Unsupervised twitter social bot detection using deep contrastive graph clustering

Abstract

The emergence of malicious Twitter social bots poses a considerable threat to the security of social networks, and the detection of evolving social bots has become challenging. State-of-the-art detection methods are usually supervised, but the label acquisition process suffers from time-consuming and inaccurate problems in addition to its inability to cope with the challenge of the continuous evolution of social bots. Moreover, structural relationships within social networks are under-explored using current unsupervised methods. To address current challenges, we propose an unsupervised social bot detection method based on deep contrastive graph clustering (BotDCGC). This approach utilises a graph attentional encoder and an inner product decoder to acquire user node embeddings. By incorporating information from both user account features and topological structure, the model employs a contrastive learning technique based on structure to discern node embeddings of distinct classes within the feature space. Subsequently, confident cluster assignments are used as soft labels to guide the embedding process by calculating the similarity between each node and the clustering centre, enabling the joint optimisation of user node embeddings and clustering results. Experiments show that BotDCGC is more effective than the state-of-the-art baselines with an accuracy of 0.8095 in the Twibot-20 dataset and 0.9334 in the Cresci-2015 dataset, and the experimental results indicate the effectiveness of the graph autoencoder module, contrastive learning and deep clustering module.