Federated prompt tuning for news framing: a community-aware approach to narrative exploitability

The spread of misinformation and manipulative narratives is a significant challenge in today's information landscape, shaping public opinion across diverse communities. Traditional framing detection methods are constrained by their post-hoc nature, limiting their ability to anticipate emerging framing tactics in real-time. To overcome these limitations, this work proposes a framework that combines Retrieval-Augmented Generation (RAG) models with Federated Prompt Tuning to generate and iteratively refine community-aware prompts. These prompts simulate how narratives derived from events, news, or factual statements might be reframed across different communities, enabling proactive identification of potentially manipulative content. Additionally, the framework exemplifies the exploitation of the framework to quantify the susceptibility of narratives to framing and support early warning systems for disinformation. Preliminary experiments conducted on a real-world dataset, including diverse community profiles, reveal the framework's potential to combat the spread of disinformation and assess framing vulnerabilities in a timely and scalable manner.