Knowledge through social networks: accuracy, error, and polarisation

This paper examines the fundamental problem of testimony. Much of what we believe to know we know in good part, or even entirely, through the testimony of others. The problem with testimony is that we often have very little on which to base estimates of the accuracy of our sources. Simulations with otherwise optimal agents examine the impact of this for the accuracy of our beliefs about the world. It is demonstrated both where social networks of information dissemination help and where they hinder. Most importantly, it is shown that both social networks and a common strategy for gauging the accuracy of our sources give rise to polarisation even for entirely accuracy motivated agents. Crucially these two factors interact, amplifying one another’s negative consequences, and this side effect of communication in a social network increases with network size. This suggests a new causal mechanism by which social media may have fostered the increase in polarisation currently observed in many parts of the world

Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey

We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building a trustworthy SNSs. In this paper, we conducted an extensive survey, covering (i) the multidisciplinary concepts of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons from existing literature. We conclude this survey paper with an in-depth discussions on the limitations of the state-of-the-art and recommend future research directions in this area.Comment: 35 pages, 8 figures, submitted to ACM Computing Survey

Impact of location on social media credibility

Social media platforms such as Twitter and Facebook allow users from all over the world to contribute content. However, these users publish content without peer review, and contributions of low quality can create credibility concerns. This reduces the potential social benefits of social media. Social media credibility models rely on popularity, temporal patterns and other collective behaviour of users to study the credibility of user generated content (UGC). However, such approaches do not take into account end user credibility perceptions and factors that may influence a contributor (author), which in turn affects credibility models in social media. Therefore, I studied the factors that influence readers' credibility perception and content credibility. I identified a number of limitations in existing models: most research considers only users' perceptions from one country or culture and then generalises the results to others. I also found these models do not consider author location when assessing credibility. Therefore, I proposed a study on the influence of author, reader and event location on user credibility perception and content credibility in social media. I propose a model that has been validated using a crowdsourced labelling approach. I ran three controlled experiments mainly varying source-based features (author) and content (text). Further, I applied a linguistic analysis approach to validate the influence of location on content credibility. I also applied a number of statistical analyses to measure the effect of all features. I validated the model using a common social media platform (Twitter) and showed the influence of non-textual features on credibility judgments of readers. Also, I found that reader location represented by culture can determine their credibility perception in social media. Moreover, I showed how distance between the event and author location can affect sources and credibility distribution in social media. Location of readers and authors, and the interaction with event locations can be used to improve assessment of credibility in social media. Reader characteristics are found to be important when studying credibility in social media as they can be used to improve user experience in social media. Moreover, an author's location can enhance credibility detection models to assess content accurately as it can differentiate between content with different credibility levels. While I do not claim that only user location can be used to build a standalone credibility system, I conclude that adding geographic location and culture of users can improve the performance of existing credibility models significantly