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Twitter bot detection has become an increasingly important task to combatmisinformation, facilitate social media moderation, and preserve the integrityof the online discourse. State-of-the-art bot detection methods generallyleverage the graph structure of the Twitter network, and they exhibit promisingperformance when confronting novel Twitter bots that traditional methods failto detect. However, very few of the existing Twitter bot detection datasets aregraph-based, and even these few graph-based datasets suffer from limiteddataset scale, incomplete graph structure, as well as low annotation quality.In fact, the lack of a large-scale graph-based Twitter bot detection benchmarkthat addresses these issues has seriously hindered the development andevaluation of novel graph-based bot detection approaches. In this paper, wepropose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmarkthat presents the largest dataset to date, provides diversified entities andrelations on the Twitter network, and has considerably better annotationquality than existing datasets. In addition, we re-implement 35 representativeTwitter bot detection baselines and evaluate them on 9 datasets, includingTwiBot-22, to promote a fair comparison of model performance and a holisticunderstanding of research progress. To facilitate further research, weconsolidate all implemented codes and datasets into the TwiBot-22 evaluationframework, where researchers could consistently evaluate new models anddatasets. The TwiBot-22 Twitter bot detection benchmark and evaluationframework are publicly available at https://twibot22.github.io/

TwiBot-22: Towards Graph-Based Twitter Bot Detection