BotRGA: Neighborhood-Aware Twitter Bot Detection with Relational Graph Aggregation

With the rapid development of AI-based technology, social bot detection is becoming an increasingly challenging task to combat the spread of misinformation and protect the authenticity of online resources. Existing graph-based social bot detection approaches primarily rely on the topological structure of the Twittersphere but often overlook the diverse influence dynamics across different relationships. Moreover, these methods typically aggregate only direct neighbors based on transitive learning, limiting their effectiveness in capturing the nuanced interactions within evolving social Twittersphere. In this paper, we propose BotRGA, a novel Twitter bot detection framework based on inductive representation learning. Our method begins with extracting the semantic features from Twitter user profiles, descriptions, tweets and constructing a heterogeneous graph, where nodes represent users and edges represent relationships. We then propose a relational graph aggregation method to learn node representations by sampling and aggregating the features from both direct and indirect neighbors. Additionally, we evaluate the importance of different relations and fuse the node’s representations across diversified relations with semantic fusion networks. Finally, we classify Twitter users into bots or genuine users and learn model parameters. Extensive experiments conducted on two comprehensive Twitter bot detection benchmarks demonstrate that the superior performance of BotRGA compared to state-of-the-art methods. Additional studies also confirm that the effectiveness of our proposed relational graph aggregation, semantic fusion networks, and strong generalization ability to new and previously unseen user communities.