Emotional bots: Content-based spammer detection on social media

Andriotis, Panagiotis; Takasu, Atsuhiro

Abstract

Recent research indicates that a considerable amount
of content on social media is generated by automated accounts. The automata present sophisticated behavior –
mimicking humans– aiming at evading traditional detection
methods. In this paper, we present a supervised approach to detect automated accounts on Twitter using mainly content-based features. We performed our experiments using four datasets that contain tweets from almost 20K malicious and benign accounts. Our methodology is lightweight and employs users’ metadata, content and sentiment features. It performs well on unseen data (0.95 F1-score) reaching 95% precision and recall. This work also demonstrates that sentiment characteristics can add value to social spambot detection algorithms when combined with known features.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 10th IEEE International Workshop on Information Forensics and Security, WIFS 2018
• Start Date: Dec 11, 2018
• End Date: Dec 13, 2018
• Acceptance Date: Aug 31, 2018
• Online Publication Date: Jan 31, 2019
• Publication Date: Jan 31, 2019
• Deposit Date: Sep 6, 2018
• Publicly Available Date: Mar 1, 2019
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 1-8
• Series Title: 2018 IEEE International Workshop on Information Forensics and Security (WIFS)
• Series ISSN: 2157-4774
• ISBN: 9781538665367
• DOI: https://doi.org/10.1109/WIFS.2018.8630760
• Keywords: Twitter , Feature extraction , Metadata , Sentiment analysis , Uniform resource locators , Mathematical model
• Public URL: https://uwe-repository.worktribe.com/output/873044
• Additional Information: Title of Conference or Conference Proceedings : IEEE International Workshop on Information Forensics and Security (WIFS)
• Contract Date: Sep 6, 2018