SNAP judgments into the digital age: Reporting on food stamps varies significantly with time, publication type, and political leaning

The Supplemental Nutrition Assistance Program (SNAP) is the second-largest and most contentious public assistance program administered by the United States government. The media forums where SNAP discourse occurs have changed with the advent of social and web-based media. We used machine learning techniques to characterize media coverage of SNAP over time (1990–2017), between outlets with national readership and those with narrower scopes, and, for a subset of web-based media, by the outlet’s political leaning. We applied structural topic models, a machine learning methodology that categorizes and summarizes large bodies of text that have document-level covariates or metadata, to a corpus of print media retrieved via LexisNexis (n = 76,634). For comparison, we complied a separate corpus via web-scrape algorithm of the Google News API (2012–2017), and assigned political alignment metadata to a subset documents according to a recent study of partisanship on social media. A similar procedure was used on a subset of the print media documents that could be matched to the same alignment index. Using linear regression models, we found some, but not all, topics to vary significantly with time, between large and small media outlets, and by political leaning. Our findings offer insights into the polarized and partisan nature of a major social welfare program in the United States, and the possible effects of new media environments on the state of this discourse.

补充营养援助计划（Supplemental Nutrition Assistance Program, SNAP）是美国政府管理的规模第二大且最具争议的公共援助项目。随着社交媒体与基于网页的媒体兴起，SNAP相关讨论的传播媒介也发生了变化。本研究借助机器学习技术，对1990年至2017年间的SNAP媒体报道进行特征刻画，对比了全国性受众媒体与受众范围较窄的媒体的报道差异，并针对部分网络媒体，依据媒体的政治倾向开展分类分析。我们通过律商联讯（LexisNexis）数据库检索得到印刷媒体语料库（n=76,634），并运用结构主题模型——一种可对带有文档级协变量或元数据的大规模文本进行分类与摘要的机器学习方法——对其进行分析。为便于对比，我们还通过网页抓取算法结合谷歌新闻应用程序接口（Google News API）构建了2012年至2017年的独立语料库，并依据近期一项社交媒体党派倾向研究的成果，为其中部分文档标注了政治倾向元数据。针对可匹配至同一党派倾向指数的印刷媒体文档子集，我们采用了相同的标注流程。通过线性回归模型分析，我们发现部分（而非全部）主题会随时间推移、媒体规模差异以及政治倾向不同而发生显著变化。本研究结果可为理解美国主要社会福利项目所具有的极化与党派属性，以及新媒体环境对这类讨论现状可能产生的影响提供参考。