Results showed that all four attributes increased the likelihood of self-identification, with the strongest drivers being the number of reported illnesses and inclusion of symptoms such as nausea, vomiting, and diarrhea. Specifically, the odds of self-identifying as a victim of a foodborne illness outbreak more than doubled when the number of people publicized as ill reached 8,500 or when symptoms were reported.

Based on their findings, the researchers underscore that publicizing unconfirmed signals of foodborne illness can lead to misattribution and overreporting, driven by psychological and social factors—a critical limitation of NDS-based outbreak detection. A notable example of this phenomenon occurred in 2022 when a crowdsourced platform flagged dry breakfast cereal as a potential source of illness, prompting media coverage and thousands of self-reports, despite subsequent investigations finding no link.

Although the study highlights limitations of NDSs and their ability to amplify false signals, it provides valuable information about which attributes people respond to in a publicized outbreak headline. Future research focused on message development could help public health authorities develop messages that maximize the likelihood of garnering responses to a call to action (e.g., reporting illnesses to the appropriate health agency).

Overall, the researchers advocate for reporting systems managed by regulatory agencies including rapid confirmation protocols to prevent premature announcements that could harm consumer trust and industry reputation. They emphasize that, as digital tools evolve, balancing timeliness with accuracy will be essential for effective outbreak management and food safety protection.

The study, published in the Journal of Food Protection, was conducted by researchers from Concordia University—Wisconsin, the University of Minnesota, and the University of Washington.