用点评文本预测餐厅卫生状况，发现效果有限。
Investigating the Association Between Text-Based Indications of Foodborne Illness from Yelp Reviews and New York City Health Inspection Outcomes (2023)
- 用注意力模型分析Yelp评论中的食物中毒迹象
- 评论信号与官方检查评分相关性极低
- 适合关注社会媒体公共卫生监测的学者
食源性疾病是由食用受污染食物引起的胃肠道疾病。餐厅是调查疫情爆发的关键场所，因其在食品采购、加工和分发方面具有共同环节。正式渠道的疾病报告有限，而社交媒体平台则包含大量用户生成内容，可提供及时的公共卫生信号。本文利用分层双曲正切注意力网络（HSAN）分类器分析来自Yelp的评论文本，将其与纽约市卫生局（NYC DOHMH）2023年发布的餐厅检查结果进行对比。研究在普查区层面评估相关性，比较不同C级餐厅占比区域的HSAN得分分布，并绘制纽约市的空间模式。结果发现，HSAN信号与检查评分在普查区层面相关性微弱，且在不同C级餐厅数量的区域间无显著差异。论文讨论了其意义，并提出下一步应开展地址级别分析。
原文摘要 · Abstract (English)
Foodborne illnesses are gastrointestinal conditions caused by consuming contaminated food. Restaurants are critical venues to investigate outbreaks because they share sourcing, preparation, and distribution of foods. Public reporting of illness via formal channels is limited, whereas social media platforms host abundant user-generated content that can provide timely public health signals. This paper analyzes signals from Yelp reviews produced by a Hierarchical Sigmoid Attention Network (HSAN) classifier and compares them with official restaurant inspection outcomes issued by the New York City Department of Health and Mental Hygiene (NYC DOHMH) in 2023. We evaluate correlations at the Census tract level, compare distributions of HSAN scores by prevalence of C-graded restaurants, and map spatial patterns across NYC. We find minimal correlation between HSAN signals and inspection scores at the tract level and no significant differences by number of C-graded restaurants. We discuss implications and outline next steps toward address-level analyses.
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