从实体提及到语气：基于大语言模型的媒体偏见分析流水线
From Entity Mentions to Tone: An LLM-Based Pipeline for Media Bias Analysis
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中文总结 AI 辅助
该研究提出一种基于大语言模型的媒体偏见分析流水线，对8358篇阿尔巴尼亚新闻文章开展实验，验证了其在偏见分析中的有效性，适用于手动验证数据集或专用语言工具有限的场景。
中文摘要 AI 辅助
本文提出了一种用于分析在线新闻中媒体偏见与框架的流水线。该流水线将文章分组为主题和事件，添加命名实体和情感注释，并通过人物提及、源级语气和事件级报道模式对新闻来源进行比较。我们将其应用于从GDELT收集的8358篇阿尔巴尼亚新闻文章，并将所得注释与GDELT的自动注释进行比较。结果显示情感和实体提取存在中等一致性，还有额外的人物-实体对可潜在支持偏见分析。我们比较了两种注释提示，发现更严格的情感验证规则消除了标签-分数不一致性，但增加了执行时间并降低了注释覆盖率。基于这些结果，其余分析使用更简单的提示。我们提供了源级框架概况、不同来源的人物级语气差异以及事件级把关和报道指标的示例分析，这些输出展示了同一新闻集合如何用于研究各来源的报道内容、对公众人物的描述方式以及报道集中的位置。该方法在手动验证数据集或专用语言工具有限的场景中特别有用。
英文摘要
This paper presents a pipeline for analyzing media bias and framing in online news. The pipeline groups articles into topics and events, adds named-entity and sentiment annotations, and compares news sources through people mentions, source-level tone, and event-level coverage patterns. We apply it to 8,358 Albanian news articles collected from GDELT and compare the resulting annotations with GDELT's automated annotations. The results show moderate agreement for sentiment and entity extraction, as well as additional person-entity pairs that can potentially support the bias analysis. We compare two annotation prompts and find that stricter sentiment-validation rules remove label-score inconsistencies but increase execution time and reduce annotation coverage. Based on these results, the simpler prompt is used for the rest of the analysis. We have provided sample analysis on source-level framing pro les, person-level tone differences across sources, and event-level gatekeeping and coverage indicators. These outputs show how the same news collection can be used to examine what sources cover, how they describe public figures, and where coverage is concentrated. The approach is particularly useful in settings where manually verified datasets or specialized language tools are limited.
发表机构
- University of Tirana（地拉那大学）
机构由 AI 辅助整理，请以论文原文为准。
