用大模型和知识图谱分析新闻中的政治立场，提升观点识别准确率。
Integrating Large Language Models and Knowledge Graphs to Capture Political Viewpoints in News Media
- 用微调的大模型识别新闻观点，结合维基数据增强实体语义描述。
- 在英国移民议题上，融合方法比单一策略性能更优，长文本模型效果最佳。
- 适合关注媒体偏见分析、信息公平性的研究者与政策制定者。
新闻媒体在民主社会中通过特定话题、观点和声音塑造政治与社会讨论。理解这些动态对于评估媒体是否提供了公正平衡的公共辩论至关重要。此前工作提出了一种混合人机流程：给定新闻语料库后，首先识别某议题下表达的观点范围，其次将相关主张归类至已定义的观点集合（如‘移民对英国经济有积极影响’）。本文改进该流程：一是对大型语言模型（LLMs）进行微调以实现观点分类；二是利用维基数据（Wikidata）为相关主体补充语义描述，丰富主张表示。我们在聚焦英国移民争论的基准上评估该方法。结果表明，两种机制单独使用均能提升分类性能，而联合使用效果最佳，尤其当采用支持长输入的LLM时表现突出。
原文摘要 · Abstract (English)
News sources play a central role in democratic societies by shaping political and social discourse through specific topics, viewpoints and voices. Understanding these dynamics is essential for assessing whether the media landscape offers a balanced and fair account of public debate. In earlier work, we introduced a pipeline that, given a news corpus, i) uses a hybrid human-machine approach to identify the range of viewpoints expressed about a given topic, and ii) classifies relevant claims with respect to the identified viewpoints, defined as sets of semantically and ideologically congruent claims (e.g., positions arguing that immigration positively impacts the UK economy). In this paper, we improve this pipeline by i) fine-tuning Large Language Models (LLMs) for viewpoint classification and ii) enriching claim representations with semantic descriptions of relevant actors drawn from Wikidata. We evaluate our approach against alternative solutions on a benchmark centred on the UK immigration debate. Results show that while both mechanisms independently improve classification performance, their integration yields the best results, particularly when using LLMs capable of processing long inputs.
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