Mining social media data to inform public health policies: a sentiment analysis case study
Suzana N. Russell, Lila Rao-Graham, Maurice McNaughton · 2024
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Description
Suzana Russell and two co-authors mine social media feelings to guide health policy, in a 2024 case study from Jamaica. When a call center outbreak hit in April 2020, the state imposed COVID limits. The team gathered over sixteen hundred tweets about the lockdown and ran a machine learning tool that scores tone. The aim was to read public mood fast, without a formal survey.
About seventy-six percent of the tweets read as negative toward the limits. The authors link that sour mood to the low compliance seen with the rules. They argue that live feeling data can help officials shape steps that the public will accept. The method offers a quick, low-cost read on response to health moves. The paper joins a push to add open data streams to policy work.
Catalog record
| CREATOR | Suzana N. Russell, Lila Rao-Graham, Maurice McNaughton |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 17 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |