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_21 have used the Partner Cyber Abuse Questionnaire and found a prevalence of 40 @cite_31 found that the reasons for ending friend relations are similar in the real and online worlds, and conjectured that tools can be built to monitor online relations. To detect cyberbullying, @cite_2 used datasets of manually annotated comments, NLP features, supervised learning, and reasoning technique, then proposed several intervention designs. @cite_37 developed and conducted participatory design sessions with teenage participants to design, improve, and evaluate prototypes that address cyberbullying scenarios. @cite_25 introduced an automated Twitter assistant that identifies text and visual bias, aggregates and presents evidence of bias to users, and enable activists to inform the public of bias, through bots.
@cite_37 @cite_21 @cite_2 @cite_31 @cite_25
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While social media platforms enable individuals to easily communicate and share experiences, they have also emerged as a tool for cyberbullying. Teenagers represent an especially vulnerable population for negative emotional responses to cyberbullying. At the same time, attempts to mitigate or prevent cyberbullying from occurring in these networked spaces have largely failed because of the complexity and nuance with which young people bully others online. To address challenges related to designing for cyberbullying intervention and mitigation, we detail findings from participatory design work with two groups of high school students in spring 2015. Over the course of five design sessions spanning five weeks, participants shared their experiences with cyberbullying and iteratively designed potential solutions. We provide an in-depth discussion of the range of cyberbullying mitigation solutions participants designed. We focus on challenges participants' identified in designing for cyberbullying support and prevention and present a set of five potential cyberbullying mitigation solutions based on the results of the design sessions. Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies. Recent analyses of self-reported data (mainly survey data) seem to suggest that social rules for ending relationships are transformed on Facebook. There seem to be a radical difference between offline and online worlds: reasons for ending online relationships are different than those for ending offline ones. These preliminary findings are, however, not supported by any quantitative evidence, and that is why we put them to test. We consider a variety of factors (e.g., age, gender, personality traits) that studies in sociology have found to be associated with friendship dissolution in the real world and study whether these factors are still important in the context of Facebook. Upon analyzing 34,012 Facebook relationships, we found that, on average, a relationship is more likely to break if it is not embedded in the same social circle, if it is between two people whose ages differ, and if one of the two is neurotic or introvert. Interestingly, we also found that a relationship with a common female friend is more robust than that with a common male friend. These findings are in line with previous analyses of another popular social-networking platform, that of Twitter. All this goes to suggest that there is not much difference between offline and online worlds and, given this predictability, one could easily build tools for monitoring online relations. Bias is a common problem in today's media, appearing frequently in text and in visual imagery. Users on social media websites such as Twitter need better methods for identifying bias. Additionally, activists --those who are motivated to effect change related to some topic, need better methods to identify and counteract bias that is contrary to their mission. With both of these use cases in mind, in this paper we propose a novel tool called UnbiasedCrowd that supports identification of, and action on bias in visual news media. In particular, it addresses the following key challenges (1) identification of bias; (2) aggregation and presentation of evidence to users; (3) enabling activists to inform the public of bias and take action by engaging people in conversation with bots. We describe a preliminary study on the Twitter platform that explores the impressions that activists had of our tool, and how people reacted and engaged with online bots that exposed visual bias. We conclude by discussing design and implication of our findings for creating future systems to identify and counteract the effects of news bias.