  Twitter bot detection has become an important and challenging task to combat
misinformation and protect the integrity of the online discourse.
State-of-the-art approaches generally leverage the topological structure of the
Twittersphere, while they neglect the heterogeneity of relations and influence
among users. In this paper, we propose a novel bot detection framework to
alleviate this problem, which leverages the topological structure of
user-formed heterogeneous graphs and models varying influence intensity between
users. Specifically, we construct a heterogeneous information network with
users as nodes and diversified relations as edges. We then propose relational
graph transformers to model heterogeneous influence between users and learn
node representations. Finally, we use semantic attention networks to aggregate
messages across users and relations and conduct heterogeneity-aware Twitter bot
detection. Extensive experiments demonstrate that our proposal outperforms
state-of-the-art methods on a comprehensive Twitter bot detection benchmark.
Additional studies also bear out the effectiveness of our proposed relational
graph transformers, semantic attention networks and the graph-based approach in
general.
