# Scalable and Generalizable Social Bot Detection through Data Selection

arXiv: https://arxiv.org/abs/1911.09179 · HTML: https://mlsanity.com/paper/1911.09179/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 30 of 59)

- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing](https://mlsanity.com/paper/2306.05524/) (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)
- [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)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)
- [Characteristics and prevalence of fake social media profiles with AI-generated faces](https://mlsanity.com/paper/2401.02627/) (2024)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning](https://mlsanity.com/paper/2304.03144/) (2023)
- [GIO: Gradient Information Optimization for Training Dataset Selection](https://mlsanity.com/paper/2306.11670/) (2023)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [Adversarial Socialbots Modeling Based on Structural Information Principles](https://mlsanity.com/paper/2312.08098/) (2023)
- [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection](https://mlsanity.com/paper/2302.06900/) (2023)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates](https://mlsanity.com/paper/2505.11577/) (2025)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery](https://mlsanity.com/paper/2506.00989/) (2025)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study](https://mlsanity.com/paper/2112.04913/) (2021)
- [MulBot: Unsupervised Bot Detection Based on Multivariate Time Series](https://mlsanity.com/paper/2209.10361/) (2022)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [Multimodal Detection of Bots on X (Twitter) using Transformers](https://mlsanity.com/paper/2308.14484/) (2023)
- [Unmasking Social Bots: How Confident Are We?](https://mlsanity.com/paper/2407.13929/) (2024)
- [On the efficacy of old features for the detection of new bots](https://mlsanity.com/paper/2506.19635/) (2025)

## Abstract (arXiv)

Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so that model performance on unseen datasets is also optimized, in addition to traditional cross-validation. We find that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data. Thanks to the simplicity of the proposed model, its logic can be interpreted to provide insights into social bot characteristics.