Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation

Our study makes several key contributions to understanding LLM - generated disinformation: By validat ion on broader datasets, our detection methods establish a robust analytical framework for examining real - world disinformation content, confirming both the increasing presence and prevalence of machine - generated texts in disinformation datasets over time. The distribution of LLM - generated content varies significantly across languages and platforms, revealing targeted patterns of misuse rather than uniform effects. This provides empirical validation for previously speculated concerns and unsupported fears ab out increased LLM deployment in disinformation campaigns. Most importantly, our findings underscore the urgent need for continued investigation and improved countermeasures, including enhanced detection methods and credibility assessment systems to preserve information integrity in our evolving digital landscape.