Social listening in the age of Infodemics: An AI-supported rapid risk assessment framework for public health threats

Background Public health emergencies are increasingly accompanied by infodemics, where the overload of information in digital and physical spaces generates confusion, driving harmful behaviors. Evidence indicates a shift in trust away from healthcare and media institutions toward online platforms, social networks, peers, and influencers, particularly among younger populations. New analytical approaches are needed to support timely public health decision-making. This study pilots an AI-supported framework for rapid social listening, narrative identification, and risk assessment within digital information environments. The framework was tested on social media content related to hormonal contraception, a controversial topic in Serbia. Methods Publicly available posts from YouTube, TikTok, and Instagram were rapidly converted into a unified textual dataset, with video content transcribed using OpenAI Whisper and image-based content processed using EasyOCR. Data was analyzed through large language model-assisted content analysis using Gemini 2.5 Pro to identify dominant narratives within short analytical timeframes. Narratives were evaluated using a two-dimensional risk assessment matrix that integrates exposure and potential health outcomes, resulting in an overall narrative risk classification ranging from low to very high. Results Seventeen distinct narratives were identified, with nine classified as potentially harmful and subjected to risk assessment. High-risk narratives primarily involved personal negative experiences following hormonal contraception use, including psychological and physical side effects, framing emergency contraception as dangerous, synthetic hormones as unnatural, and emphasizing adverse effects after discontinuation. Non-harmful narratives included clarification of misinformation, endorsement of contraception as safe and effective, and the importance of consulting healthcare professionals. Conclusions The proposed framework enabled rapid social listening and narrative risk analysis within short timeframes and with minimal human resources. This approach offers a practical and scalable tool for early detection of infodemic risks, enabling timely and evidence-informed public health communication and response.