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Journal articles on the topic 'Online social networks'

To see the other types of publications on this topic, follow the link: Online social networks.

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Melton, James, Robert Miller, and Michelle Salmona. "Online Social Networks." International Journal of Information Systems and Social Change 3, no. 2 (April 2012): 24–38. http://dx.doi.org/10.4018/ijissc.2012040102.

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Previous research has shown that many college students in the United States post content to social networking sites that they know would be considered inappropriate by employers and other authority figures. However, the phenomenon has not been extensively studied in cross-cultural context. To address this knowledge gap, a survey of college students in Australia, Denmark, the United Kingdom, and the United States was conducted. The study found a universal tendency among the four groups: students knew the content they were posting would be considered inappropriate by employers and other authority figures, but they chose to post it anyway. The article also reports on differences in the way this tendency was manifested and on related aspects of social networking across cultures, including decisions about privacy and information disclosure.

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2

Cardon, Peter W. "Online Social Networks." Business Communication Quarterly 72, no. 1 (July 22, 2008): 96–97. http://dx.doi.org/10.1177/1080569908330376.

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3

Faloutsos, Michalis, Thomas Karagiannis, and Sue Moon. "Online social networks." IEEE Network 24, no. 5 (September 2010): 4–5. http://dx.doi.org/10.1109/mnet.2010.5578911.

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4

Fu, Xiaoming, Andrea Passarella, Daniele Quercia, Alessandra Sala, and Thorsten Strufe. "Online Social Networks." Computer Communications 73 (January 2016): 163–66. http://dx.doi.org/10.1016/j.comcom.2015.11.005.

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5

Howard, Bill. "Analyzing online social networks." Communications of the ACM 51, no. 11 (November 2008): 14–16. http://dx.doi.org/10.1145/1400214.1400220.

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6

Gabielkov, Maksym, Ashwin Rao, and Arnaud Legout. "Sampling online social networks." ACM SIGCOMM Computer Communication Review 44, no. 4 (February 25, 2015): 127–28. http://dx.doi.org/10.1145/2740070.2631452.

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7

Mehta, Neil, and Ashish Atreja. "Online social support networks." International Review of Psychiatry 27, no. 2 (March 4, 2015): 118–23. http://dx.doi.org/10.3109/09540261.2015.1015504.

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8

Papagelis, Manos, Gautam Das, and Nick Koudas. "Sampling Online Social Networks." IEEE Transactions on Knowledge and Data Engineering 25, no. 3 (March 2013): 662–76. http://dx.doi.org/10.1109/tkde.2011.254.

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9

Garton, Laura, Caroline Haythornthwaite, and Barry Wellman. "Studying Online Social Networks." Journal of Computer-Mediated Communication 3, no. 1 (June 23, 2006): 0. http://dx.doi.org/10.1111/j.1083-6101.1997.tb00062.x.

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10

Angiani, Giulio, Paolo Fornacciari, Eleonora Iotti, Monica Mordonini, and Michele Tomaiuolo. "Participation in Online Social Networks." International Journal of Interactive Communication Systems and Technologies 8, no. 2 (July 2018): 36–55. http://dx.doi.org/10.4018/ijicst.2018070103.

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Why and how more and more people get involved and use social networking systems are critical topics in social network analysis (SNA). As a matter of fact, social networking systems bring online a growing number of acquaintances, for many different purposes. Both business interests and personal recreational goals are motivations for using online social networks (OSN) or other social networking systems. The participation in social networks is a phenomenon which has been studied with several theories, and SNA is useful for common business problems, e.g., launching distributed teams, retaining people with vital knowledge for the organization, improving access to knowledge and spreading ideas and innovation. Nevertheless, there are some difficulties, such as anti-social behaviors of participants, lack of incentives, organizational costs and risks. In this article, a survey of the basic features of SNA, participation theories and models are discussed, with emphasis on social capital, information spreading, motivations for participation, and anti-social behaviors of social network users.

