HHG-Bot: A Hyperheterogeneous Graph-Based Twitter Bot Detection Model
Tianbo Wang, Zhao Wang, Huacheng Li, Chunhe Xia, Chun Jiang Zhao
Abstract
Detecting Twitter bots is essential for combating misinformation and maintaining the integrity of online social networks. Existing methods often overlook the high-order interactions and heterogeneous relationships among users and tweets, limiting their effectiveness in addressing sophisticated bot behaviors. This article introduces HHG-Bot, a novel hyper-heterogeneous graph-based framework for Twitter bot detection. The proposed approach integrates heterogeneous graph convolutional networks with a trainable hypergraph aggregation model to capture complex, high-order interactions. To overcome the challenge of labeled data scarcity, HHG-Bot employs a meta-learning paradigm that enhances the model’s generalization capability across different bot types. Experiments conducted on the Twibot-20 benchmark dataset demonstrate that HHG-Bot achieves state-of-the-art performance, surpassing existing methods in terms of accuracy (86.17%), F1-score (87.51%), and Matthews correlation coefficient (MCC) (71.75%). The results validate the effectiveness of leveraging hypergraphs and meta-learning for detecting Twitter bots, particularly in scenarios with limited labeled data.