RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection
This addresses the challenge of detecting bots in scenarios where relationship data is unavailable, offering a robust solution for social media platforms.
The paper tackled the problem of bot detection on platforms like Twitter by proposing a multimodal framework that integrates textual features and user metadata without relying on explicit user-user relationship data, achieving accuracies of 99.8%, 99.1%, and 96.8% on three datasets.
Detecting automated accounts (bots) among genuine users on platforms like Twitter remains a challenging task due to the evolving behaviors and adaptive strategies of such accounts. While recent methods have achieved strong detection performance by combining text, metadata, and user relationship information within graph-based frameworks, many of these models heavily depend on explicit user-user relationship data. This reliance limits their applicability in scenarios where such information is unavailable. To address this limitation, we propose a novel multimodal framework that integrates detailed textual features with enriched user metadata while employing graph-based reasoning without requiring follower-following data. Our method uses transformer-based models (e.g., BERT) to extract deep semantic embeddings from tweets, which are aggregated using max pooling to form comprehensive user-level representations. These are further combined with auxiliary behavioral features and passed through a GraphSAGE model to capture both local and global patterns in user behavior. Experimental results on the Cresci-15, Cresci-17, and PAN 2019 datasets demonstrate the robustness of our approach, achieving accuracies of 99.8%, 99.1%, and 96.8%, respectively, and highlighting its effectiveness against increasingly sophisticated bot strategies.