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Twitter bot detection has become an increasingly important and challengingtask to combat online misinformation, facilitate social content moderation, andsafeguard the integrity of social platforms. Though existing graph-basedTwitter bot detection methods achieved state-of-the-art performance, they areall based on the homophily assumption, which assumes users with the same labelare more likely to be connected, making it easy for Twitter bots to disguisethemselves by following a large number of genuine users. To address this issue,we proposed HOFA, a novel graph-based Twitter bot detection framework thatcombats the heterophilous disguise challenge with a homophily-oriented graphaugmentation module (Homo-Aug) and a frequency adaptive attention module(FaAt). Specifically, the Homo-Aug extracts user representations and computes ak-NN graph using an MLP and improves Twitter's homophily by injecting the k-NNgraph. For the FaAt, we propose an attention mechanism that adaptively servesas a low-pass filter along a homophilic edge and a high-pass filter along aheterophilic edge, preventing user features from being over-smoothed by theirneighborhood. We also introduce a weight guidance loss to guide the frequencyadaptive attention module. Our experiments demonstrate that HOFA achievesstate-of-the-art performance on three widely-acknowledged Twitter bot detectionbenchmarks, which significantly outperforms vanilla graph-based bot detectiontechniques and strong heterophilic baselines. Furthermore, extensive studiesconfirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's abilityto demystify the heterophilous disguise challenge.

HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention