compelled to observe moral rules because doing so maintains a predictable social environment
Thesis (PhD) — Boston College, 2017
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Psychology
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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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Gao, Fei. "Structure based online social network link prediction study." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/structure-based-online-social-network-link-prediction-study(41697041-bfe4-4e64-a516-1a0703cfb4bb).html.
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This thesis shed light on the Internet-based social network link prediction problem. After reviewing recent research achievements in this area, two hypotheses are introduced: (i) The performance of topology-based network prediction methods and the characteristics of the networks are correlated. (ii) As networks are dynamic, the performance of prediction can be improved by providing different treatment to different nodes and links. To verify the Hypothesis (i), we conduct experiments with six selected online social networks. The correlation coefficients are calculated between six common network metrics and ten widely used topology-based network link prediction methods. The results show a strong correlation between Gini Coefficient and Preferential Attachment method. This study also reveals two types of networks: prediction-friendly network, for which most of the selected prediction methods perform well with an AUC result above 0.8, and prediction unfriendly network that on the contrary. For Hypothesis (ii), we proposed two network prediction models, the Hybrid Prediction Model and Community Bridge Boosting Prediction Model (CBBPM). The hybrid prediction model assumes network links are formed following different rules. The model linearly combines eight link prediction methods and the evolvement rules have been probed by finding the best weight for each of the methods by solving the linear optimization problem. This experiment result shows an improvement of prediction accuracy. This model takes link prediction as a time series problem. Different from Hybrid Prediction Model, CBBPM provides a different treatment on nodes. We define and classify network nodes as community bridge nodes in a novel approach based on their degree and links position in network communities. The similarity score that is calculated from the selected prediction methods is then boosted for predicting new links. The results from this model also show an enhancement of prediction accuracy. The two hypotheses are validated using the research experiments.
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Dimadi, Ioanna. "Social media sentiment analysis for firm's revenue prediction." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-363117.
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The advent of the Internet and its social media platforms have affected people’s daily life. More and more people use it as a tool in order to communicate, exchange opin-ions and share information with others. However, those platforms have not only been used for socializing but also for expressing people’s product preferences. This wide spread of social networking sites has enabled companies to take advantage of them as an important way of approaching their target audience. This thesis focuses on study-ing the influence of social media platforms on the revenue of a single organization like Nike that uses them actively. Facebook and Twitter, two widely-used social me-dia platforms, were investigated with tweets and comments produced by consumer’s online discussions in brand’s hosted pages being gathered. This unstructured social media data were collected from 26 Nike official pages, 13 fan pages from each plat-form and their sentiment was analyzed. The classification of those comments had been done by using the Valence Aware Dictionary and Sentiment Reasoner (VADER), a lexicon-based approach that is implemented for social media analysis. After gathering the five-year Nike’s revenue, the degree to which these could be affected by the clas-sified data was examined by using multiple stepwise linear regression analysis. The findings showed that the fraction of positive/total for both Facebook and Twitter ex-plained 84.6% of the revenue’s variance. Fitting this data on the multiple regression model, Nike’s revenue could be forecast with a root mean square error around 287 billion.
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Mallek, Sabrine. "Social Network Analysis : Link prediction under the Belief Function Framework." Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0204/document.
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Les réseaux sociaux sont de très grands systèmes permettant de représenter les interactions sociales entre les individus. L'analyse des réseaux sociaux est une collection de méthodes spécialement conçues pour examiner les aspects relationnels des structures sociales. L'un des défis les plus importants dans l'analyse de réseaux sociaux est le problème de prédiction de liens. La prédiction de liens étudie l'existence potentielle de nouvelles associations parmi des entités sociales non connectées. La plupart des approches de prédiction de liens se concentrent sur une seule source d'information, c'est-à-dire sur les aspects topologiques du réseau (par exemple le voisinage des nœuds) en supposant que les données sociales sont entièrement fiables. Pourtant, ces données sont généralement bruitées, manquantes et sujettes à des erreurs d'observation causant des distorsions et des résultats probablement erronés. Ainsi, cette thèse propose de gérer le problème de prédiction de liens sous incertitude. D'abord, deux nouveaux modèles de graphes de réseaux sociaux uniplexes et multiplexes sont introduits pour traiter l'incertitude dans les données sociales. L'incertitude traitée apparaît au niveau des liens et est représentée et gérée à travers le cadre de la théorie des fonctions de croyance. Ensuite, nous présentons huit méthodes de prédiction de liens utilisant les fonctions de croyance fondées sur différentes sources d'information dans les réseaux sociaux uniplexes et multiplexes. Nos contributions s'appuient sur les informations disponibles sur le réseau social. Nous combinons des informations structurelles aux informations des cercles sociaux et aux attributs des nœuds, ainsi que l'apprentissage supervisé pour prédire les nouveaux liens. Des tests sont effectués pour valider la faisabilité et l'intérêt de nos approches à celles de la littérature. Les résultats obtenus sur les données du monde réel démontrent que nos propositions sont pertinentes et valables dans le contexte de prédiction de liensSocial networks are large structures that depict social linkage between millions of actors. Social network analysis came out as a tool to study and monitor the patterning of such structures. One of the most important challenges in social network analysis is the link prediction problem. Link prediction investigates the potential existence of new associations among unlinked social entities. Most link prediction approaches focus on a single source of information, i.e. network topology (e.g. node neighborhood) assuming social data to be fully trustworthy. Yet, such data are usually noisy, missing and prone to observation errors causing distortions and likely inaccurate results. Thus, this thesis proposes to handle the link prediction problem under uncertainty. First, two new graph-based models for uniplex and multiplex social networks are introduced to address uncertainty in social data. The handled uncertainty appears at the links level and is represented and managed through the belief function theory framework. Next, we present eight link prediction methods using belief functions based on different sources of information in uniplex and multiplex social networks. Our proposals build upon the available information in data about the social network. We combine structural information to social circles information and node attributes along with supervised learning to predict new links. Tests are performed to validate the feasibility and the interest of our link prediction approaches compared to the ones from literature. Obtained results on social data from real-world demonstrate that our proposals are relevant and valid in the link prediction context
10
Suaysom, Natchanon. "Iterative Matrix Factorization Method for Social Media Data Location Prediction." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/hmc_theses/96.
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Since some of the location of where the users posted their tweets collected by social media company have varied accuracy, and some are missing. We want to use those tweets with highest accuracy to help fill in the data of those tweets with incomplete information. To test our algorithm, we used the sets of social media data from a city, we separated them into training sets, where we know all the information, and the testing sets, where we intentionally pretend to not know the location. One prediction method that was used in (Dukler, Han and Wang, 2016) requires appending one-hot encoding of the location to the bag of words matrix to do Location Oriented Nonnegative Matrix Factorization (LONMF). We improve further on this algorithm by introducing iterative LONMF. We found that when the threshold and number of iterations are chosen correctly, we can predict tweets location with higher accuracy than using LONMF.
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Howard, Philip. "Developing the actuarial prediction of violent and sexual reoffending." Thesis, University of Birmingham, 2014. http://etheses.bham.ac.uk//id/eprint/4802/.
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This thesis aims to develop and improve the actuarial prediction of violent and sexual offending. It demonstrates the importance of understanding offence classification and specialisation, and the value of dynamic risk factors in actuarial risk prediction. Its findings are especially relevant to prison and probation risk assessment and management practice in England and Wales, where the National Offender Management Service (NOMS) makes extensive use of the Offender Assessment System (OASys). Chapter 1 takes a novel, empirical approach to determining which offences should be counted as “violent” by a new nonsexual violence risk scale. Chapter 2 then develops this new scale, the OASys Violence Predictor (OVP), which combines static and dynamic risk factors, and validates it through comparison with NOMS’s existing scales. Chapter 3 then shows that OVP is also an equally good or superior predictor of nonsexual violence among offenders with a history of sexual offending. Chapter 4 shows that OVP’s dynamic risk factors have causal properties and reassessment over time improves prediction. Chapter 5 demonstrates the significance of offence specialisation by sexual offenders to risk predictor development. Chapter 6 concludes the thesis with an overview and discussion of the findings, limitations, practical implications, and future research directions.
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Sunar, Ayse Saliha. "Prediction of course completion based on participants' social engagement on a social-constructive MOOC platform." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/419583/.
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MOOCs offer world-widely accessible online content typically including videos, readings, quizzes along with social communication tools on a platform that enables participants to learn at their own pace. In 2016, over 58 million people join MOOCs. Far fewer people actually participate in MOOCs than originally sign up and then there is a steady attrition as courses progress. The observation of high attrition has prompted concerns among MOOC providers to mitigate their high attrition rates. Recent studies have been able to correlate social engagement of learners to course completion. Researchers use participants' digital traces to make sense of their engagement in a course and identify their needs to predict future patterns and to make interventions based on these patterns. The research reported here was conducted to further understand learners social engagement on a social-constructivist MOOC platform, the impact of engagement on course completion, and to predict learners' course completion. The findings of this research show that a commonly known social feature, follow, which is integrated into the Futurelearn MOOC platform has potential value in allowing tracking and analysing the behaviours of participants. The patterns of learners social engagement were modelled and a completion prediction model was developed. This model was successful at predicting those who might complete the course at a high or low success rate. The contributions of this research are that the behaviour chains could be the basis of a personalised recommender system, and the completion model based on social behaviour could contribute to wider prediction model based on a wider range of factors.
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Isah, Haruna. "Social Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methods." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16066.
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With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.
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Fetta, Angelico Giovanni. "Investigating social networks with Agent Based Simulation and Link Prediction methods." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/60113/.
