BotBR: Social Bot Detection with Balanced Feature Fusion and Reliability-Enhanced Graph Learning
Year: 2025
Type: conference-paper
Abstract: The rise of social bots poses a significant threat to online platforms, making their detection an urgent priority.Recent advancements in Graph Neural Networks (GNNs) have significantly improved bot detection by leveraging the rich relational data within social networks.However, existing approaches face two key challenges: imbalanced feature fusion across different modalities and edge heterophily, which limit their effectiveness.To address these issues, we propose BotBR, a novel bot detection framework.BotBR tackles feature imbalance by employing decision trees to extract behavioral patterns from user-related numerical features and utilizes an attention mechanism to seamlessly integrate multi-modal feature embeddings.To mitigate edge heterophily, BotBR incorporates an edge detector to differentiate between high-and low-reliability edges, optimizing the utilization of structural graph information.Furthermore, a homophily-based graph is introduced for consistency contrastive learning, enhancing the model's robustness.Experimental results on real-world bot detection benchmark datasets demonstrate that BotBR achieves state-of-the-art performance while maintaining efficiency comparable to classical methods.
Authors: Qilong Lin, Jingya Zhou
Institutions: Soochow University, Nanjing University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G2177886316, 多源异质社交网络融合与营销影响力传播优化研究
OpenAlex ID: W4412377240