HHG-Bot: A Hyperheterogeneous Graph-Based Twitter Bot Detection Model

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.