Leveraging Multiyear, Geospatial Social Media Data for Health Policy Evaluations: Lessons from the Philadelphia Beverage Tax

Context: Public reactions to health policies are vital to understand policy sustainability and impact but have been elusively difficult to dynamically measure. The 2021 launch of the Twitter Academic Application Programming Interface (API), allowing for historical tweet analyses, represents a potentially powerful tool for complex, comprehensive policy analyses. Objective: Using the Philadelphia Beverage Tax (implemented January 2017) as a case study, this research extracted longitudinal and geographic changes in sentiments, and key influencers in policy-related conversations. Design: The Twitter API was used to retrieve all publicly available tweets related to the Tax between 2016 and 2019. Setting: Twitter. Participants: Users who posted publicly available tweets related to the Philadelphia Beverage Tax (PBT). Main Outcome: Tweet content, frequency, sentiment, and user-related information. Measures: Tweet content, authors, engagement, and location were analyzed in parallel to key PBT events. Published emotional lexicons were used for sentiment analyses. Results: A total of 45 891 tweets were retrieved (1311 with geolocation data). Changes in the tweet volume and sentiment were strongly driven by Tax-related litigation. While anger and fear increased in the months prior to the policy's implementation, they progressively decreased after its implementation; trust displayed an inverse trend. The 50 tweeters with the highest positive engagement included media outlets (n = 24), displaying particularly high tweet volume/engagement, and public personalities (n = 10), displaying the greatest polarization in tweet sentiment. Most geo-located tweets, reflecting 321 unique locations, were from the Philadelphia region (55.2%). Sentiment and positive engagement varied, although concentrations of negative sentiments were observed in some Philadelphia suburbs. Conclusions: Findings highlighted how longitudinal Twitter data can be leveraged to deconstruct specific, dynamic insights on public policy reactions and information dissemination to inform better policy implementation and evaluation (eg, anticipating catalysts for both heightened public interest and geographic, sentiment changes in policy conversations). This study provides policymakers a blueprint to conduct similar cost and time efficient yet dynamic and multifaceted health policy evaluations.