Adversaries leverage social network friend relationships to collect sensitive data from users and target them with abuse that includes fake news, cyberbullying, malware, and propaganda. Case in point, 71 out of 80 user study participants had at least 1 Facebook friend with whom they never interact, either in Facebook or in real life, or whom they believe is likely to abuse their posted photos or status updates, or post offensive, false or malicious content. We introduce AbuSniff, a system that identifies Facebook friends perceived as strangers or abusive, and protects the user by unfriending, unfollowing, or restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse.We introduce mutual Facebook activity features and show that they can train supervised learning algorithms to predict questionnaire responses. We have evaluated AbuSniff through several user studies with a total of 263 participants from 25 countries. After answering the questionnaire, participants agreed to unfollow and restrict abusers in 91.6 and 90.9 of the cases respectively, and sandbox or unfriend non-abusive strangers in 92.45 of the cases. Without answering the questionnaire, participants agreed to take the AbuSniff suggested action against friends predicted to be strangers or abusive, in 78.2 of the cases. AbuSniff increased the participant self-reported willingness to reject invitations from strangers and abusers, their awareness of friend abuse implications and their perceived protection from friend abuse.
@cite_27 found that in Twitter, Korean users tended to unfollow people who posted many tweets per time unit, about uninteresting topics, including details of their lives. @cite_12 found that unfollow tends to be reciprocal. This relatively harmless tit-for-tat behavior may explain the willingness of participants in our studies to unfollow abusive friends. @cite_9 found that users who receive acknowledgments from others are less likely to unfollow them. Future work may compare the willingness of a user to unfollow Facebook friends who posted general abuse vs. abuse personally targeted to the user.
@cite_9 @cite_27 @cite_12
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We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results. They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter. This study investigates unfollow behavior in Twitter, i.e. people removing others from their Twitter following lists. Considering the interdependency and dynamics of unfollow decisions, we use actor-oriented modeling (SIENA) to examine the impacts of reciprocity, status, embeddedness, homophily, and informativeness on tie dissolution. Focusing on ordinary users in tightly-knitted user groups, the results show that relational properties play key roles in the emergence of unfollow behavior: mutual following relations and common followees reduce the likelihood of unfollowing. And unfollow tends to be reciprocal: when a user is unfollowed by someone, he or she will unfollow back. However, there is no evidence of the impacts of homophily based on common interests and informativeness of interactions. The findings suggest that Twitter has many heterogeneous user groups and relational and informational factors may not be applicable universally. Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45 improvement in AUC, depending on the dataset). Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.