用事件指纹自动识别全球新闻，高效分析灾害与恐袭报道差异。
Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks
- 基于时间、地点、事件类型构建事件指纹，无需训练数据
- 2020年识别出470起事件的27,441篇跨语言新闻
- 适用于媒体监测与跨国报道研究，支持开放数据共享
由于跨语言事件新闻识别方法依赖难以扩展的专业知识，比较研究困难。本文提出FAME（FINGERPRINT TO ARTICLE MATCHING FOR EVENTS）方法，利用事件指纹——即事件发生的时间、地点和类别（如风暴或洪水）——自动高效识别关键事件相关新闻。该方法无需训练数据，在包含数千万篇文章的多语言数据库中表现优异，成功识别2020年全球470起自然灾害与恐怖袭击事件的27,441篇相关报道。数据来源包括MediaCloud多语言新闻库及EM-DAT、USGS、GTD三个专家标注事件数据库。案例研究发现：媒体报道强度与死亡人数、事发国GDP及报道国与事发国贸易量呈正相关。研究公开了NLP标注结果与跨国媒体关注度数据，支持学术界与媒体监控组织使用。
原文摘要 · Abstract (English)
Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.
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