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Twitter bot detection has become an important and challenging task to combatmisinformation and protect the integrity of the online discourse.State-of-the-art approaches generally leverage the topological structure of theTwittersphere, while they neglect the heterogeneity of relations and influenceamong users. In this paper, we propose a novel bot detection framework toalleviate this problem, which leverages the topological structure ofuser-formed heterogeneous graphs and models varying influence intensity betweenusers. Specifically, we construct a heterogeneous information network withusers as nodes and diversified relations as edges. We then propose relationalgraph transformers to model heterogeneous influence between users and learnnode representations. Finally, we use semantic attention networks to aggregatemessages across users and relations and conduct heterogeneity-aware Twitter botdetection. Extensive experiments demonstrate that our proposal outperformsstate-of-the-art methods on a comprehensive Twitter bot detection benchmark.Additional studies also bear out the effectiveness of our proposed relationalgraph transformers, semantic attention networks and the graph-based approach ingeneral.

Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers