# BotBuster: Multi-platform Bot Detection Using A Mixture of Experts

arXiv: https://arxiv.org/abs/2207.13658 · HTML: https://mlsanity.com/paper/2207.13658/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2022.

## Built on by (most cited first, 11 of 11)

- [Language-driven All-in-one Adverse Weather Removal](https://mlsanity.com/paper/2312.01381/) (2023)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://mlsanity.com/paper/2306.17408/) (2023)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [LMHaze: Intensity-aware Image Dehazing with a Large-scale Multi-intensity Real Haze Dataset](https://mlsanity.com/paper/2410.16095/) (2024)
- [DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration](https://mlsanity.com/paper/2504.05135/) (2025)
- [Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations](https://mlsanity.com/paper/2409.02965/) (2024)
- [What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics](https://mlsanity.com/paper/2501.00855/) (2025)
- [Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models](https://mlsanity.com/paper/2503.21000/) (2025)
- [RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection](https://mlsanity.com/paper/2510.23648/) (2025)
- [VLM-Augmented Degradation Modeling for Image Restoration Under Adverse Weather Conditions](https://mlsanity.com/paper/2511.16998/) (2025)

## Abstract (arXiv)

Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset and F1=60.92 on an external evaluation set. Further analysis shows that only 36 posts is required for a stable bot classification. Investigation shows that bot post features have changed across the years and can be difficult to differentiate from human features, making bot detection a difficult and ongoing problem.