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Social networks are increasingly being investigated in the context of individual behaviours. Research suggests that friendship connections have the ability to influence individual actions, change personal opinions and subsequently impact upon personal wellbeing. This thesis aims to investigate the effects of social networks, through the use of Agent Based Simulation (ABS) and Link Prediction (LP) methods. Three main investigations form this thesis, culminating in the development of a new simulation-based approach to Link Prediction (PageRank-Max) and a model of behavioural spread through a connected population (Behavioural PageRank-Max). The first project investigates the suitability of ABS to explore a connected social system. The Peter Principle is a theory of managerial incompetence, having the potential to cause detrimental effects to system efficiency. Through the investigation of a theoretical hierarchy of workplace social contacts, it is observed that the structure of a social network has the ability to impact system efficiency, demonstrating the importance of social network structure in conjunction with individual behaviours. The second project aims to further understand the structure of social networks, through the exploration of adolescent offline friendship data, taken from 'A Stop Smoking in Schools Trial' (ASSIST). An initial analysis of the data suggests certain factors may be pertinent in the formation of school social networks, identifying the importance of centrality measures. An ABS aiming to predict the evolution of the ASSIST social networks is created, developing an algorithm based upon the optimisation of an individual's eigen-centrality - termed PageRank-Max. This new approach to Link Prediction is found to predict ASSIST social network evolution more accurately than four existing prominent LP algorithms. The final part of this thesis attempts to improve the PageRank-Max method, by placing particular emphasis upon specific individual attributes. Two new methods are developed, the first restricting the search space of the algorithm (Behavioural Search), while the second alters its calculation process by applying specific attribute weights (Behavioural PageRank-Max). The results demonstrate the importance of individual attributes in adolescent friendship selection. Furthermore, the Behavioural PageRank-Max offers an approach to model the spread of behaviours in conjunction with social network structure, with the value of this being evaluated against alternative models.
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Lelonkiewicz, Jarosław Roman. "Cognitive mechanisms and social consequences of imitation." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/23490.
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When interacting, people imitate each other. This tendency is truly ubiquitous and occurs in many different situations and behaviours. But what causes it? Several mechanisms have been proposed to contribute to imitation. In this thesis, I focus on three candidate mechanisms: simulation, temporal adaptation, and the goal to affiliate with others. I start by discussing different imitative behaviours, and reviewing the evidence that imitation might at times emerge spontaneously. I also review the evidence suggesting that the three candidate mechanisms might be involved in such emergent imitation. Then, I present three sets of experiments. In the first set, I investigate the role of simulation in language processing. In three experiments, I test the hypothesis that comprehenders use their language production system to simulate their interlocutor, which in turn facilitates their ability to predict the next word they will see or hear. I manipulate whether participants read the sentences silently or aloud and measure their ability to predict the final word of a sentence. My results demonstrate that prediction is enhanced when people use their production system during reading aloud. This gives some credence to the idea that simulation is routinely engaged in language processing, which in turn opens up a possibility that it may contribute to linguistic imitation. In the second set of experiments, I investigate whether temporal adaptation leads agents to imitate features of their partner’s actions. In three experiments, I test this by manipulating the partner’s response speed and the information about the partner’s actions. I show that agents imitate response speed when they are able to observe the partner. Moreover, they adapt to the specific temporal pattern of their partner’s actions. These findings provide evidence for the engagement of the temporal adaptation mechanism during motor interactions, and for its involvement in imitation. In the third set of experiments, I turn to the hypothesis that people engage in linguistic imitation because they want to harness the social benefits it brings. I investigate a key assumption of this hypothesis: that imitation has positive consequences for the social interaction. In three experiments, I manipulate whether participants’ word choice is imitated or counter-imitated by their conversational partner and measure how it affects the participants’ evaluation of the interaction and the partner, and their willingness to cooperate with the partner. I find evidence that linguistic imitation has positive social consequences. These results are consonant with the claim that imitation is motivated by the goal to affiliate and foster social relations. Taken together, these findings suggest that imitation might occur both in motor actions and language, and that it might have diverse causes. My work on language suggests that the tendency to linguistically imitate others could both result from the simulation mechanism, and be motivated by the goal to affiliate. My work on motor actions shows that automatic temporal adaptation contributes to emergent imitation during interactions. This research is conducive to the greater aim of cross-examining the currently known mechanisms of imitation.
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Hinz, Jessica G. "Prediction of child abuse potential of pregnant teens : social support, conflict, attachment /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841149.
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Eiffert, Stuart Christopher. "Simultaneous Prediction and Planning in Crowds using Learnt Models of Social Response." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25959.
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The ability of autonomous mobile robots to work alongside humans and animals in real world environments has the potential to revolutionise the way in which many routine and labour intensive tasks are completed. Whilst we are seeing increasing applications in controlled environments, such as traffic and warehousing, robots are still far from ubiquitous in everyday life.
In unstructured environments, such as agriculture or pedestrian crowds, where interactions between agents are not guided by infrastructure, there exist additional challenges that need to be overcome before we are likely to see the widespread adoption of mobile robots.
Safe navigation in shared environments requires the accurate perception of nearby individuals using a robot's on board sensors. Additionally, the future motion of detected individuals needs to be predicted both for collision avoidance and efficient navigation. These predictions should reflect the inherent uncertainty of the individual's future, including the ways in which an individual might respond to its neighbours, including the robot itself. As such, there exists a dependency between any prediction of an individual's motion and the planned path of the robot, which needs to be accounted for both during the prediction and planning stages of navigation.
This thesis focuses on how prediction and planning can be approached in a single framework to address this dependency, using learnt models of social response within a sampling based path planner for simultaneous prediction and planning (SPP). Additional challenges faced in navigating shared and unstructured environments are also addressed, including predicting the uncertain branching and multi-modal nature of agent motion during social interactions, and overcoming the on-board limitations of mobile robots --- such as resource and sensing constraints --- in order to achieve extended autonomy.
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Mohammadi, Samin. "Analysis of user popularity pattern and engagement prediction in online social networks." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0019/document.