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Lu, Yingjie, Xinwei Wang, Lin Su, and Han Zhao. "Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities." Mathematics 11, no. 21 (October 24, 2023): 4412. http://dx.doi.org/10.3390/math11214412.

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Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

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12

Jalili, Mahdi, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, and Matjaž Perc. "Link prediction in multiplex online social networks." Royal Society Open Science 4, no. 2 (February 2017): 160863. http://dx.doi.org/10.1098/rsos.160863.

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Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

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13

Walczak, Steven. "Artificial Neural Network Research in Online Social Networks." International Journal of Virtual Communities and Social Networking 10, no. 4 (October 2018): 1–15. http://dx.doi.org/10.4018/ijvcsn.2018100101.

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Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network “friends” is developed to demonstrate the applicability of artificial neural networks for virtual community research.

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14

Korzynski, Pawel. "Online social networks and leadership." International Journal of Manpower 34, no. 8 (November 11, 2013): 975–94. http://dx.doi.org/10.1108/ijm-07-2013-0173.

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15

Rejaie, Reza, Mojtaba Torkjazi, Masoud Valafar, and Walter Willinger. "Sizing up online social networks." IEEE Network 24, no. 5 (September 2010): 32–37. http://dx.doi.org/10.1109/mnet.2010.5578916.

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Willinger, Walter, Reza Rejaie, Mojtaba Torkjazi, Masoud Valafar, and Mauro Maggioni. "Research on online social networks." ACM SIGMETRICS Performance Evaluation Review 37, no. 3 (January 21, 2010): 49–54. http://dx.doi.org/10.1145/1710115.1710125.

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17

Summerskill, Benjamin. "Online social networks and wellbeing." Lancet 374, no. 9689 (August 2009): 514. http://dx.doi.org/10.1016/s0140-6736(09)61472-0.

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18

Li, Le, Ke Gu, An Zeng, Ying Fan, and Zengru Di. "Modeling online social signed networks." Physica A: Statistical Mechanics and its Applications 495 (April 2018): 345–52. http://dx.doi.org/10.1016/j.physa.2017.12.089.

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19

Conti, Marco, and Andrea Passarella. "Online Social Networks and Media." Online Social Networks and Media 1 (June 2017): iii—vi. http://dx.doi.org/10.1016/s2468-6964(17)30045-9.

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20

Mayer, Adalbert. "Online social networks in economics." Decision Support Systems 47, no. 3 (June 2009): 169–84. http://dx.doi.org/10.1016/j.dss.2009.02.009.

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21

Greenhow, Christine. "Online social networks and learning." On the Horizon 19, no. 1 (February 2011): 4–12. http://dx.doi.org/10.1108/10748121111107663.

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22

Zhang, Huiling, Md Abdul Alim, Xiang Li, My T. Thai, and Hien T. Nguyen. "Misinformation in Online Social Networks." ACM Transactions on Information Systems 34, no. 3 (May 5, 2016): 1–24. http://dx.doi.org/10.1145/2885494.

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23

Bonato, Anthony, Noor Hadi, Paul Horn, Paweł Prałat, and Changping Wang. "Models of Online Social Networks." Internet Mathematics 6, no. 3 (January 2009): 285–313. http://dx.doi.org/10.1080/15427951.2009.10390642.

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24

Sabatini, Fabio, and Francesco Sarracino. "Online Social Networks and Trust." Social Indicators Research 142, no. 1 (April 4, 2018): 229–60. http://dx.doi.org/10.1007/s11205-018-1887-2.

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25

Krasnova, Hanna, Sarah Spiekermann, Ksenia Koroleva, and Thomas Hildebrand. "Online Social Networks: Why We Disclose." Journal of Information Technology 25, no. 2 (June 2010): 109–25. http://dx.doi.org/10.1057/jit.2010.6.

