In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns. Detecting bot activity is complex because many bots are actively trying to avoid detection. We present a novel, complex machine learning algorithm utilizing a range of features including: length of user names, reposting rate, temporal patterns, sentiment expression, followers-to-friends ratio, and message variability for bot detection. Our novel technique for Twitter bot detection is effective at detecting bots with a 2.25% misclassification rate.
Efthimion, Phillip George; Payne, Scott; and Proferes, Nicholas
"Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots,"
SMU Data Science Review: Vol. 1:
2, Article 5.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss2/5
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