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De nos jours, les médias sociaux ont largement affecté tous les aspects de la vie humaine. Le changement le plus significatif dans le comportement des gens après l'émergence des réseaux sociaux en ligne (OSNs) est leur méthode de communication et sa portée. Avoir plus de connexions sur les OSNs apporte plus d'attention et de visibilité aux gens, où cela s'appelle la popularité sur les médias sociaux. Selon le type de réseau social, la popularité se mesure par le nombre d'adeptes, d'amis, de retweets, de goûts et toutes les autres mesures qui servaient à calculer l'engagement. L'étude du comportement de popularité des utilisateurs et des contenus publiés sur les médias sociaux et la prédiction de leur statut futur sont des axes de recherche importants qui bénéficient à différentes applications telles que les systèmes de recommandation, les réseaux de diffusion de contenu, les campagnes publicitaires, la prévision des résultats des élections, etc. Cette thèse porte sur l'analyse du comportement de popularité des utilisateurs d'OSN et de leurs messages publiés afin, d'une part, d'identifier les tendances de popularité des utilisateurs et des messages et, d'autre part, de prévoir leur popularité future et leur niveau d'engagement pour les messages publiés par les utilisateurs. A cette fin, i) l'évolution de la popularité des utilisateurs de l'ONS est étudiée à l'aide d'un ensemble de données d'utilisateurs professionnels 8K Facebook collectées par un crawler avancé. L'ensemble de données collectées comprend environ 38 millions d'instantanés des valeurs de popularité des utilisateurs et 64 millions de messages publiés sur une période de 4 ans. Le regroupement des séquences temporelles des valeurs de popularité des utilisateurs a permis d'identifier des modèles d'évolution de popularité différents et intéressants. Les grappes identifiées sont caractérisées par l'analyse du secteur d'activité des utilisateurs, appelé catégorie, leur niveau d'activité, ainsi que l'effet des événements externes. Ensuite ii) la thèse porte sur la prédiction de l'engagement des utilisateurs sur les messages publiés par les utilisateurs sur les OSNs. Un nouveau modèle de prédiction est proposé qui tire parti de l'information mutuelle par points (PMI) et prédit la réaction future des utilisateurs aux messages nouvellement publiés. Enfin, iii) le modèle proposé est élargi pour tirer profit de l'apprentissage de la représentation et prévoir l'engagement futur des utilisateurs sur leurs postes respectifs. L'approche de prédiction proposée extrait l'intégration de l'utilisateur de son historique de réaction au lieu d'utiliser les méthodes conventionnelles d'extraction de caractéristiques. La performance du modèle proposé prouve qu'il surpasse les méthodes d'apprentissage conventionnelles disponibles dans la littérature. Les modèles proposés dans cette thèse, non seulement déplacent les modèles de prédiction de réaction vers le haut pour exploiter les fonctions d'apprentissage de la représentation au lieu de celles qui sont faites à la main, mais pourraient également aider les nouvelles agences, les campagnes publicitaires, les fournisseurs de contenu dans les CDN et les systèmes de recommandation à tirer parti de résultats de prédiction plus précis afin d'améliorer leurs services aux utilisateursNowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user services
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Mohammadi, Samin. "Analysis of user popularity pattern and engagement prediction in online social networks." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0019.
Full textAbstract:
De nos jours, les médias sociaux ont largement affecté tous les aspects de la vie humaine. Le changement le plus significatif dans le comportement des gens après l'émergence des réseaux sociaux en ligne (OSNs) est leur méthode de communication et sa portée. Avoir plus de connexions sur les OSNs apporte plus d'attention et de visibilité aux gens, où cela s'appelle la popularité sur les médias sociaux. Selon le type de réseau social, la popularité se mesure par le nombre d'adeptes, d'amis, de retweets, de goûts et toutes les autres mesures qui servaient à calculer l'engagement. L'étude du comportement de popularité des utilisateurs et des contenus publiés sur les médias sociaux et la prédiction de leur statut futur sont des axes de recherche importants qui bénéficient à différentes applications telles que les systèmes de recommandation, les réseaux de diffusion de contenu, les campagnes publicitaires, la prévision des résultats des élections, etc. Cette thèse porte sur l'analyse du comportement de popularité des utilisateurs d'OSN et de leurs messages publiés afin, d'une part, d'identifier les tendances de popularité des utilisateurs et des messages et, d'autre part, de prévoir leur popularité future et leur niveau d'engagement pour les messages publiés par les utilisateurs. A cette fin, i) l'évolution de la popularité des utilisateurs de l'ONS est étudiée à l'aide d'un ensemble de données d'utilisateurs professionnels 8K Facebook collectées par un crawler avancé. L'ensemble de données collectées comprend environ 38 millions d'instantanés des valeurs de popularité des utilisateurs et 64 millions de messages publiés sur une période de 4 ans. Le regroupement des séquences temporelles des valeurs de popularité des utilisateurs a permis d'identifier des modèles d'évolution de popularité différents et intéressants. Les grappes identifiées sont caractérisées par l'analyse du secteur d'activité des utilisateurs, appelé catégorie, leur niveau d'activité, ainsi que l'effet des événements externes. Ensuite ii) la thèse porte sur la prédiction de l'engagement des utilisateurs sur les messages publiés par les utilisateurs sur les OSNs. Un nouveau modèle de prédiction est proposé qui tire parti de l'information mutuelle par points (PMI) et prédit la réaction future des utilisateurs aux messages nouvellement publiés. Enfin, iii) le modèle proposé est élargi pour tirer profit de l'apprentissage de la représentation et prévoir l'engagement futur des utilisateurs sur leurs postes respectifs. L'approche de prédiction proposée extrait l'intégration de l'utilisateur de son historique de réaction au lieu d'utiliser les méthodes conventionnelles d'extraction de caractéristiques. La performance du modèle proposé prouve qu'il surpasse les méthodes d'apprentissage conventionnelles disponibles dans la littérature. Les modèles proposés dans cette thèse, non seulement déplacent les modèles de prédiction de réaction vers le haut pour exploiter les fonctions d'apprentissage de la représentation au lieu de celles qui sont faites à la main, mais pourraient également aider les nouvelles agences, les campagnes publicitaires, les fournisseurs de contenu dans les CDN et les systèmes de recommandation à tirer parti de résultats de prédiction plus précis afin d'améliorer leurs services aux utilisateursNowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user services
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Burton, Paul E. Wircenski Jerry L. "Dimensions of social networks as predictors of employee performance." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3994.
