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- W2913462649 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." @default.
- W2913462649 created "2019-02-21" @default.
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- W2913462649 date "2018-12-01" @default.
- W2913462649 modified "2023-09-27" @default.
- W2913462649 title "Emotional Bots: Content-based Spammer Detection on Social Media" @default.
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- W2913462649 doi "https://doi.org/10.1109/wifs.2018.8630760" @default.
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