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In recent years, Twitter has seen a proliferation of automated accounts orbots that send spam, offer clickbait, compromise security using malware, andattempt to skew public opinion. Previous research estimates that around 9% to17% of Twitter accounts are bots contributing to between 16% to 56% of tweetson the medium. This paper introduces an unsupervised approach to detect Twitterspam campaigns in real-time. The bot groups we detect tweet duplicate contentwith shortened embedded URLs over extended periods of time. Our experimentswith the detection protocol reveal that bots consistently account for 10% to50% of tweets generated from 7 popular URL shortening services on Twitter. Moreimportantly, we discover that bots using shortened URLs are connected to largescale spam campaigns that control thousands of domains. There appear to be twodistinct mechanisms used to control bot groups and we investigate both in thispaper. Our detection system runs 24/7 and actively collects bots involved inspam campaigns and adds them to an evolving database of malicious bots. We makeour database of detected bots available for query through a REST API so otherscan filter tweets from malicious bots to get high quality Twitter datasets foranalysis.

An Unsupervised Approach to Detect Spam Campaigns that Use Botnets on Twitter