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Britt, Chester Lamont III. "Crime, criminal careers and social control: A methodological analysis of economic choice and social control theories of crime." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185168.
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This study tests the validity of two theories of crime: economic choice (as manifest in the criminal career paradigm) and social control. The test of these two theories is primarily methodological, in that four types of crime data (official and longitudinal (Uniform Crime Reports), official and cross-sectional (Bail Decisionmaking Study), self-report and longitudinal (National Youth Survey), and self-report and cross-sectional (Seattle Youth Study)) and a variety of graphical and statistical techniques are used to compare findings on (1) the stability of the age distribution of crime, (2) the prevalence of offense specialization, and (3) the differences in the causes of participating in crime compared to the causes of frequency of criminal activity among those individuals committing crimes. The findings on the relation between age and crime show the general shape of the age-crime curve is stable across year of the data or curve, type of data, cohort, and age group. The tests for offense specialization reveal that offenders are versatile. An individual's current offense type is not predictable, with much accuracy, on the basis of prior offending. Again, the lack of offense specialization held across type of data, but age, race, and gender distinctions also failed to alter significantly the observed pattern of versatility. Findings on the causes of participation in crime and frequency of criminal activity among active offenders showed only trivial differences in the set of statistically significant predictors for each operationalization of crime and delinquency. Two distinct operationalizations of frequency also showed no substantial difference in the set of statistically significant predictors. Similar to the findings on age and crime, and offense specialization, the pattern of results for the participation and frequency analyses held across type of data. In sum, the results tended to support the predictions of social control theory over those of the economic choice-criminal career view of crime.
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Choi, Yoonjoo. "Protein loop structure prediction." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:bd5c1b9b-89ba-4225-bc17-85d3f5067e58.
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This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.
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Murgraff, Vered. "Exploring the applicability of social cognition models to the understanding of higher risk single-occasion drinking." Thesis, University of East London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265081.
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Stephens, Nick. "The North Korean conundrum and the deficiencies of western-rational social theory." Diss., Connect to the thesis, 2007. http://hdl.handle.net/10066/1060.
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Rawashdeh, Ahmad. "Semantic Similarity of Node Profiles in Social Networks." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922.
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Flatley, Kirsty Jo-Anne. "The efficacy of social cognition models in the prediction of alcohol-related behaviours." Thesis, University of Strathclyde, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442022.
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Shahbazi, Maryam. "Identification and Prediction of Opinion Leaders in Large Scale Enterprise Social Networks (ESNs)." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18867.
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Increased application of Enterprise Social Network (ESN) platforms at the workplace extended the sources of influence on corporate social communities from management to unofficial opinion leaders that can have a direct influence on individuals' decision-making using ESN platforms. This study has attempted to examine the relationship between network structural positions and behaviours of ESN users and their ability to influence other users via ESN platforms. By analysing the pattern of user interactions via the ESN platform (Yammer), this study revealed that over (𝐍≅𝟑𝟑%) of users’ who voluntary participated at online group discussion forums were triggered by less than (𝐧≅𝟑%) of opinion-makers in ESN platforms. Correspondingly, the results of this study showed that membership in various online workgroups, structural position of actors in social networks, and the degree of actors and groups’ online activities largely impact a user’s ability to become an opinion leader or an influencer within corporate social environments. Looking further into the results it is revealed that memberships in various workgroups significantly promote the users’ ability of influence, however, the number of direct ties do not promote the degree of influence with the same rate. This means users who are members of highly dynamic online groups are more likely to influence others compared with those who are members of limited communities regardless of the number of social communities’ members. Considering these facts, this study has proposed a model to compute an influence score which represents the ability of an ESN’s user to influence others in decision making. Subsequently, a set of predictor features were extracted to successfully predict this score.
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Zhu, Linhong, Dong Guo, Junming Yin, Steeg Greg Ver, and Aram Galstyan. "Scalable temporal latent space inference for link prediction in dynamic social networks (extended abstract)." IEEE, 2017. http://hdl.handle.net/10150/626028.
