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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.

Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down rapidly. Using social network datasets we show the main factors leading to such a dramatic collapse. At early stage mostly the loosely bound users disappear, later collective effects play the main role leading to cascading failures. We present a theory based on a generalised threshold model to explain the findings and show how the collapse time can be estimated in advance using the dynamics of the churning users. Our results shed light to possible mechanisms of instabilities in other competing social processes.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

New technologies in communications and networking have shaped the way political movements can be mobilised and coordinated in important ways. Recent uprisings have shown dramatically how a people can communicate its cause effectively beyond borders, through online social networking channels and mobile phone technologies. Hannah Arendt, as an eminent scholar of power and politics in the modern era, offers a relevant lens with which to theoretically examine the implications and uses of online social networks and their impact on politics as praxis. This article creates an account of how Arendt might have evaluated virtual social networks in the context of their potency to create power, spaces and possibilities for political action. With an Arendtian lens the article examines whether these virtual means of 'shared appearances' facilitate or frustrate efforts in the formation of political power and the creation of new beginnings. Based on a contemporary reading of her writings, the article concludes that Arendt's own assessment of online social networks, as spheres for political action, would likely have been very critical. ; Peer-reviewed ; Post-print

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

Intro -- Acknowledgments -- Introduction -- Chapter Overview -- Terminology and Definitions -- Attributes -- Privacy Settings of Attributes -- Risk Sources -- Data Inference -- Threat -- Privacy Harm -- Privacy Risks -- Privacy Risk Analysis -- Dimensions of Privacy Scoring in OSNs -- Type of Data -- Assumptions About the User -- Privacy Settings -- Risk Sources -- Privacy Metrics -- Sensitivity -- Visibility -- Reachability -- Data Inference -- Suggestion of Countermeasures -- Attribute Visibility in OSN -- Visibility Matrix -- Construction of Visibility Matrix -- An Illustration -- Open Problems -- Harm Trees for OSNs -- Harm Trees -- Construction of Harm trees -- Harm Likelihood -- Harm Expressions -- Harm Database -- Open Problems -- Privacy Risk Analysis in OSNs -- An Overview -- PrivOSN in Details -- Computation of Accuracy -- Evaluation of Harm Likelihoods -- Computation Profile Similarity -- Presentation of Privacy Risk to the User -- Residual Risks -- Open Problems -- Social Benefits -- Social Ties -- Social Capital -- Understanding Social Benefits -- Social Benefit Criteria -- Attributes and Social Benefit Criteria -- Evaluation of Social Benefit -- Open Problems -- Choosing the Right Privacy Settings -- Privacy and Social Benefit Tradeoff in Privacy Management -- An Integer Programming Model -- Balancing Privacy Risks and Social Benefits -- Formulation of the IP Problem -- Objective Function -- Privacy Risk Constraints -- Social Benefit Constraints -- Privacy Setting Constraints -- Open Problems -- Conclusion -- Notations and Their Meanings -- Comparison of Privacy Scoring Mechanisms -- Cases 3 and 4 -- Bibliography -- Authors' Biographies -- Blank Page.

New technologies in communications and networking have shaped the way political movements can be mobilised and coordinated in important ways. Recent uprisings have shown dramatically how a people can communicate its cause effectively beyond borders, through online social networking channels and mobile phone technologies. Hannah Arendt, as an eminent scholar of power and politics in the modern era, offers a relevant lens with which to theoretically examine the implications and uses of online social networks and their impact on politics as praxis. This article creates an account of how Arendt might have evaluated virtual social networks in the context of their potency to create power, spaces and possibilities for political action. With an Arendtian lens the article examines whether these virtual means of 'shared appearances' facilitate or frustrate efforts in the formation of political power and the creation of new beginnings. Based on a contemporary reading of her writings, the article concludes that Arendt's own assessment of online social networks, as spheres for political action, would likely have been very critical.

Nowadays the communication between people is highly influenced by the Online Social Networks (OSNs). Immense number of personal, professional and political thoughts are shared online every single day. Thus, OSNs are attractive for cyber criminals who are trying to exploit their weaknesses and vulnerabilities. Fake accounts on OSNs have become a basic threat used in different online attacks. And even if some of these attacks are harmless like generating fake accounts for "likes" on Facebook, followers on Twitter and views on YouTube, other attacks are more serious and can be dangerous online. Influence on trending topics, spread spam advertisements and false political content are just some of the examples how attackers are able to wreak havoc online by using fake profiles. With the increasing number of security and privacy threats, some of the OSNs have adopted security measures to stop the mass creation of the fake accounts. However, those measures are often ineffective by the many tools available on the underground marketplaces that allow people to cheaply acquire fake accounts. In this regard, this thesis aims to detect the fake profiles on a very popular OSN, Twitter, with the help of machine learning algorithms. The first key contribution is the research on the appropriate machine learning techniques. Support Vector Machine, Random Forest and k-Nearest Neighbour were the three supervised learning algorithms. In addition to them, one clustering algorithm was tested, namely k-Means. The next contribution is the acquisition of labelled data related to real and fake profiles in Twitter for the training phase. After analysing the behaviour of the users and their tweets activities, an extensive dataset of 12 features was created. The named Fake profile´s detection dataset plays key role in distinguishing fake accounts among real ones and it is applied on the machine learning algorithms. Analysis of the results has been performed in five different scenarios. The classifiers achieve accuracy score of around 92% for separating the fake profiles from the real ones and the clustering algorithm is able to detect all fake profiles. Finally, for testing purposes were analysed some of the followers of Donald Trump with the already trained models.