# BotPercent: Estimating Bot Populations in Twitter Communities

arXiv: https://arxiv.org/abs/2302.00381 · HTML: https://mlsanity.com/paper/2302.00381/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection](https://mlsanity.com/paper/2404.01822/) (2024)
- [Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media](https://mlsanity.com/paper/2406.00969/) (2024)
- [Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes](https://mlsanity.com/paper/2503.09626/) (2025)
- [What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics](https://mlsanity.com/paper/2501.00855/) (2025)

## Abstract (arXiv)

Twitter bot detection is vital in combating misinformation and safeguarding the integrity of social media discourse. While malicious bots are becoming more and more sophisticated and personalized, standard bot detection approaches are still agnostic to social environments (henceforth, communities) the bots operate at. In this work, we introduce community-specific bot detection, estimating the percentage of bots given the context of a community. Our method -- BotPercent -- is an amalgamation of Twitter bot detection datasets and feature-, text-, and graph-based models, adjusted to a particular community on Twitter. We introduce an approach that performs confidence calibration across bot detection models, which addresses generalization issues in existing community-agnostic models targeting individual bots and leads to more accurate community-level bot estimations. Experiments demonstrate that BotPercent achieves state-of-the-art performance in community-level Twitter bot detection across both balanced and imbalanced class distribution settings, %outperforming existing approaches and presenting a less biased estimator of Twitter bot populations within the communities we analyze. We then analyze bot rates in several Twitter groups, including users who engage with partisan news media, political communities in different countries, and more. Our results reveal that the presence of Twitter bots is not homogeneous, but exhibiting a spatial-temporal distribution with considerable heterogeneity that should be taken into account for content moderation and social media policy making. The implementation of BotPercent is available at https://github.com/TamSiuhin/BotPercent.