# Adversarial Creation and Detection of AI-Generated Social Bot Content

arXiv: https://arxiv.org/abs/2606.07219 · HTML: https://mlsanity.com/paper/2606.07219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities](https://mlsanity.com/paper/2308.12833/) (2023)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media](https://mlsanity.com/paper/2412.18148/) (2024)
- [CatBoost: unbiased boosting with categorical features](https://mlsanity.com/paper/1706.09516/) (2017)
- [Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore](https://mlsanity.com/paper/2405.04286/) (2024)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [Under the Surface: Tracking the Artifactuality of LLM-Generated Data](https://mlsanity.com/paper/2401.14698/) (2024)
- [How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare](https://mlsanity.com/paper/2506.06299/) (2025)
- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [How Well Can LLMs Echo Us? Evaluating AI Chatbots' Role-Play Ability with ECHO](https://mlsanity.com/paper/2404.13957/) (2024)
- [Characteristics and prevalence of fake social media profiles with AI-generated faces](https://mlsanity.com/paper/2401.02627/) (2024)
- [WETBench: A Benchmark for Detecting Task-Specific Machine-Generated Text on Wikipedia](https://mlsanity.com/paper/2507.03373/) (2025)
- [TGDataset: Collecting and Exploring the Largest Telegram Channels Dataset](https://mlsanity.com/paper/2303.05345/) (2023)
- [Social Bots: Detection and Challenges](https://mlsanity.com/paper/2312.17423/) (2023)

## Abstract (arXiv)

The convergence of large language models and social bots allows malicious actors to manipulate the information ecosystem by generating human-like content at scale. Existing models for detecting AI-generated content often fail in the wild, primarily due to the lack of ground-truth data. We address this gap through an adversarial methodology that models the impersonation of real social media users by malicious actors. Using this methodology, we curate a multilingual, cross-platform dataset of paired human and AI-generated messages. Training on such adversarial data yields accurate detection of AI-generated text. Our approach significantly outperforms existing models for content-based bot detection in real-world, out-of-distribution data.