Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.
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Today, social networking has considerably changed why people are taking pictures all the time everywhere they go. More than 500 million photos are uploaded and shared every day, along with more than 200 hours of videos every minute. More particularly, with the ubiquity of smartphones, social network users are now taking photos of events in their lives, travels, experiences, etc. and instantly uploading them online. Such public data sharing puts at risk the users' privacy and expose them to a surveillance that is growing at a very rapid rate. Furthermore, new techniques are used today to extract publicly shared data and combine it with other data in ways never before thought possible. However, social networks users do not realize the wealth of information gathered from image data and which could be used to track all their activities at every moment (e.g., the case of cyberstalking). Therefore, in many situations (such as politics, fraud fighting and cultural critics, etc.), it becomes extremely hard to maintain individuals' anonymity when the authors of the published data need to remain anonymous.Thus, the aim of this work is to provide a privacy-preserving constraint (de-linkability) to bound the amount of information that can be used to re-identify individuals using online profile information. Firstly, we provide a framework able to quantify the re-identification threat and sanitize multimedia documents to be published and shared. Secondly, we propose a new approach to enrich the profile information of the individuals to protect. Therefore, we exploit personal events in the individuals' own posts as well as those shared by their friends/contacts. Specifically, our approach is able to detect and link users' elementary events using photos (and related metadata) shared within their online social networks. A prototype has been implemented and several experiments have been conducted in this work to validate our different contributions. ; De nos jours, les réseaux sociaux ont considérablement changé la façon dont les personnes prennent des photos qu'importe le lieu, le moment, le contexte. Plus que 500 millions de photos sont partagées chaque jour sur les réseaux sociaux, auxquelles on peut ajouter les 200 millions de vidéos échangées en ligne chaque minute. Plus particulièrement, avec la démocratisation des smartphones, les utilisateurs de réseaux sociaux partagent instantanément les photos qu'ils prennent lors des divers événements de leur vie, leurs voyages, leurs aventures, etc. Partager ce type de données présente un danger pour la vie privée des utilisateurs et les expose ensuite à une surveillance grandissante. Ajouté à cela, aujourd'hui de nouvelles techniques permettent de combiner les données provenant de plusieurs sources entre elles de façon jamais possible auparavant. Cependant, la plupart des utilisateurs des réseaux sociaux ne se rendent même pas compte de la quantité incroyable de données très personnelles que les photos peuvent renfermer sur eux et sur leurs activités (par exemple, le cas du cyberharcèlement). Cela peut encore rendre plus difficile la possibilité de garder l'anonymat sur Internet dans de nombreuses situations où une certaine discrétion est essentielle (politique, lutte contre la fraude, critiques diverses, etc.).Ainsi, le but de ce travail est de fournir une mesure de protection de la vie privée, visant à identifier la quantité d'information qui permettrait de ré-identifier une personne en utilisant ses informations personnelles accessibles en ligne. Premièrement, nous fournissons un framework capable de mesurer le risque éventuel de ré-identification des personnes et d'assainir les documents multimédias destinés à être publiés et partagés. Deuxièmement, nous proposons une nouvelle approche pour enrichir le profil de l'utilisateur dont on souhaite préserver l'anonymat. Pour cela, nous exploitons les évènements personnels à partir des publications des utilisateurs et celles partagées par leurs contacts sur leur réseau social. Plus précisément, notre approche permet de détecter et lier les évènements élémentaires des personnes en utilisant les photos (et leurs métadonnées) partagées au sein de leur réseau social. Nous décrivons les expérimentations que nous avons menées sur des jeux de données réelles et synthétiques. Les résultats montrent l'efficacité de nos différentes contributions.
International audience ; Dysfunctions in online social networks (e.g., echo chambers or filter bubbles) are studied by characterizing the opinion of users, for example, as Democrat-or Republican-leaning, or in continuous scales ranging from most liberal to most conservative. Recent studies have stressed the need for studying these phenomena in complex social networks in additional dimensions of social cleavage, including anti-elite polarization and attitudes towards changing cultural issues. The study of social networks in high-dimensional opinion spaces remains challenging in settings such as that of the US, both because of the dominance of a principal liberal-conservative cleavage, and because two-party political systems structure preferences of users and the tools to measure them. This article builds on embedding of social graphs in multi-dimensional ideological spaces and NLP methods to identify additional cleavage dimensions linked to cultural, policy, social, and ideological groups and preferences. Using Twitter social graph data I infer the political stance of nearly 2 million users connected to the political debate in the US for several issue dimensions of public debate. The proposed method shows that it is possible to identify several dimensions structuring social graphs, non-aligned to liberal-conservative divides and related to new emergent social cleavages. These results also shed a new light on ideological scaling methods gaining attention in many disciplines, allowing to identify and test the nature of spatial dimensions mined on social graphs.
