Retweet Us, We Will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services
Hridoy Sankar Dutta, Aditya Chetan, Brihi Joshi, Tanmoy Chakraborty

TL;DR
This paper investigates collusive retweeters involved in blackmarket services on Twitter, developing detection models and a real-time browser extension to identify fake retweeters, thereby addressing social reputation manipulation.
Contribution
It introduces a novel dataset of blackmarket retweeters, characterizes their social behavior, and proposes a supervised detection model with a real-time browser extension.
Findings
Supervised models achieve 87% Macro F1-score in detecting customer types.
The browser extension SCoRe accurately spots fake retweeters with 85% user feedback accuracy.
Blackmarket customers exhibit distinct social behaviors from genuine users.
Abstract
Twitter has increasingly become a popular platform to share news and user opinion. A tweet is considered to be important if it receives high number of affirmative reactions from other Twitter users via Retweets. Retweet count is thus considered as a surrogate measure for positive crowd-sourced reactions - high number of retweets of a tweet aid in making its topic trending. This in turn bolsters the social reputation of the author of the tweet. Since social reputation/impact of users/tweets influences many decisions (such as promoting brands, advertisement etc.), several blackmarket syndicates have actively been engaged in producing fake retweets in a collusive manner. Users who want to boost the impact of their tweets approach the blackmarket services, and gain retweets for their own tweets by either paying money (Premium Services) or by retweeting other customers' tweets. Thus they…
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Taxonomy
TopicsSpam and Phishing Detection · Misinformation and Its Impacts · Advanced Malware Detection Techniques