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On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of Industry players and policymakers worldwide. Despite the Impressive scope of this phenomenon, very little Is understood about what motivates users to disclose personal Information. Integrating focus group results Into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose Information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

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26

Kim, Jooho, and Makarand Hastak. "Social network analysis: Characteristics of online social networks after a disaster." International Journal of Information Management 38, no. 1 (February 2018): 86–96. http://dx.doi.org/10.1016/j.ijinfomgt.2017.08.003.

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27

Yudina, E. N., and I. V. Alekseenko. "Characteristics of Solidarity on Social Networks." Communicology 8, no. 1 (March 31, 2020): 114–27. http://dx.doi.org/10.21453/2311-3065-2020-8-1-114-127.

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The social networking influences all areas of society and stimulates the activity of the social networks communities. Later the importance of social networks revised the researches of the sociological aspects of social solidarity. Solidarity of modern society intensifies according to a sharp increase of possibilities to communicate with the help of the social networks that has become tools of forming the online communities. The authors suggest a new term for characteristics of a new solidarity at network communities – online solidarity. This term shows the connotation with the Internet; it also shows that online solidarity arises on micro level of the interpersonal relations. In the article authors analyze the term and characteristics of online solidarity in network communities and represent the results of the original research (questionnaire, 2019) “Students on Social Networks”. The authors come to conclusion that users with lots of “friends” at social networks feel more successful and socially demanded. Despite of its flexibility and lightness online-solidarity plays an important role in the society and has an influence on the macro level of society.

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28

Hristova, Desislava, Pietro Panzarasa, and Cecilia Mascolo. "Multilayer Brokerage in Geo-Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 159–67. http://dx.doi.org/10.1609/icwsm.v9i1.14629.

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Open network structures and brokerage positions have long been seen as playing a crucial role in sustaining social capital and competitive advantage. The degree to which individuals intermediate between otherwise disconnected others can differ across online and offline social networks. For example, users may broker online between two others who then exchange offline the information received through social media. Yet network studies of social capital have often neglected the interplay between online and offline interactions, and have concentrated primarily on a single layer. Here, we propose a geo-social multilayer approach to brokerage that casts light on the integrated online and offline foundations of social capital. Drawing on a data set of 37,722 Foursquare users in London, we extend the notion of brokerage by examining users’ positions in an online social network and their offline mobility patterns through checkins. We find that social and geographic brokerage positions are distinct and asymmetric across the social and co-location networks. On the one hand, users may appear to be brokers online when in fact their ability to intermediate would be mitigated if their offline positions were also taken into account. On the other, users who appear to have little brokerage power offline may be active brokers within networks that combine both online and offline interactions.

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29

Fani, Hossein, and Ebrahim Bagheri. "Community detection in social networks." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630001. http://dx.doi.org/10.1142/s2425038416300019.

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Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.

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30

Li, Jianghao, and Guo Yang. "Network embedding enhanced intelligent recommendation for online social networks." Future Generation Computer Systems 119 (June 2021): 68–76. http://dx.doi.org/10.1016/j.future.2021.01.017.

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31

Bollen, Johan, Bruno Gonçalves, Guangchen Ruan, and Huina Mao. "Happiness Is Assortative in Online Social Networks." Artificial Life 17, no. 3 (July 2011): 237–51. http://dx.doi.org/10.1162/artl_a_00034.

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Online social networking communities may exhibit highly complex and adaptive collective behaviors. Since emotions play such an important role in human decision making, how online networks modulate human collective mood states has become a matter of considerable interest. In spite of the increasing societal importance of online social networks, it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that the general happiness, or subjective well-being (SWB), of Twitter users, as measured from a 6-month record of their individual tweets, is indeed assortative across the Twitter social network. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important factor in how positive and negative sentiments are maintained and spread through human society. Future research may focus on how event-specific mood states can propagate and influence user behavior in “real life.”

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32

Keijzer, Marijn A., Michael Mäs, and Andreas Flache. "Communication in Online Social Networks Fosters Cultural Isolation." Complexity 2018 (November 4, 2018): 1–18. http://dx.doi.org/10.1155/2018/9502872.

