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Twitter is the largest and most popular micro-blogging website on Internet. Due to low publication barrier, anonymity and wide penetration, Twitter has become an easy target or platform for extremists to disseminate their ideologies and opinions by posting hate and extremism promoting tweets. Millions of tweets are posted on Twitter everyday and it is practically impossible for Twitter moderators or an intelligence and security analyst to manually identify such tweets, users and communities. However, automatic classification of tweets into pre-defined categories is a non-trivial problem problem due to short text of the tweet (the maximum length of a tweet can be 140 characters) and noisy content (incorrect grammar, spelling mistakes, presence of standard and non-standard abbreviations and slang). We frame the problem of hate and extremism promoting tweet detection as a one-class or unary-class categorization problem by learning a statistical model from a training set containing only the objects of one class . We propose several linguistic features such as presence of war, religious, negative emotions and offensive terms to discriminate hate and extremism promoting tweets from other tweets. We employ a single-class SVM and KNN algorithm for one-class classification task. We conduct a case-study on Jihad, perform a characterization study of the tweets and measure the precision and recall of the machine-learning based classifier. Experimental results on large and real-world dataset demonstrate that the proposed approach is effective with F-score of 0.60 and 0.83 for the KNN and SVM classifier respectively.

VOX-Pol Blog Post.

VOX-Pol Blog post.

VOX-Pol Blog Post.

VOX-Pol Blog Post.

Does the URL referral structure of websites lead users into ‘rabbit holes’ of unreliable content? Past work suggests algorithmic recommender systems on sites like YouTube lead users to view more unreliable content. However, websites without algorithmic recommender systems have financial and political motivations to influence the movement of users, potentially creating browsing rabbit holes. We address this gap using browser telemetry that captures referrals to a large sample of domains rated as reliable or unreliable information sources. Our results suggest the incentives for unreliable sites to retain and monetise users create rabbit holes. After landing on an unreliable site, users are very likely to be referred to another page on the site. Further, unreliable sites are better at retaining users than reliable sites. We find less support for political motivations. While reliable and unreliable sites are largely disconnected from one another, the probability of traveling from one unreliable site to another is relatively low. Our findings indicate the need for additional focus on site-level incentives to shape traffic moving through their sites.