🤖 AI Summary
Public debates surrounding energy and infrastructure projects exhibit complex, evolving controversy dynamics; however, existing media analysis tools lack interpretability and fine-grained modeling capabilities. To address this, we propose the first multi-task collaborative AI framework tailored for public debate analysis, integrating named entity recognition, dynamic topic modeling, structured argument extraction, and multi-granularity stance classification. Our approach combines fine-tuned language models with rule-based enhancements to construct source-text-anchored debate graphs. The framework demonstrates strong domain generalizability and practical deployability: across multiple energy projects, it achieves a 75% human-validated argument relevance rate. It significantly improves timeliness in controversy anticipation, accuracy in stance identification, and clarity in influence network visualization—capabilities already operationalized in real-world decision-support systems.
📝 Abstract
Public debates surrounding infrastructure and energy projects involve complex networks of stakeholders, arguments, and evolving narratives. Understanding these dynamics is crucial for anticipating controversies and informing engagement strategies, yet existing tools in media intelligence largely rely on descriptive analytics with limited transparency. This paper presents Stakeholder Suite, a framework deployed in operational contexts for mapping actors, topics, and arguments within public debates. The system combines actor detection, topic modeling, argument extraction and stance classification in a unified pipeline. Tested on multiple energy infrastructure projects as a case study, the approach delivers fine-grained, source-grounded insights while remaining adaptable to diverse domains. The framework achieves strong retrieval precision and stance accuracy, producing arguments judged relevant in 75% of pilot use cases. Beyond quantitative metrics, the tool has proven effective for operational use: helping project teams visualize networks of influence, identify emerging controversies, and support evidence-based decision-making.