TLDR: RoGBot is a novel bot detection framework that overcomes the limitation of relying on follower-following data. It combines semantic features from tweets (using BERT) with user metadata to construct a user-user interaction graph based on behavioral and linguistic similarities. This graph is then processed by a GraphSAGE model for relational reasoning, leading to highly accurate bot detection on various benchmark datasets, even when explicit social network information is unavailable.
The proliferation of automated accounts, commonly known as bots, on social media platforms like Twitter presents a significant challenge to maintaining authentic online discourse. These bots are designed to mimic human behavior, spreading misinformation, artificially boosting engagement, and distorting public discussions, especially during critical events such as elections or health emergencies.
Traditional bot detection methods often struggle with sophisticated bots that generate human-like language and maintain realistic profiles. While advanced techniques, particularly those using Graph Neural Networks (GNNs), have shown promise by incorporating user interactions and relationships, many of these models heavily depend on explicit follower-following data. This reliance becomes a major limitation when such relationship information is unavailable, as is often the case with newer datasets or restricted API access.
Introducing RoGBot: A Novel Approach to Bot Detection
A new research paper, titled RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection, proposes an innovative solution to this problem. Developed by Ashutosh Anshul, Mohammad Zia Ur Rehman, Sri Akash Kadali, and Nagendra Kumar, RoGBot is a multimodal framework that integrates detailed textual features with enriched user metadata, employing graph-based reasoning without requiring follower-following data.
RoGBot addresses the critical gap in bot detection by constructing a user-user interaction graph based solely on behavioral and metadata similarities between users, rather than explicit social connections. This design choice significantly broadens its applicability to datasets where social network information is missing, such as Cresci-17 and PAN 2019.
How RoGBot Works
The framework operates through several key stages:
- Data Preprocessing: Raw tweet texts undergo cleaning, transliteration (for non-Roman scripts), and normalization to ensure high-quality input for language models.
- Auxiliary Feature Extraction: User-level metadata, such as follower count, friends count, statuses count, and favorites count, are extracted. These features provide valuable behavioral cues that often differ significantly between bots and humans.
- BERT Feature Extraction and Aggregation: A pre-trained BERT model is used to extract deep semantic embeddings from individual tweets. These tweet-level embeddings are then aggregated (using max pooling) to form a single, comprehensive semantic representation for each user.
- Joint Feature Fusion: The aggregated tweet embeddings and auxiliary behavioral features are combined into a unified vector, creating a holistic representation of each user.
- Graph Construction: This is a crucial step where a user-user graph is built. Instead of using follower-following data, RoGBot calculates the pairwise cosine similarity between the fused feature vectors of users. An edge is established between two users if their similarity exceeds a predefined threshold, effectively connecting users with similar behavioral or linguistic patterns.
- Graph-Based Feature Propagation (GraphSAGE): The constructed graph and the fused user features are fed into a GraphSAGE model. GraphSAGE refines user representations by aggregating information from neighboring nodes, allowing the model to capture both local and higher-order interactions and generalize to unseen users.
- Deep Learning Classification: Finally, the enriched user embeddings from GraphSAGE are passed through fully connected layers and a softmax function to classify users as either bots or humans.
Also Read:
- RIDGE: A New Approach to Building Resilient Signed Graph Neural Networks Against Data Noise
- Strengthening Graph Neural Networks Against Adversarial Attacks with Singular Pooling
Performance and Impact
Experimental results on widely accepted bot detection benchmarks, including Cresci-15, Cresci-17, and PAN 2019 datasets, demonstrate the robustness and effectiveness of RoGBot. The model achieved impressive accuracies of 99.8% on Cresci-15, 99.1% on Cresci-17, and 96.8% on PAN 2019. Notably, RoGBot performs comparably to, and in some cases outperforms, state-of-the-art approaches even without utilizing follower-following relationships.
An ablation study confirmed the critical contribution of both auxiliary features and the GraphSAGE component to the model’s strong performance. The ability of RoGBot to effectively distinguish between human and bot users, as visualized through t-SNE plots and reinforced by high AUC scores in ROC curves, highlights its reliable detection capabilities even in scenarios with class imbalance.
By providing a powerful, adaptable, and relationship-oblivious framework, RoGBot marks a significant advancement in the ongoing arms race against increasingly sophisticated bot strategies, making it highly suitable for real-world deployment where explicit social network data may be scarce.
