Beyond speculation: Measuring the Growing Presence of LLM-generated texts in Multilingual Disinformation

Jan 1, 2026·
Dominik Macko
,
Aashish Anantha Ramakrishnan
Jason Lucas
Jason Lucas
,
Robert Moro
,
Ivan Srba
,
Adaku Uchendu
,
Dongwon Lee
· 1 min read
Abstract
Large Language Models (LLMs) have demonstrated unprecedented capabilities in generating human-like text, raising significant concerns about their potential misuse in creating disinformation campaigns across multiple languages. This study moves beyond speculation to provide empirical measurements of the growing presence of LLM-generated texts in multilingual disinformation ecosystems. We analyze the proliferation of AI-generated content across diverse linguistic contexts, examining how different languages and cultural contexts influence the detection and spread of LLM-generated disinformation. Our findings reveal concerning trends in the sophistication and scale of AI-generated multilingual disinformation, highlighting the urgent need for robust detection mechanisms and cross-linguistic approaches to combat this emerging threat.
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Publication
In IEEE (Accepted)
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This work provides the first comprehensive empirical analysis of LLM-generated content in multilingual disinformation campaigns, offering crucial insights for developing cross-linguistic detection strategies.
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Status: This article has been accepted for publication in IEEE (2025). Full publication details including DOI and page numbers will be updated upon final publication.

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Jason Lucas
Authors
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.

Authors