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Online social networks play an increasingly important role in communication between friends, colleagues, business partners, and family members. This development sparked public and scholarly debate about how these new platforms affect dynamics of cultural diversity. Formal models of cultural dissemination are powerful tools to study dynamics of cultural diversity but they are based on assumptions that represent traditional dyadic, face-to-face communication, rather than communication in online social networks. Unlike in models of face-to-face communication, where actors update their cultural traits after being influenced by one of their network contacts, communication in online social networks is often characterized by a one-to-many structure, in that users emit messages directly to a large number of network contacts. Using analytical tools and agent-based simulation, we show that this seemingly subtle difference can have profound implications for emergent dynamics of cultural dissemination. In particular, we show that within the framework of our model online communication fosters cultural diversity to a larger degree than offline communication and it increases chances that individuals and subgroups become culturally isolated from their network contacts.

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33

SONG, Yang. "From Offline Social Networks to Online Social Networks: Changes in Entrepreneurship." Informatica Economica 20, no. 2/2015 (June 30, 2015): 120–33. http://dx.doi.org/10.12948/issn14531305/19.2.2015.12.

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34

Erlandsson, Fredrik, Roozbeh Nia, Henric Johnson, and Felix S. Wu. "Making social interactions accessible in online social networks." Information Services & Use 33, no. 2 (October 30, 2013): 113–17. http://dx.doi.org/10.3233/isu-130702.

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35

Drushel, Bruce E. "HIV/AIDS, Social Capital, and Online Social Networks." Journal of Homosexuality 60, no. 8 (August 2013): 1230–49. http://dx.doi.org/10.1080/00918369.2013.784114.

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36

Tauginienė, Loreta, and Rima Kalinauskaitė. "Participation of doctoral students in online social networks." Studies in Graduate and Postdoctoral Education 9, no. 2 (November 16, 2018): 144–64. http://dx.doi.org/10.1108/sgpe-d-18-00002.

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Purpose This paper aims to examine the use of online social networks by doctoral students. Design/methodology/approach A quantitative online survey was conducted – 448 doctoral students from 15 universities and 11 research institutes in Lithuania were asked about their participation in both academic and non-academic online social networks. Findings The results show that despite efforts to link academics to society, doctoral students are not supported by universities/research institutes nor are doctoral students trained for this purpose, including regarding such threats as offensive posts. Additionally, more comprehensive information is disclosed in academic social networks, but these networks are less common and less frequently used. Research limitations/implications International doctoral students in Lithuania cover about 4.4 per cent of the total population of doctoral students. They were not invited to participate in the survey. Furthermore, doctoral students consider any online social network as their professional (academic) network, as was found from our results. This resulted in the confusion of our definition of academic online social networks. Practical implications Learning about the diverse online roles doctoral students may take could be facilitated were doctoral students to receive clear and consistent awareness-raising and develop self-awareness in the importance of the roles, the most central online social networks and potential threats, and related institutional support to address them. Originality/value This study provides results on how engagement of doctoral students in online social networks might affect their links with society and what academic institutions should promote in doctoral education.

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37

Li, Xuefeng, Yang Xin, Chensu Zhao, Yixian Yang, and Yuling Chen. "Graph Convolutional Networks for Privacy Metrics in Online Social Networks." Applied Sciences 10, no. 4 (February 15, 2020): 1327. http://dx.doi.org/10.3390/app10041327.

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In recent years, privacy leakage events in large-scale social networks have become increasingly frequent. Traditional methods relying on operators have been unable to effectively curb this problem. Researchers must turn their attention to the privacy protection of users themselves. Privacy metrics are undoubtedly the most effective method. However, social networks have a substantial number of users and a complex network structure and feature set. Previous studies either considered a single aspect or measured multiple aspects separately and then artificially integrated them. The measurement procedures are complex and cannot effectively be integrated. To solve the above problems, we first propose using a deep neural network to measure the privacy status of social network users. Through a graph convolution network, we can easily and efficiently combine the user features and graph structure, determine the hidden relationships between these features, and obtain more accurate privacy scores. Given the restriction of the deep learning framework, which requires a large number of labelled samples, we incorporate a few-shot learning method, which greatly reduces the dependence on labelled data and human intervention. Our method is applicable to online social networks, such as Sina Weibo, Twitter, and Facebook, that can extract profile information, graph structure information of users’ friends, and behavioural characteristics. The experiments show that our model can quickly and accurately obtain privacy scores in a whole network and eliminate traditional tedious numerical calculations and human intervention.

