Restaurants are critical venues at which to investigate foodborne illness outbreaks due to shared sourcing, preparation, and distribution of foods. Formal channels to report illness after food consumption, such as 311, New York City's non-emergency municipal service platform, are underutilized. Given this, online social media platforms serve as abundant sources of user-generated content that provide critical insights into the needs of individuals and populations. We extracted restaurant reviews and metadata from Yelp to identify potential outbreaks of foodborne illness in connection with consuming food from restaurants. Because the prevalence of foodborne illnesses may increase in warmer months as higher temperatures breed more favorable conditions for bacterial growth, we aimed to identify seasonal patterns in foodborne illness reports from 311 and identify seasonal patterns of foodborne illness from Yelp reviews for New York City restaurants using a Hierarchical Sigmoid Attention Network (HSAN). We found no evidence of significant bivariate associations between any variables of interest. Given the inherent limitations of relying solely on user-generated data for public health insights, it is imperative to complement these sources with other data streams and insights from subject matter experts. Future investigations should involve conducting these analyses at more granular spatial and temporal scales to explore the presence of such differences or associations.

翻译：餐馆是调查食源性疾病暴发的关键场所，因其涉及食品的采购、制备和分发共享渠道。纽约市非紧急市政服务平台311等正式疾病报告渠道未能得到充分利用。鉴于此，社交媒体平台作为用户生成内容的丰富来源，可提供关于个体及群体需求的深度见解。我们通过提取Yelp平台餐厅评论及元数据，识别与餐厅消费相关的潜在食源性疾病暴发事件。鉴于气温升高为细菌繁殖创造更有利条件可能导致食源性疾病在暖季多发，本研究旨在运用分层Sigmoid注意力网络识别纽约市餐厅311报告的食源性疾病季节模式及Yelp评论中的季节特征。结果未发现任何关注变量间存在显著双变量关联。鉴于仅依赖用户生成数据获取公共卫生洞察存在固有局限性，必须结合其他数据源与领域专家见解进行补充。未来研究应在更精细的时空尺度上开展此类分析，以探索此类差异或关联的存在性。