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Understanding and characterizing the processes driving social interactions is one of the fundamental problems in social network research. A particular instance of this problem, known as link prediction, has recently attracted considerable attention in various research communities. Link prediction has many important commercial applications, e.g., recommending friends in an online social network such as Facebook and suggesting interesting pins in a collection sharing network such as Pinterest. This work is focused on the temporal link prediction problem: Given a sequence of graph snapshots G1, · ··, Gt from time 1 to t, how do we predict links in future time t + 1? To perform link prediction in a network, one needs to construct models for link probabilities between pairs of nodes. A temporal latent space model is proposed that is built upon latent homophily assumption and temporal smoothness assumption. First, the proposed modeling allows to naturally incorporate the well-known homophily effect (birds of a feather flock together). Namely, each dimension of the latent space characterizes an unobservable homogeneous attribute, and shared attributes tend to create a link in a network.
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Jonathan, Joan. "Prediction of Factors Influencing Rats Tuberculosis Detection Performance Using Data Mining Techniques." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385471.
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This thesis aimed to predict the factors that influence rats TB detection performance using data mining techniques. A rats TB detection performance dataset was given from APOPO TB training and research center in Morogoro, Tanzania. After data preprocessing, the size of the dataset was 471,133 rats TB detection performance observations and a sample size of 4 female rats. However, in the analysis, only 200,000 data observations were used. Based on the CRISP-DM methodology, this thesis used R language as a data mining tool to analyze the given data. To build the predictive model the classification technique was used to predict the influencing factors and classify rats using a decision tree, random forest, and naive Bayes algorithms. The built predictive models were validated with the same test data to check their classification prediction accuracy and to find the best. The results pinpoint that the random forest is the best predictive model with an accuracy of 78.82%. However, the accuracy differences are negligible. When considering the predictive model accuracy (78.78%) and speed (3 seconds) of the decision tree, it is the best predictive model since it has less building time compared to the random forest (154 seconds). Moreover, the results manifest that age is the most significant influencing factor, and rats of ages between 3.1 to 6 years portrayed potentiality in detection performance. The other predicted factors are Session_Completion_Time, Session_Start_Time, and Av_Weight_Per_Year. These results are useful as a reference to rats TB trainers and researchers in rats TB and Information Systems. Further research using other data mining techniques and tools is valuable.
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Princiotta, Dana Kristina. "Predicting Autism in Young Children Based on Social Interaction and Selected Demographic Variables." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145365.
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The purpose of the present study was to examine whether an autism diagnosiscould be predicted by social interaction as measured by the Ghuman-Folstein Screen forSocial Interaction in conjunction with selected demographic variables (i.e., sex, age,ethnicity, mother's educational level, and socio-economic status). Univariate andbivariate analyses were conducted to explore each predictor variable and to explorepossible relationships between predictor variables and autism. Binary logistic regressionwas utilized to examine various models' ability to predict autism. The final model wasable to correctly identify 74% of the cases. The GF-SSI was the greatest predictor ofautism. The selected demographic variables were not significant predictors of autism.These results were discussed in relation to the literature on sex, age, ethnicity, maternaleducation and socio-economic status. Future directions for research were also discussed.
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Arsiwalla, Dilbur D. Pettit Gregory S. "The interplay of positive parenting and positive social information processing in the prediction of children's social and behavioral adjustment." Auburn, Ala, 2009. http://hdl.handle.net/10415/1812.
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Lowy, Elliott. "The evolution of the golden rule /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9017.
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Cutugno, Carmen. "Statistical models for the corporate financial distress prediction." Thesis, Università degli Studi di Catania, 2011. http://hdl.handle.net/10761/283.
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Adewopo, Victor A. "Exploring Open Source Intelligence for cyber threat Prediction." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162491804723753.
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Yasheen, Sharifa. "Evaluation of Markov Models in Location Based Social Networks in Terms of Prediction Accuracy." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13039.
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Location Based Social Networks has attracted millions of mobile internet users. On their smart phones people can share their locations using social network services. The main purpose of check-ins is to provide other users’ information about places they visit. Location Based Social Network with thousands of check-ins allows users to learn social behavior through spatial-temporal effect, which provides different services such as place recommendation and traffic prediction. Through this information, we can have an idea about important locations in the city and human mobility. The main purpose of this thesis is to evaluate Markov Models in Location Based Social Networks in terms of prediction accuracy. Location Based Social Network features and basic information’s will be analyzed before modeling of human mobility. Afterwards with the use of three methods human mobility will be modeled. In all the models the check-ins are analyzed based on prior category. After estimation the user’s possible next check-in category, and according to the user’s check-ins in the following category, it predicts the next possible check-in location. Finally a comparison will be made considering the models prediction accuracy.
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Hoyle, Jonathan. "Stressful events, cognition, and perceived social support in the prediction of depression in adolescence /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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Mansouri, Mehrdad. "Social Approaches to Disease Prediction." Thesis, 2014. http://hdl.handle.net/1828/5735.
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Objective: This thesis focuses on design and evaluation of a disease prediction system that be able to detect hidden and upcoming diseases of an individual. Unlike previous works that has typically relied on precise medical examinations to extract symptoms and risk factors for computing probability of occurrence of a disease, the proposed disease prediction system is based on similar patterns of disease comorbidity in population and the individual to evaluate the risk of a disease.
