Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities

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Authors Udit Arora, Hridoy Sankar Dutta, Brihi Joshi, A. Chetan, Tanmoy Chakraborty
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Paper Abstract With the rise in popularity of social media platforms like Twitter, having higher influence on these platforms has a greater value attached to it, since it has the power to influence many decisions in the form of brand promotions and shaping opinions. However, blackmarket services that allow users to inorganically gain influence are a threat to the credibility of these social networking platforms. Twitter users can gain inorganic appraisals in the form of likes, retweets, and follows through these blackmarket services either by paying for them or by joining syndicates wherein they gain such appraisals by providing similar appraisals to other users. These customers tend to exhibit a mix of organic and inorganic retweeting behavior, making it tougher to detect them. In this article, we investigate these blackmarket customers engaged in collusive retweeting activities. We collect and annotate a novel dataset containing various types of information about blackmarket customers and use these sources of information to construct multiple user representations. We adopt Weighted Generalized Canonical Correlation Analysis (WGCCA) to combine these individual representations to derive user embeddings that allow us to effectively classify users as genuine users, bots, promotional customers, and normal customers. Our method significantly outperforms state-of-the-art approaches (32.95% better macro F1-score than the best baseline).
Date of publication 2020
Code Programming Language Python

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