新闻媒体的大规模自动化分析：一种用于肥胖政策研究的新型计算方法。
Large-scale automated analysis of news media: a novel computational method for obesity policy research.
作者信息
Hamad Rita, Pomeranz Jennifer L, Siddiqi Arjumand, Basu Sanjay
机构信息
Division of General Medical Disciplines, Stanford University, Palo Alto, California, USA.
出版信息
Obesity (Silver Spring). 2015 Feb;23(2):296-300. doi: 10.1002/oby.20955. Epub 2014 Dec 17.
OBJECTIVE
Analyzing news media allows obesity policy researchers to understand popular conceptions about obesity, which is important for targeting health education and policies. A persistent dilemma is that investigators have to read and manually classify thousands of individual news articles to identify how obesity and obesity-related policy proposals may be described to the public in the media. A machine learning method called "automated content analysis" that permits researchers to train computers to "read" and classify massive volumes of documents was demonstrated.
METHODS
14,302 newspaper articles that mentioned the word "obesity" during 2011-2012 were identified. Four states that vary in obesity prevalence and policy (Alabama, California, New Jersey, and North Carolina) were examined. The reliability of an automated program to categorize the media's framing of obesity as an individual-level problem (e.g., diet) and/or an environmental-level problem (e.g., obesogenic environment) was tested.
RESULTS
The automated program performed similarly to human coders. The proportion of articles with individual-level framing (27.7-31.0%) was higher than the proportion with neutral (18.0-22.1%) or environmental-level framing (16.0-16.4%) across all states and over the entire study period (P<0.05).
CONCLUSIONS
A novel approach to the study of how obesity concepts are communicated and propagated in news media was demonstrated.
目的
分析新闻媒体能让肥胖政策研究者了解公众对肥胖的普遍认知，这对于确定健康教育和政策目标很重要。一个长期存在的难题是，研究人员必须阅读并手动分类数千篇新闻文章，以确定肥胖及与肥胖相关的政策提案在媒体中可能如何向公众描述。一种名为“自动内容分析”的机器学习方法得以展示，该方法能让研究人员训练计算机“阅读”并分类大量文档。
方法
识别出2011年至2012年期间提到“肥胖”一词的14302篇报纸文章。对肥胖患病率和政策各不相同的四个州（阿拉巴马州、加利福尼亚州、新泽西州和北卡罗来纳州）进行了考察。测试了一个自动程序将媒体对肥胖的框架分类为个人层面问题（如饮食）和/或环境层面问题（如致胖环境）的可靠性。
结果
自动程序的表现与人工编码员相似。在所有州以及整个研究期间，具有个人层面框架的文章比例（27.7 - 31.0%）高于具有中性框架（18.0 - 22.1%）或环境层面框架（16.0 - 16.4%）的文章比例（P<0.05）。
结论
展示了一种研究肥胖概念在新闻媒体中如何传播和扩散的新方法。
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