Methods: We combine three machine learning algorithms to construct the prediction system: an item based recommendation system, a Bayesian graphical model and a rule based recommender. We also propose multiple similarity measures for the recommendation system, each useful in a particular condition. We finally show how best values of parameters of the system can be derived from optimization of cost function and ROC curve.
Results: A permutation test is designed to evaluate accuracy of the prediction system accurately. Results showed considerable advantage of the proposed system in compare to an item based recommendation system and improvements of prediction if system is trained for each specific gender and race.
Conclusion: The proposed system has been shown to be a competent method in accurately identifying potential diseases in patients with multiple diseases, just based on their disease records. The procedure also contains novel soft computing and machine learning ideas that can be used in prediction problems. The proposed system has the possibility of using more complex datasets that include timeline of diseases, disease networks and social network. This makes it an even more capable platform for disease prediction. Hence, this thesis contributes to improvement of the disease prediction field.Graduate
0800
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mehrdadmansouri@yahoo.com
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Sahoo, Shaktisri Anilranjan. "Link Prediction in Social Networks." Thesis, 2013. http://ethesis.nitrkl.ac.in/5217/1/109CS0184.pdf.
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In a social network there can be many different kind of links or edges between the nodes. Those could for example be social contacts, hyper-references or phone calls. Link Prediction is the problem of predicting edges that either don't yet exist at the given time t or exist, but have not been discovered, are likely to occur in the near future. We develop approaches to link prediction based on measures for analysing the proximity of nodes in a network. Consider a co-authorship network among scientists, e.g. two scientists who are close in the network will have colleagues in common, so they are more likely to collaborate in the near future. Our goal is to make this intuitive notion precise and to understand which measures of proximity in a network lead to the most accurate link predictions. Link prediction algorithms can be classified into three categories: Node neighbourhood approaches, Path based approaches and Meta approaches. Node neighbourhood approach is based on local features of a network, focusing mainly on the nodes structure(i.e. based on the number of common friends that two users share). The local-based measures are: Common Neighbors, Jaccards coefficient, Adamic/Adar and Preferential Attachment. Path based algorithms considers the ensemble of all paths between two nodes. The Path based algorithms are: Katz, Sim-Rank, Hitting Time and Commute Time, Rooted PageRank, PropFlow and High-Performance Link Prediction. Meta-Approaches alter the data before being passed to one of the path based approaches. The algorithms are: Low-rank approximation, Unseen bigrams and Clustering.
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Lin, Chuan-Heng, and 林泉亨. "MRT Demand Prediction through Social Media." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/08325576544480906430.
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碩士國立臺灣大學
土木工程學研究所
103
With the technological improvements of mobile devices and the increasing number of social media posts, there are more and more data on human mobility based on which information could potentially be extracted. Current research related to social media are mostly focused on inter-person behaviors. Conversely, related topics on system level performances are rarely discussed. This thesis applies feature extraction methods on quantitative, textual, and image data to retrieve useful features from social media. In addition, a machine learning pipeline based on support vector machine, random forest and stochastic gradient boosting is constructed for a short-term transportation demand forecast. Furthermore, real-world datasets from Instagram together with the demand data of the Taipei Metro Rapid Transit system are demonstrated in this work. Validation results show that social media has the potential to enhance the forecasting accuracy.
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Santos, Hugo Filipe Paulino dos. "Social network embeddings for churn prediction." Master's thesis, 2020. http://hdl.handle.net/10071/22051.
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With the large adoption of Internet customers became more aware of existing services
and their prices. From the perspective of companies acquiring a new customer is more
expensive than maintaining existing ones. In this sense, companies began to address the
challenge of leaving customers to other companies. Customer churn is even more challenging
in the telecommunications sector, because customers can change operator faster due to
shorter loyalty period and easy migration service to other telecommunications operators
without associated costs. Anticipating churn is therefore a major concern for telecommunication
companies, which leads them to carry out retention campaigns for these customers.
Predictive models allows us to predict whether a customer will leave their operator using
that client’s past information. The present work describes how a predictive model was
build to predict the outflow of customers exploring customer relationships. Unlike other
works, it uses a social network analysis that takes advantage of small customer representations
(network embeddings) and allows to obtain better results than other methods.Com a generalização da Internet os clientes tornaram-se mais informados dos serviços existentes e dos seus preços. Na perspetiva das empresas, adquirir um novo cliente é mais dispendioso que manter os existentes. Nesse sentido as empresas começaram a abordar o desafio da saída de clientes para outras companhias. A saída de clientes é ainda mais desafiante no setor das telecomunicações, porque os clientes podem mudar de operador com maior rapidez devido ao período de fidelização mais curto e à fácil migração do serviço para outros operadores de telecomunicações sem custos associados. Antecipar a saída é, portanto, uma grande preocupação para as empresas de telecomunicações, que as leva a realizar campanhas de retenção para esses clientes. Modelos preditivos permitem prever se um cliente vai abandonar a sua operadora atual usando informação passada desse cliente. O presente trabalho detalha como foi construído um modelo preditivo para prever a saída de clientes explorando relacionamentos entre clientes. Ao contrário de outros trabalhos, este utiliza uma análise de rede social que tira partido de representações de baixa dimensionalidade dos clientes (network embeddings) e permite obter melhores resultados que outros métodos.
