  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.
