Abstract & Article Details
Review Article • Vol.6, Issue 7 • ISSN: 2766-2276 • Open Access • CC BY 4.0
Metadata and Sentiment Data Analytics on Social Media Tweets
Abstract
The outbreak of COVID-19 pandemic in early 2020 led to decisions by both the federal and state governments in the United States of America (hereafter, governments) to implement policies about staying at home to reduce the spread of the COVID-19 virus. With the closure of many businesses, people worked from home and schools moved face-to-face classes to online or remote learning. People were concerned about the policies and the impact of such policies on their livelihood. Social media websites such as Twitter (X) were used to voice opinions about the challenges posed by the stay-at-home orders. People expressed positive and negative sentiments about the closures and reopening of offices, schools, restaurants, and other public places as well as the impact of the government stay-at-home policies. This article examines both the positive and negative sentiments expressed using the tweets from the Twitter (X) platform. The metadata and sentimental data collected via the social media site, Twitter (X) for three states – North Carolina, Pennsylvania, and California on the pandemic, stay-at-home and reopening policies were analyzed and discussed. The study adds value to existing literature about COVID-19 in understanding people’s opinions, better information sharing by governments, scientists and others that influence policy decisions in cases of future public health crisis.
Research Topics
How to Cite
Article Information
| Journal | Journal of Biomedical Research & Environmental Sciences (JBRES) |
|---|---|
| ISSN | 2766-2276 |
| DOI | DOI 10.37871/jbres2143 |
| Volume / Issue | Vol. 6, Issue 7 |
| Received | June 21, 2025 |
| Accepted | July 15, 2025 |
| Published | July 16, 2025 |
| Article Type | Review Article |
| Pages | 884-890 |
| License | CC BY 4.0 — Open Access |
| Publisher | SciRes Literature LLC, Sheridan, WY, USA |
| Language | English |
Published under CC BY 4.0 — free to share, copy, adapt, and redistribute with attribution.