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Silva, Ivo Lima da. "Hashtag popularity prediction for social networks." Master's thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/111214.
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Peng, Sin-Ya, and 彭新雅. "Emerging Topics Prediction on Social Media." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/azye93.
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Silva, Ivo Lima da. "Hashtag popularity prediction for social networks." Dissertação, 2018. https://repositorio-aberto.up.pt/handle/10216/111214.
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Fu, Chun-Hao, and 傅駿浩. "Link Prediction for Social User-Item Networks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/61110466335513386640.
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碩士國立清華大學
通訊工程研究所
101
Recommendation is the most popular tool to help users find the new items they are interested in. We study the link prediction problem on the author-conference network of DBLP data set, and we would like to predict what conferences the author will publish in. Collaborative filtering is the most common method to suggest items for users. However, the limitation of this approach is the sparsity problem. As a result, we perform the random walk on the graph to calculate the transition probability for predicting. We consider not only the bipartite graph but also the relationship of these authors, so we perform the random walk on this union of two graphs. Experimental results show it can predict more precisely when choosing the appropriate parameters in our algorithm, and it is useful with the information of the friendship.
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Chou, Hsiao-Yu, and 周筱瑜. "Performance Prediction Model for Social Media Campaign." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/k5qa32.
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碩士國立交通大學
資訊科學與工程研究所
105
Advertising is a very important part in business activities and is needed for marketing and building brand image. By the developing of the internet, social media become popular. The advertisements on social media can be shown to target audience at any time and any place for certain objective. It is surly more effective than traditional advertisements and becomes a trend. However, the performance cannot be known before the campaign runs. Thus, it is hard for campaign managers to decide the budget. No matter the budget is too high or too low can cause waste of money. Therefore, we proposed a model to predict social media campaign performance. The model can predict the performance of fan page promoting campaigns on Facebook. It includes four phases. First, we preprocess the data by removing outliers and normalizing. Second, we group the data into several clusters according the characteristics with K-Means clustering. Third, we build decision trees for each cluster in order to predict the cost per fan (CPF). Finally, we expand the result we get from the decision trees and decide the final result. According to the experiments, the hit rate is 61%. With this model, we can provide the result to campaign owners and help them allocating the budget.
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Yang, Ya-Han, and 楊亞瀚. "Influence Maximization Prediction on Dynamic Social Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4gb4rj.
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碩士淡江大學
資訊工程學系碩士班
106
Up to now, much literature has focused on influence maximization problem. These methods and literature are all based on the greedy algorithm. However, social networks are growing rapidly, so the efficiency and scalability of the algorithm have become more important. In recent years, the study on the issue of influence maximization has also focused on the efficiency of the algorithm, but these studies are all based on the analysis of the static social network. However, every minute and second has new relationships or interaction on the social network, we describe this status as the dynamic social network. Therefore, in this paper, we focus on the analysis of the dynamic social network. By observing the changes in the dynamic social network structure, we can find out the pattern of variation and build a model to predict the future network structure. Eventually, using an efficient algorithm to solve the influence maximization problem based on the dynamic social network. The experimental results show that our prediction model has a high accuracy, we also can obtain a seed set different from the analysis of static social network and get more influence spreads.
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Chen, Ju-Peng, and 陳如芃. "Prediction of social annotation on resource-sharing services." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78370794966992125865.
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碩士國立臺灣大學
電機工程學研究所
95
Popular social bookmark service del.icio.us enables easy annotation for user to orga- nize their resources. The lightweight conceptual structure built by users called folkson- omy is able to provide a di?erent retrieval service that utilzes its power. However, the performance was hindered by lack of tags at a resource’s new arrival. Thus, our work aims to overcome the handicap of retreival for new-coming URLs by predicting tags at early stage. We exploit accumulated tagging records from users to predict tags. Our experiements on del.icio.us URLs show that our algorithm has a high coverage of tags appearing in the mature stage. Our prediction captures 80% of a 13-month old tag set at the first month and 80.23% of 100-users tag set with 5-users tag set.
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Chen, Ju-Peng. "Prediction of social annotation on resource-sharing services." 2007. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1206200719380800.
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Ivashkevych, E., and Yuliia Chala. "Social knowledge, social thinking, social prediction and social intuition in the paradigm of social intellect of a person." Thesis, 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/46371.
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Xu, Feifei. "Data Mining in Social Media for Stock Market Prediction." 2012. http://hdl.handle.net/10222/15459.
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In this thesis, machine learning algorithms are used in NLP to get the public sentiment on individual stocks from social media in order to study its relationship with the stock price change. The NLP approach of sentiment detection is a two-stage process by implementing Neutral v.s. Polarized sentiment detection before Positive v.s. Negative sentiment detection, and SVMs are proved to be the best classifiers with the overall accuracy rates of 71.84% and 74.3%, respectively. It is discovered that users’ activity on StockTwits overnight significantly positively correlates to the stock trading volume the next business day. The collective sentiments for afterhours have powerful prediction on the change of stock price for the next day in 9 out of 15 stocks studied by using the Granger Causality test; and the overall accuracy rate of predicting the up and down movement of stocks by using the collective sentiments is 58.9%.