Identifying social bots via heterogeneous motifs based on Na\"ive Bayes model

TL;DR

This paper introduces a theoretically grounded framework for social bot detection using heterogeneous motifs and Na"ive Bayes, outperforming existing methods on large benchmarks.

Contribution

It refines homogeneous motifs into heterogeneous ones with node-label info and quantifies their detection capabilities, providing a systematic approach for social bot identification.

Findings

Outperforms state-of-the-art detection methods.

Heterogeneous motifs with highest capability achieve similar results to all motifs.

Systematic evaluation confirms the effectiveness of the proposed framework.

Abstract

Identifying social bots has become a critical challenge due to their significant influence on social media ecosystems. Despite advancements in detection methods, most topology-based approaches insufficiently account for the heterogeneity of neighborhood preferences and lack a systematic theoretical foundation, relying instead on intuition and experience. Here, we propose a theoretical framework for detecting social bots utilizing heterogeneous motifs based on the Na\"ive Bayes model. Specifically, we refine homogeneous motifs into heterogeneous ones by incorporating node-label information, effectively capturing the heterogeneity of neighborhood preferences. Additionally, we systematically evaluate the contribution of different node pairs within heterogeneous motifs to the likelihood of a node being identified as a social bot. Furthermore, we mathematically quantify the maximum capability of each heterogeneous motif, enabling the estimation of its potential benefits. Comprehensive evaluations on four large, publicly available benchmarks confirm that our method surpasses state-of-the-art techniques, achieving superior performance across five evaluation metrics. Moreover, our results reveal that selecting motifs with the highest capability achieves detection performance comparable to using all heterogeneous motifs. Overall, our framework offers an effective and theoretically grounded solution for social bot detection, significantly enhancing cybersecurity measures in social networks.