The COVID-19 pandemic revealed gaps in infected case reporting due to self-testing for infection and lack of transparency regarding the spread and origin of the disease. Therefore, an alternative method for infectious disease detection that operates independently of official or patient reports, such as scanning social media for posts discussing infection symptoms, would advance epidemiologic surveillance. I will evaluate the potential of Reddit posts to determine the prevalence of influenza-like-illness (ILI) cases as an indicator of influenza infection in the Western United States during the 2024-2025 influenza season; through this project, I hope to validate social media as an accessible, time-efficient way to supplement traditional surveillance methods.

I will train a classification model able to differentiate between posts suggesting the Redditor has ILI from those that indicate other infections or neutrally discuss ILI from sickness-related subreddits (e.g. r/sick, r/flu). I will apply my model to subreddits indicating geographic location (e.g. r/California, r/SanJose) for each 3-month time frame between the start of October 2024 and end of March 2025. After mapping the relative prevalence of ILI per state and comparing with government-issued maps and statistics, I can gauge the potential of Reddit as an informal source for syndromic surveillance. An understanding of using such informal sources would fill gaps in traditional surveillance methods.