ArticleRevista panamericana de salud publica = Pan American journal of public health2024
Mining social media data to inform public health policies: a sentiment analysis case study.
Article in Revista panamericana de salud publica = Pan American journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sentiment analysis in public health: a systematic review of the current state, challenges, and future directions.Frontiers in public health · 2025Pooled it
- FDA authorisation of Zyn nicotine pouches: cross-sectional analysis of public discourse on X.Tobacco control · 2026Article
- A hybrid deep learning model for sentiment analysis of COVID-19 tweets with class balancing.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.