International audience ; Dysfunctions in online social networks (e.g., echo chambers or filter bubbles) are studied by characterizing the opinion of users, for example, as Democrat-or Republican-leaning, or in continuous scales ranging from most liberal to most conservative. Recent studies have stressed the need for studying these phenomena in complex social networks in additional dimensions of social cleavage, including anti-elite polarization and attitudes towards changing cultural issues. The study of social networks in high-dimensional opinion spaces remains challenging in settings such as that of the US, both because of the dominance of a principal liberal-conservative cleavage, and because two-party political systems structure preferences of users and the tools to measure them. This article builds on embedding of social graphs in multi-dimensional ideological spaces and NLP methods to identify additional cleavage dimensions linked to cultural, policy, social, and ideological groups and preferences. Using Twitter social graph data I infer the political stance of nearly 2 million users connected to the political debate in the US for several issue dimensions of public debate. The proposed method shows that it is possible to identify several dimensions structuring social graphs, non-aligned to liberal-conservative divides and related to new emergent social cleavages. These results also shed a new light on ideological scaling methods gaining attention in many disciplines, allowing to identify and test the nature of spatial dimensions mined on social graphs.
To answer questions about the origins and outcomes of collective action, political scientists increasingly turn to datasets with social network information culled from online sources. However, a fundamental question of external validity remains untested: are the relationships measured between a person and her online peers informative of the kind of offline, "real-world" relationships to which network theories typically speak? This article offers the first direct comparison of the nature and consequences of online and offline social ties, using data collected via a novel network elicitation technique in an experimental setting. We document strong, robust similarity between online and offline relationships. This parity is not driven by sharedidentityof online and offline ties, but a shared nature of relationships in both domains. Our results affirm that online social tie data offer great promise for testing long-standing theories in the social sciences about the role of social networks.
Social media and our political and economic lives -- Social media and social justice in the digital age -- Social media power in #Ferguson -- Affected and effective: @Blacklivesmattercincy -- Political discourse on social media, twitter trolls and hashtag hijacking -- Election 2016: trolling in the twittersphere and gaming the system -- Fake news, bots and doublespeak -- The political economy of social media networks, social justice, and truth -- Social justice, national cultural politics, and the summer of 2020 -- Conclusions: the political economy of social media and social justice.
Intro -- Contents -- Preface -- Chapter 1 -- Influencers and Activists: Political Performances in an Increasingly Online World -- Abstract -- Introduction -- Instagram -- Digital Activism -- Political Performance -- Platforms and Politics -- The Political Potential of Instagram -- Influencers and Activism -- Methods -- Data -- A Note on Ethics -- Case 1: The Women's March -- Case 2: The Reproductive Justice Movement -- Content Analyses with an Exploratory Sample -- Quantitative Analysis for Case 1 -- Findings -- Currating Activism -- Case 1: Doing the Unexpected -- Case 2: Are Organizations Influencers? -- Discussion -- Conclusion -- References -- Biographical Sketch -- Chapter 2 -- Social Comparison on Facebook and Its Effect on an Individual's Well-Being -- Abstract -- Introduction -- The Social Comparison Theory -- Upward and Downward Comparison -- New Perspectives on Social Comparison -- Social Comparison on Social Network Sites -- Social Comparison and the Way of Use SNSs -- Personality and Social Comparison Processes on Facebook -- Consequences of Online Social Comparison -- Conclusion -- References -- Biographical Sketches -- Chapter 3 -- The Influence of Instagram upon Millennials' Purchase Intention: Celebrity Endorsement and Image Posts -- Abstract -- Introduction -- Literature Review -- Instagram -- Millennials -- User Generated Content (UGC) -- Visual Cues - Colour -- Celebrity Endorsement -- Endorser Attractiveness -- Visual Attractiveness -- Purchase Intention -- Methodology -- Discussion -- Conclusion -- References -- Chapter 4 -- Social Semiotic Aspect of Instagram Social Networks -- Abstract -- Introduction -- Instagram Communication Model -- The Typical Semiotic Communication Model -- Instagram's Semiotic Communication Model -- Instagram as Visual/Pictorial Social Network -- Social Semiotic Model of Instagram Environment.
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Online and Social Networking Communities is a professional guide written for educational practitioners and trainers who wish to use online communication tools effectively in their teaching. Focusing on the student experience of learning in online communities, it addresses 'web 2.0' and other 'social software' tools and considers the role these technologies play in supporting student learning and building learning communities.
