📊 Just published three research datasets on Zenodo from my doctoral work on AI-driven disinformation:
🔴 RogueGPT Corpus — 2,308 multilingual text fragments from 7 LLMs
🔵 JudgeGPT Data — 504 participants, 2,438 dual-axis perception judgments
🟢 Expert Survey — 21 domain experts on GenAI threats & countermeasures
All CC BY 4.0 | Part of our WWW '26 research
https://doi.org/10.5281/zenodo.18703138
https://doi.org/10.5281/zenodo.18703385
https://doi.org/10.5281/zenodo.18703601
#disinformation #GenerativeAI #OpenData #AcademicResearch #WWW2026
RogueGPT Stimulus Corpus: A Multilingual LLM-Generated News Dataset
A multilingual corpus of 2,308 text fragments generated by seven large language models (GPT-3.5 Turbo, GPT-4o, Gemma 7B, Llama 2 13B, Mistral 7B, Phi-3 Mini, GPT-4) and sourced from human journalists, designed for research on AI-generated disinformation detection. The corpus follows a four-quadrant design crossing content origin (Human vs. Machine) with veracity (Real vs. Fake) across four languages (English, German, French, Spanish) and three formats (tweet, headline, short article). Created using the RogueGPT platform as part of a doctoral dissertation on the AI-driven disinformation ecosystem at Frankfurt University of Applied Sciences.
Our paper "Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems" has been accepted at ACM TheWebConf '26 (WWW '26)! 🎉
We built JudgeGPT and RogueGPT to study how well people can spot AI-generated news. Spoiler: it's harder than you'd think.
Paper: https://arxiv.org/abs/2601.21963
Code: https://github.com/aloth/JudgeGPT
#WWW2026 #misinformation #GenAI #LLM #AcademicMastodon
Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems. Central to this work are our practical contributions: JudgeGPT, a platform for evaluating human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation. Our findings show that detection capabilities have improved, but the competition between generation and detection continues. We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI. This work contributes to research addressing the adverse impacts of AI on information quality.
Our colleague Hidir Aras from @fiz_karlsruhe is co-organising the 4th Int. Workshop on AI and Semantic Technologies for Scientific, Technical, and Legal Web co-located with The Web Conference 2026 in Dubai, UAE
Deadline Jan 5 (abstract)/12(paper submission): https://semtech4stld.github.io/
#www2026 #semanticweb #knowledgegraphs #llms #AI #ontologies #legalAI #AIethics #patents #generativeAI
Alexander Loth
DiTraRe