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Online social networks are designed to encourage disclosure while also having the ability to disrupt existing privacy boundaries. This study assesses those individuals who are the most active online: "Digital Natives." The specific focus includes participants' privacy beliefs; how valuable they believe their personal, private information to be; and what risks they perceive in terms of disclosing this information in a fairly anonymous online setting. A model incorporating these concepts was tested in the context of communication privacy management theory. Study findings suggest that attitudinal measures were stronger predictors of privacy behaviors than were social locators. In particular, support was found for a model positing that if an individual placed a higher premium on their personal, private information, they would then be less inclined to disclose such information while visiting online social networking sites.

We use a large quota-sampled online survey and data on Facebook connections among survey respondents in six successor states of former Yugoslavia to demonstrate that, even more than two decades after the violence had ended, online social connections in this region are substantially related to people's war experiences of combat, victimhood, and forced migration, as well as to their views of the wars' causes, conduct, and consequences. What is particularly important, the sizes of the effects of these war-related factors on respondents' online social networks are substantively large and comparable to those of gender, ethnicity, education, or political ideology. Our findings are an important contribution to the understanding of the deeply pervasive and long-lasting effects of wars on societies. They also highlight the enduring relevance of wartime violence in postwar social networks that is likely to affect efforts at enduring conflict resolution and reconciliation.

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.

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.

Online Social Networks (OSNs) are computer-based technologies that enable users to create content, share information, and establish social relationships in online platforms. The advent of OSNs has dramatically revolutionized the way we access the news, share opinion, make business and politics. Although the wide adoption of OSNs brought several positive effects, the combination of its technological and social aspects hides harmful effects for both the individual users and the entire society. Among the potential risks analyzed in the literature (e.g., security, health, etc.), in this thesis, we analyze the perils related to the privacy leakage and the manipulation of opinions in OSNs. In particular, we investigate the factors driving these perils, with the final objective of raising users' awareness of the risks behind their online activities. We show how, for both the privacy and manipulation perils, social connections play a central role in fostering and exacerbating such issues. In fact, social connections among OSN users result in a network structure, which enables the spreading of information, behaviours, and opinions across the OSN population through online interactions. Along this research direction, we first explore to what extent an individual's privacy can be violated by leveraging information provided by other users in the OSN. In particular, we examine the problem of location privacy by developing methods to assess users' privacy risks and strategies to control the public exposure of their data. Then, we explore the privacy peril by considering the diffusion of behaviours and opinions in OSNs. In fact, social interactions can substantially affect the extent to which a behaviour, an opinion, or a product is adopted by OSN users. This concept is a social phenomenon referred to as social influence. According to this concept, we investigate whether social influence modelling (i.e., learning influence strengths among subjects) can be used to accurately predict users' future activity and, in turn, violate their privacy. We present different approaches to model social influence and we show how such models can be employed to violate users' privacy. Online interactions and social influence play also a crucial role in the manipulation of peoples' belief and opinion. Manipulation campaigns have raised particular concerns in the political context. Bots (i.e., software-controlled accounts) and trolls (i.e., state-sponsored human operators) are the main actors responsible for these campaigns. In this thesis, we analyze the activity of such malicious actors to enhance and enable countermeasures for their detection. More specifically, we first uncover the strategies employed by bots to avoid detection and manipulate human users. Then, we present an approach for detecting trolls' activity in OSNs that accurately identifies troll accounts and unveils their distinguishing behaviour with respect to regular users. The results presented in this thesis confirm the privacy and manipulation risks in OSNs: On one hand, we prove that users' privacy is not under individual control as public information can be efficiently used to predict their behaviour, and in turn, violate their privacy. On the other hand, we show that malicious actors have become increasingly sophisticated to escape detection andmanipulate human users. However, the majority of OSN users are not conscious or underestimate the potential risks behind their online activity. Towards raising users' awareness of such perils and to mitigate this set of open problems, we propose an awareness service, based on a mobile application, to timely communicate users their current risks in OSNs. For this purpose, we deploy a framework to collect users' data in a privacy-preserving way and provide them with feedback about their privacy and manipulation risks in real-time.

This study argues that Facebook only generates bridging social capital through driving people to offline events. Other indicators on Facebook such as Facebook friends or Facebook group membership do not appear associated with social capital. Beyond that, political positions posted on Facebook appear to be reasonably accurate but influenced by what the user's Facebook friends have on their profiles.

With the rapid development of information technologies, security violations in online social networks (OSN) have emerged as a critical issue. Traditional technical and organizational approaches do not consider economic factors, which are increasingly important to sustain information security investment. In this paper, we develop an evolutionary game model to study the sustainability of information security investment in OSN, and propose a quantitative approach to analyze and optimize security investment. Additionally, we examine a contract with an incentive mechanism to eliminate free riding, which helps sustain the security investment. Numerical examples are provided for illustration and simulation purposes, leading to several countermeasures and suggestions. Our analytical results show that the optimal strategy of information security investment not only is correlated with profit growth coefficients and investment costs, but is also influenced significantly by the profits from free riding. If the profit growth coefficients are prohibitively small, both OSN service providers and online platforms will not choose to sustain investment based on small profits. As profit growth coefficients increase, there is a higher probability that game players will invest. Another major finding is that the (Invest, Invest) profile is much less sensitive to the change of profit growth coefficients and the convergent speed of this scenario is faster than the other profiles. The government agency can use the proposed model to determine a proper incentive or penalty to help both parties reach the optimal strategies and thus improve OSN security.

COVID-19 affected the entire world due to the unavailability of the vaccine. The social distancing was a contributing factor that gave rise to the usage of Online Social Networks. It has been seen that people share the information that comes to them without verifying its source . One of the common forms of information that is disseminated that have a radical purpose is propaganda. Propaganda is organized and conscious method of molding conclusions and impacting an individual's contemplations to accomplish the ideal aim of proselytizer. For this paper, different propagandistic tweets were shared in the COVID-19 Era. Data regarding COVID-19 propaganda was extracted from Twitter. Labelling of data was performed manually using different propaganda identification techniques and Hybrid feature engineering was used to select the essential features. Ensemble machine learning classifiers were used for performing the binary classification. Adaboost shows an accuracy of 98.7%, which learns from a weak learning algorithm by updating the weights.