用统一框架解析公共辩论中的各方立场与议题，助力决策透明化
Stakeholder Suite: A Unified AI Framework for Mapping Actors, Topics and Arguments in Public Debates
- 整合角色识别、话题建模与论点提取，构建端到端分析流程
- 75%的试点案例中生成的相关论点被判定为有效，准确率高
- 适合政策制定者与项目团队用于预判争议和制定沟通策略
围绕基础设施与能源项目的公共辩论涉及复杂的利益相关方网络、论点与动态叙事。理解这些互动对预见争议和制定参与策略至关重要，但现有媒体智能工具多依赖描述性分析，透明度有限。本文提出Stakeholder Suite，一个在实际场景中部署的统一框架，用于映射公共辩论中的参与者、话题与论点。该系统集成角色检测、话题建模、论点抽取与立场分类，在多个能源基础设施项目上进行测试，实现细粒度、基于来源的洞察，且可适应不同领域。框架在检索精度和立场分类准确率上表现优异，75%的试点案例中生成的论点被评估为相关。除量化指标外，该工具已在实践中有效支持项目团队可视化影响力网络、识别新兴争议，并推动基于证据的决策。
原文摘要 · Abstract (English)
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.
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