DHOW-MiLLA: Joint Workshop on Diffusion of Harmful Content on Online Web and Countering Misinformation in the Age of LLMs and Agents

The rapid proliferation of digital technologies and the ascent of large language models (LLMs) and agent-based AI systems have fundamentally altered the information landscape, creating a dual-use paradigm where advanced tools facilitate both the detection and the sophisticated generation of harmful content. While these technologies offer significant benefits for automated fact-checking and claim verification, they simultaneously enable the scalable production of deepfakes, personalized propaganda, and multimodal misinformation across diverse languages and platforms. Current research efforts remain largely fragmented often restricted to specific platforms, monolingual contexts, or isolated issues such as hate speech or cyber-bullying, rendering traditional counter-measures like blocking or down-ranking increasingly ineffective against actors who seamlessly switch platforms to maintain reach. To address these evolving complexities, it is no longer sufficient to rely on historical assumptions regarding content propagation; rather, the community must adopt interdisciplinary frameworks that integrate novel agentic architectures for generation, retrieval, verification, and explanation. Our DHOW-MiLLA workshop aims to consolidate these disparate research directions under one umbrella, bridging the gap between academia and industry to develop robust, transparent, and adaptive systems for a safer Internet. By focusing on critical contemporary issues, including the impact of AI-generated content on elections and geopolitical conflicts, the workshop seeks to mobilize a cohesive research community. Through rich dialogue and knowledge exchange, this initiative strives to reorient the research agenda, fostering the development of cross-platform, multilingual solutions that not only mitigate emerging risks but actively harness AI capabilities to counteract the multifaceted nature of modern misinformation.