Bot Detection via Heterogeneous Motifs and Naive Bayes

This paper addresses the critical problem of social bot detection, which is crucial for maintaining the integrity of social media. It proposes a novel approach using heterogeneous motifs and a Naive Bayes model, offering a theoretically grounded solution that improves upon existing methods. The focus on incorporating node-label information to capture neighborhood preference heterogeneity and quantifying motif capabilities is a significant contribution. The paper's strength lies in its systematic approach and the demonstration of superior performance on benchmark datasets.

• •Proposes a novel bot detection method using heterogeneous motifs and Naive Bayes.
• •Incorporates node-label information to capture neighborhood preference heterogeneity.
• •Quantifies the maximum capability of each heterogeneous motif.
• •Achieves superior performance compared to state-of-the-art techniques.
• •Offers a theoretically grounded solution for social bot detection.