  Twitter bot detection has become a crucial task in efforts to combat online
misinformation, mitigate election interference, and curb malicious propaganda.
However, advanced Twitter bots often attempt to mimic the characteristics of
genuine users through feature manipulation and disguise themselves to fit in
diverse user communities, posing challenges for existing Twitter bot detection
models. To this end, we propose BotMoE, a Twitter bot detection framework that
jointly utilizes multiple user information modalities (metadata, textual
content, network structure) to improve the detection of deceptive bots.
Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE)
layer to improve domain generalization and adapt to different Twitter
communities. Specifically, BotMoE constructs modal-specific encoders for
metadata features, textual content, and graphical structure, which jointly
model Twitter users from three modal-specific perspectives. We then employ a
community-aware MoE layer to automatically assign users to different
communities and leverage the corresponding expert networks. Finally, user
representations from metadata, text, and graph perspectives are fused with an
expert fusion layer, combining all three modalities while measuring the
consistency of user information. Extensive experiments demonstrate that BotMoE
significantly advances the state-of-the-art on three Twitter bot detection
benchmarks. Studies also confirm that BotMoE captures advanced and evasive
bots, alleviates the reliance on training data, and better generalizes to new
and previously unseen user communities.
