Stakeholder Suite: A Unified AI Framework for Mapping Actors, Topics and Arguments in Public Debates
This addresses the need for transparent tools in media intelligence to anticipate controversies and inform engagement strategies for infrastructure and energy projects, though it appears incremental.
The paper tackles the problem of understanding complex public debates by presenting Stakeholder Suite, a unified AI framework that maps actors, topics, and arguments, achieving 75% relevance in pilot use cases.
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.