  As malicious actors employ increasingly advanced and widespread bots to
disseminate misinformation and manipulate public opinion, the detection of
Twitter bots has become a crucial task. Though graph-based Twitter bot
detection methods achieve state-of-the-art performance, we find that their
inference depends on the neighbor users multi-hop away from the targets, and
fetching neighbors is time-consuming and may introduce bias. At the same time,
we find that after finetuning on Twitter bot detection, pretrained language
models achieve competitive performance and do not require a graph structure
during deployment. Inspired by this finding, we propose a novel bot detection
framework LMBot that distills the knowledge of graph neural networks (GNNs)
into language models (LMs) for graph-less deployment in Twitter bot detection
to combat the challenge of data dependency. Moreover, LMBot is compatible with
graph-based and graph-less datasets. Specifically, we first represent each user
as a textual sequence and feed them into the LM for domain adaptation. For
graph-based datasets, the output of LMs provides input features for the GNN,
enabling it to optimize for bot detection and distill knowledge back to the LM
in an iterative, mutually enhancing process. Armed with the LM, we can perform
graph-less inference, which resolves the graph data dependency and sampling
bias issues. For datasets without graph structure, we simply replace the GNN
with an MLP, which has also shown strong performance. Our experiments
demonstrate that LMBot achieves state-of-the-art performance on four Twitter
bot detection benchmarks. Extensive studies also show that LMBot is more
robust, versatile, and efficient compared to graph-based Twitter bot detection
methods.