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38

Magelinski, Thomas, and Kathleen M. Carley. "Contextualizing Online Conversational Networks." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 590–601. http://dx.doi.org/10.1609/icwsm.v17i1.22171.

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Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 million tweets discussing the 2020 US election. We find that even filtered data contains many different conversational contexts, with users engaging in multiple conversations. While users engage in multiple conversations, the overlap between any two pairs of conversations tends to be only 30-40%, giving very different networks for different conversations. Even accounting for this variation, we show that the relative social status of users varies considerably across contexts, with tau=0.472 on average. Our findings imply that standard network analysis on social media data can be unreliable in the face of multiple conversational contexts.

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Lakon, Cynthia M., Yu Zheng, and Cornelia Pechmann. "Social network tie functions of social support and social influence and adult smoking abstinence." PLOS ONE 19, no. 3 (March 7, 2024): e0296458. http://dx.doi.org/10.1371/journal.pone.0296458.

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Adults’ social network ties serve multiple functions and play prominently in quitting smoking. We examined three types of adults’ egocentric social networks, including family, friends, and friends online to investigate how two network characteristics with major relevance to health behavior, network size and tie closeness, related to the emotional and confidant support and to pro- and anti-smoking social influence these ties may transmit. We also examine whether the social support and social influence constructs related to smoking abstinence. We utilized baseline and 7-day abstinence survey data from 123 adult current smokers attempting to quit prior to the start of a randomized controlled quit-smoking trial of a social support intervention for quitting smoking on Twitter. To examine study relationships, we estimated Negative Binomial Regression models and Logistic Regression models. For all networks, network size and tie closeness related positively to most of the social support and social influence constructs, with tie closeness related most strongly, especially for online friends. Family pro-smoking social influence related negatively to smoking abstinence, and there were marginally negative relationships for family emotional support and family confidant support. Online friend emotional support had a marginally positive relationship with smoking abstinence. Overall, our findings indicated the importance of the social support and social influence functions of each type of network tie, with larger networks and closer ties related to higher levels of social support and social influence. Moreover, family network pro-smoking social influence may compromise abstinence while emotional support from online friend network ties may reinforce it.

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40

Tan, Leonard, Thuan Pham, Kei Ho Hang, and Seng Kok Tan. "Event Prediction in Online Social Networks." Journal of Data Intelligence 2, no. 1 (March 2021): 64–94. http://dx.doi.org/10.26421/jdi2.1-4.

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Event prediction is a very important task in numerous applications of interest like fintech, medical, security, etc. However, event prediction is a highly complex task because it is challenging to classify, contains temporally changing themes of discussion and heavy topic drifts. In this research, we present a novel approach which leverages on the RFT framework developed in \cite{tan2020discovering}. This study addresses the challenge of accurately representing relational features in observed complex social communication behavior for the event prediction task; which recent graph learning methodologies are struggling with. The concept here, is to firstly learn the turbulent patterns of relational state transitions between actors preceeding an event and then secondly, to evolve these profiles temporally, in the event prediction process. The event prediction model which leverages on the RFT framework discovers, identifies and adaptively ranks relational turbulence as likelihood predictions of event occurrences. Extensive experiments on large-scale social datasets across important indicator tests for validation, show that the RFT framework performs comparably better by more than 10\% to HPM \cite{amodeo2011hybrid} and other state-of-the-art baselines in event prediction.

