  Twitter bots are automatic programs operated by malicious actors to
manipulate public opinion and spread misinformation. Research efforts have been
made to automatically identify bots based on texts and networks on social
media. Existing methods only leverage texts or networks alone, and while few
works explored the shallow combination of the two modalities, we hypothesize
that the interaction and information exchange between texts and graphs could be
crucial for holistically evaluating bot activities on social media. In
addition, according to a recent survey (Cresci, 2020), Twitter bots are
constantly evolving while advanced bots steal genuine users' tweets and dilute
their malicious content to evade detection. This results in greater
inconsistency across the timeline of novel Twitter bots, which warrants more
attention. In light of these challenges, we propose BIC, a Twitter Bot
detection framework with text-graph Interaction and semantic Consistency.
Specifically, in addition to separately modeling the two modalities on social
media, BIC employs a text-graph interaction module to enable information
exchange across modalities in the learning process. In addition, given the
stealing behavior of novel Twitter bots, BIC proposes to model semantic
consistency in tweets based on attention weights while using it to augment the
decision process. Extensive experiments demonstrate that BIC consistently
outperforms state-of-the-art baselines on two widely adopted datasets. Further
analyses reveal that text-graph interactions and modeling semantic consistency
are essential improvements and help combat bot evolution.
