{"slug": "adversarial-creation-and-detection-of-ai-generated-social-bot-content", "title": "Adversarial Creation and Detection of AI-Generated Social Bot Content", "summary": "Researchers at the University of California, Berkeley, led by Mykola Trokhymovych, developed an adversarial methodology that models impersonation of real social media users to create a multilingual, cross-platform dataset of paired human and AI-generated messages, and showed that training on such data yields accurate detection of AI-generated text, significantly outperforming existing content-based bot detection models on real-world out-of-distribution data.", "body_md": "# Computer Science > Computation and Language\n\n[Submitted on 5 Jun 2026]\n\n# Title:Adversarial Creation and Detection of AI-Generated Social Bot Content\n\n[View PDF](/pdf/2606.07219)\n\n[HTML (experimental)](https://arxiv.org/html/2606.07219v1)\n\nAbstract: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.\n\n## Submission history\n\nFrom: Mykola Trokhymovych [[view email](/show-email/b810330f/2606.07219)]\n\n**[v1]** Fri, 5 Jun 2026 12:32:47 UTC (1,155 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/adversarial-creation-and-detection-of-ai-generated-social-bot-content", "canonical_source": "https://arxiv.org/abs/2606.07219", "published_at": "2026-08-15 13:40:47+00:00", "updated_at": "2026-08-15 14:11:00.252243+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety"], "entities": ["University of California, Berkeley", "Mykola Trokhymovych"], "alternates": {"html": "https://wpnews.pro/news/adversarial-creation-and-detection-of-ai-generated-social-bot-content", "markdown": "https://wpnews.pro/news/adversarial-creation-and-detection-of-ai-generated-social-bot-content.md", "text": "https://wpnews.pro/news/adversarial-creation-and-detection-of-ai-generated-social-bot-content.txt", "jsonld": "https://wpnews.pro/news/adversarial-creation-and-detection-of-ai-generated-social-bot-content.jsonld"}}