BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers

TL;DR

BotDGT introduces a dynamic graph transformer framework that effectively models the evolving nature of social networks to improve social bot detection accuracy and robustness.

Contribution

It is the first to incorporate both topological structure and dynamic temporal information using a novel dynamic graph transformer for social bot detection.

Findings

Outperforms existing static graph methods in accuracy, recall, and F1-score.

Effectively captures evolving behavior patterns of social bots.

Demonstrates robustness against evasion tactics.

Abstract

Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions. While earlier graph-based approaches, which leverage topological structure of social networks, yielded notable outcomes, they overlooked the inherent dynamicity of social networks -- In reality, they largely depicted the social network as a static graph and solely relied on its most recent state. Due to the absence of dynamicity modeling, such approaches are vulnerable to evasion, particularly when advanced social bots interact with other users to camouflage identities and escape detection. To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network. Specifically, we characterize a social network as a dynamic graph. A structural module is employed to acquire topological information from each historical snapshot. Additionally, a temporal module is proposed to integrate historical context and model the evolving behavior patterns exhibited by social bots and legitimate users. Experimental results demonstrate the superiority of BotDGT against the leading methods that neglected the dynamic nature of social networks in terms of accuracy, recall, and F1-score.