# MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark

arXiv: https://arxiv.org/abs/2301.01123 · HTML: https://mlsanity.com/paper/2301.01123/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 69 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking](https://mlsanity.com/paper/1911.01214/) (2019)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers](https://mlsanity.com/paper/2109.02927/) (2021)
- [BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2106.13092/) (2021)
- [Fame for sale: efficient detection of fake Twitter followers](https://mlsanity.com/paper/1509.04098/) (2015)
- [MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://mlsanity.com/paper/1606.03784/) (2016)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Stance Detection in Web and Social Media: A Comparative Study](https://mlsanity.com/paper/2007.05976/) (2020)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [SATAR: A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection](https://mlsanity.com/paper/2106.13089/) (2021)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs](https://mlsanity.com/paper/1606.05694/) (2016)
- [The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation](https://mlsanity.com/paper/2204.02000/) (2022)

## Built on by (most cited first, 16 of 16)

- [Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection](https://mlsanity.com/paper/2302.06900/) (2023)
- [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)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [BSG4Bot: Efficient Bot Detection based on Biased Heterogeneous Subgraphs](https://mlsanity.com/paper/2410.05356/) (2024)
- [Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes](https://mlsanity.com/paper/2503.09626/) (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)
- [RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection](https://mlsanity.com/paper/2602.21749/) (2026)
- [Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents](https://mlsanity.com/paper/2605.12512/) (2026)
- [Social Media Bot Detection using Dropout-GAN](https://mlsanity.com/paper/2311.05079/) (2023)
- [A Survey on Quantitative Modeling of Trust in Online Social Networks](https://mlsanity.com/paper/2603.11054/) (2026)
- [SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection](https://mlsanity.com/paper/2605.30166/) (2026)
- [CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era](https://mlsanity.com/paper/2609.23320/) (2026)
- [Evi-VN: Hard Region Guided Virtual Node Evidence Injection for GNN-Based Fraud Detection](https://mlsanity.com/paper/2610.11665/) (2026)

## Abstract (arXiv)

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.