# BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2106.13092 · HTML: https://mlsanity.com/paper/2106.13092/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2021.

## Built on by (most cited first, 30 of 39)

- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction](https://mlsanity.com/paper/2306.01951/) (2023)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights](https://mlsanity.com/paper/2406.10727/) (2024)
- [RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search](https://mlsanity.com/paper/2206.06757/) (2022)
- [LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://mlsanity.com/paper/2306.17408/) (2023)
- [MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark](https://mlsanity.com/paper/2301.01123/) (2023)
- [SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection](https://mlsanity.com/paper/2405.11225/) (2024)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection](https://mlsanity.com/paper/2302.06900/) (2023)
- [Datavoidant: An AI System for Addressing Political Data Voids on Social Media](https://mlsanity.com/paper/2210.13594/) (2022)
- [RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection](https://mlsanity.com/paper/2304.08239/) (2023)
- [Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery](https://mlsanity.com/paper/2506.00989/) (2025)
- [Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective](https://mlsanity.com/paper/2307.01968/) (2023)
- [Multimodal Detection of Bots on X (Twitter) using Transformers](https://mlsanity.com/paper/2308.14484/) (2023)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [TRACE-Bot: Detecting Emerging LLM-Driven Social Bots via Implicit Semantic Representations and AIGC-Enhanced Behavioral Patterns](https://mlsanity.com/paper/2604.02147/) (2026)
- [BSG4Bot: Efficient Bot Detection based on Biased Heterogeneous Subgraphs](https://mlsanity.com/paper/2410.05356/) (2024)
- [A Graph Neural Architecture Search Approach for Identifying Bots in Social Media](https://mlsanity.com/paper/2411.16285/) (2024)
- [ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making](https://mlsanity.com/paper/2503.04569/) (2025)
- [Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes](https://mlsanity.com/paper/2503.09626/) (2025)
- [A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects](https://mlsanity.com/paper/2504.04311/) (2025)
- [BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection](https://mlsanity.com/paper/2506.13795/) (2025)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection](https://mlsanity.com/paper/2602.00318/) (2026)
- [FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation](https://mlsanity.com/paper/2604.10678/) (2026)
- [BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline](https://mlsanity.com/paper/2306.00037/) (2023)

## Abstract (arXiv)

Twitter bot detection is an important and challenging task. Existing bot detection measures fail to address the challenge of community and disguise, falling short of detecting bots that disguise as genuine users and attack collectively. To address these two challenges of Twitter bot detection, we propose BotRGCN, which is short for Bot detection with Relational Graph Convolutional Networks. BotRGCN addresses the challenge of community by constructing a heterogeneous graph from follow relationships and applies relational graph convolutional networks. Apart from that, BotRGCN makes use of multi-modal user semantic and property information to avoid feature engineering and augment its ability to capture bots with diversified disguise. Extensive experiments demonstrate that BotRGCN outperforms competitive baselines on a comprehensive benchmark TwiBot-20 which provides follow relationships.