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41

Chung, Jin Young, and Dimitrios Buhalis. "Information Needs in Online Social Networks." Information Technology & Tourism 10, no. 4 (December 1, 2008): 267–81. http://dx.doi.org/10.3727/109830508788403123.

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42

Merchant, Guy. "Identity, Social Networks and Online Communication." E-Learning and Digital Media 3, no. 2 (June 2006): 235–44. http://dx.doi.org/10.2304/elea.2006.3.2.235.

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43

Ananthula, Swathi, Omar Abuzaghleh, Navya Bharathi Alla, Swetha Prabha Chaganti, Pragna chowdary kaja, and Deepthi Mogilineedi. "Measuring Privacy in Online Social Networks." International Journal of Security, Privacy and Trust Management 4, no. 2 (May 30, 2015): 01–09. http://dx.doi.org/10.5121/ijsptm.2015.4201.

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44

., C. Kavitha, and Nageswararao Sirisala. "Trust Computation in Online Social Networks." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 174–78. http://dx.doi.org/10.26438/ijcse/v7i4.174178.

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45

Marturano, Antonio. "The Ethics of Online Social Networks." International Review of Information Ethics 16 (December 1, 2011): 3–5. http://dx.doi.org/10.29173/irie209.

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46

Halevy, Alon, Cristian Canton-Ferrer, Hao Ma, Umut Ozertem, Patrick Pantel, Marzieh Saeidi, Fabrizio Silvestri, and Ves Stoyanov. "Preserving integrity in online social networks." Communications of the ACM 65, no. 2 (February 2022): 92–98. http://dx.doi.org/10.1145/3462671.

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Zhang, Yifeng, Xiaoqing Li, and Te-Wei Wang. "Identifying Influencers in Online Social Networks." International Journal of Intelligent Information Technologies 9, no. 1 (January 2013): 1–20. http://dx.doi.org/10.4018/jiit.2013010101.

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Online social networks (OSNs) are quickly becoming a key component of the Internet. With their widespread acceptance among the general public and the tremendous amount time that users spend on them, OSNs provide great potentials for marketing, especially viral marketing, in which marketing messages are spread among consumers via the word-of-mouth process. A critical task in viral marketing is influencer identification, i.e. finding a group of consumers as the initial receivers of a marketing message. Using agent-based modeling, this paper examines the effectiveness of tie strength as a criterion for influencer identification on OSNs. Results show that identifying influencers by the number of strong connections that a user has is superior to doing so by the total number of connections when the strength of strong connections is relatively high compared to that of weak connections or there is a relatively high percentage of strong connections between users. Implications of the results are discussed.

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Franchi, Enrico, Agostino Poggi, and Michele Tomaiuolo. "Information Attacks on Online Social Networks." Journal of Information Technology Research 7, no. 3 (July 2014): 54–71. http://dx.doi.org/10.4018/jitr.2014070104.

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Online social networks have changed the way people interact, allowing them to stay in touch with their acquaintances, reconnect with old friends, and establish new relationships with other people based on hobbies, interests, and friendship circles. Unfortunately, the regrettable concurrence of the users' carefree attitude in sharing information, the often sub-par security measures from the part of the system operators and, eventually, the high value of the published information make online social networks an interesting target for crackers and scammers alike. The information contained can be used to trigger attacks to even more sensible targets and the ultimate goal of sociability shared by the users allows sophisticated forms of social engineering inside the system. This work reviews some typical social attacks that are conducted on social networking systems, carrying real-world examples of such violations and analysing in particular the weakness of password mechanisms. It then presents some solutions that could improve the overall security of the systems.

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Srivastava, Agrima, and G. Geethakumari. "Privacy landscape in online social networks." International Journal of Trust Management in Computing and Communications 3, no. 1 (2015): 19. http://dx.doi.org/10.1504/ijtmcc.2015.072461.

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Li, Nan, and Guanling Chen. "Sharing location in online social networks." IEEE Network 24, no. 5 (September 2010): 20–25. http://dx.doi.org/10.1109/mnet.2010.5578914.

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