Mining social media data to inform public health policies: a sentiment analysis case study.

Abstract

In the face of growing health challenges, nontraditional sources of data, such as open data, have the potential to transform how decisions are made and used to inform public health policies. Focusing on the COVID-19 pandemic, this article presents a case study employing sentiment analysis on unstructured social media data from Twitter (now X) to gauge public sentiment regarding pandemic-related restrictions. Our study aims to uncover and analyze Jamaican citizens' emotions and opinions surrounding COVID-19 restrictions following an outbreak at a call center in April 2020. Machine learning sentiment analysis was used to analyze tweets from Twitter related to the lockdown. A total of 1 609 tweets were retrieved and analyzed, 76% of which expressed negative sentiments, suggesting that the majority of citizens were not in favor of the restrictions. The low compliance with the government-mandated policy may be related to the high percentage of negative sentiments expressed. Insights from citizens' sentiments derived from open data sources such as Twitter can serve as valuable indicators for public health policymakers, providing critical input that will aid in tailoring interventions that align with public sentiments, thereby enhancing the effectiveness of and compliance with public health policies. This type of analysis can be useful to the health community and more generally to governments, as it allows for a more scientific assessment of public response to public health intervention techniques in real time. This study contributes to the emerging discourse on the integration of nontraditional data into public health policy-making, highlighting the growing potential for the use of these novel analytic techniques in addressing complex public health challenges.