  Twitter bot detection has become an increasingly important task to combat
misinformation, facilitate social media moderation, and preserve the integrity
of the online discourse. State-of-the-art bot detection methods generally
leverage the graph structure of the Twitter network, and they exhibit promising
performance when confronting novel Twitter bots that traditional methods fail
to detect. However, very few of the existing Twitter bot detection datasets are
graph-based, and even these few graph-based datasets suffer from limited
dataset scale, incomplete graph structure, as well as low annotation quality.
In fact, the lack of a large-scale graph-based Twitter bot detection benchmark
that addresses these issues has seriously hindered the development and
evaluation of novel graph-based bot detection approaches. In this paper, we
propose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmark
that presents the largest dataset to date, provides diversified entities and
relations on the Twitter network, and has considerably better annotation
quality than existing datasets. In addition, we re-implement 35 representative
Twitter bot detection baselines and evaluate them on 9 datasets, including
TwiBot-22, to promote a fair comparison of model performance and a holistic
understanding of research progress. To facilitate further research, we
consolidate all implemented codes and datasets into the TwiBot-22 evaluation
framework, where researchers could consistently evaluate new models and
datasets. The TwiBot-22 Twitter bot detection benchmark and evaluation
framework are publicly available at https://twibot22.github.io/
