用新闻文本分析社区特征，为健康研究提供新视角。
Perceptions of Edinburgh: Capturing Neighbourhood Characteristics by Clustering Geoparsed Local News
- 通过本地化街级地理标注与全文聚类结合，挖掘新闻中的社区信息。
- 提取的主题在定性与定量上均符合现实，验证了有效性。
- 适合关注城市健康、社会不平等与空间研究的学者使用。
我们所生活的社区以复杂且难以定义的方式影响着健康。然而，对地方性过程如何影响健康与不平等的理解仍十分有限，这制约了有效政策干预的发展。新闻媒体提供了可被用于健康研究的社会与社区信息。本文提出一种基于本地新闻文章的社区特征表征方法，具体而言，展示如何利用自然语言处理（NLP）技术，通过分析、地理标注与聚类新闻文章来解锁更多关于社区的信息。本研究的创新之处在于将针对本地的街级地理标注与全文新闻聚类相结合，实现对社区特征的更细致分析。我们评估了输出结果，通过定性与定量双重证据表明，从新闻中提取的主题合理且反映真实世界特征。这一成果具有重要意义，使我们能更好地理解社区对健康的影响。基于新闻数据的社区特征研究成果，将支持新一代基于地点的研究，拓展对一系列空间过程及其对健康影响的考察，推动新的流行病学研究。
原文摘要 · Abstract (English)
The communities that we live in affect our health in ways that are complex and hard to define. Moreover, our understanding of the place-based processes affecting health and inequalities is limited. This undermines the development of robust policy interventions to improve local health and well-being. News media provides social and community information that may be useful in health studies. Here we propose a methodology for characterising neighbourhoods by using local news articles. More specifically, we show how we can use Natural Language Processing (NLP) to unlock further information about neighbourhoods by analysing, geoparsing and clustering news articles. Our work is novel because we combine street-level geoparsing tailored to the locality with clustering of full news articles, enabling a more detailed examination of neighbourhood characteristics. We evaluate our outputs and show via a confluence of evidence, both from a qualitative and a quantitative perspective, that the themes we extract from news articles are sensible and reflect many characteristics of the real world. This is significant because it allows us to better understand the effects of neighbourhoods on health. Our findings on neighbourhood characterisation using news data will support a new generation of place-based research which examines a wider set of spatial processes and how they affect health, enabling new epidemiological research.
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