Online Social Networks (OSNs) witness a rise in user activity whenever an event takes place. Malicious entities exploit this spur in user-engagement levels to spread malicious content that compromises system reputation and degrades user experience. It also generates revenue from advertisements, clicks, etc. for the malicious entities. Facebook, the world's biggest social network, is no exception and has recently been reported to face much abuse through scams and other type of malicious content, especially during news making events. Recent studies have reported that spammers earn $200 million just by posting malicious links on Facebook. In this paper, we characterize malicious content posted on Facebook during 17 events, and discover that existing efforts to counter malicious content by Facebook are not able to stop all malicious content from entering the social graph. Our findings revealed that malicious entities tend to post content through web and third party applications while legitimate entities prefer mobile platforms to post content. In addition, we discovered a substantial amount of malicious content generated by Facebook pages. Through our observations, we propose an extensive feature set based on entity profile, textual content, metadata, and URL features to identify malicious content on Facebook in real time and at zero-hour. This feature set was used to train multiple machine learning models and achieved an accuracy of 86.9 . The intent is to catch malicious content that is currently evading Facebook's detection techniques. Our machine learning model was able to detect more than double the number of malicious posts as compared to existing malicious content detection techniques. Finally, we built a real world solution in the form of a REST based API and a browser plug-in to identify malicious Facebook posts in real time.
Multiple machine learning based techniques have been proposed in the past to detect malicious content on other social networks such as Twitter and YouTube @cite_4 @cite_16 @cite_6 @cite_13 . The efficiency of such techniques comes from features like age of the account, number of social connections, past messages of the user, etc. @cite_4 . However, none of these features are available on Facebook publicly. Other techniques make use of OSN specific features like user replies, user mentions, retweets (Twitter) @cite_16 , post views and ratings (YouTube) @cite_20 to identify malicious content, which cannot be ported to Facebook. Blacklists have been shown to be highly ineffective initially, capturing less than 20
@cite_4 @cite_6 @cite_16 @cite_13 @cite_20
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An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Doxing is online abuse where a malicious party harms another by releasing identifying or sensitive information. Motivations for doxing include personal, competitive, and political reasons, and web users of all ages, genders and internet experience have been targeted. Existing research on doxing is primarily qualitative. This work improves our understanding of doxing by being the first to take a quantitative approach. We do so by designing and deploying a tool which can detect dox files and measure the frequency, content, targets, and effects of doxing on popular dox-posting sites. This work analyzes over 1.7 million text files posted to paste-bin.com, 4chan.org and 8ch.net, sites frequently used to share doxes online, over a combined period of approximately thirteen weeks. Notable findings in this work include that approximately 0.3 of shared files are doxes, that online social networking accounts mentioned in these dox files are more likely to close than typical accounts, that justice and revenge are the most often cited motivations for doxing, and that dox files target males more frequently than females. We also find that recent anti-abuse efforts by social networks have reduced how frequently these doxing victims closed or restricted their accounts after being attacked. We also propose mitigation steps, such a service that can inform people when their accounts have been shared in a dox file, or law enforcement notification tools to inform authorities when individuals are at heightened risk of abuse.