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Understanding COVID-19 response by twitter users: A text analysis approach.

Digvijay Pandey¹, Bandinee Pradhan², Wangmo³

¹Department of Technical Education, IET, Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh, Lucknow 226021, India.

|July 25, 2022

Summary

Researchers analyzed 100,000 tweets about the COVID-19 pandemic using natural language processing (NLP). The study found a significant portion of public discussion on Twitter showed positive or mixed sentiments, focusing on public health and cases.

Keywords:

COVID-19 Public health Public opinion Sentiments Twitter

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Area of Science:

Public Health
Computational Social Science
Natural Language Processing

Background:

The COVID-19 pandemic presented an unprecedented global health crisis.
Twitter serves as a critical platform for public discourse and understanding societal responses during health emergencies.

Purpose of the Study:

To analyze public sentiment and identify key themes in Twitter discussions related to the COVID-19 outbreak.
To explore the utility of Twitter data and NLP techniques for real-time public health monitoring.

Main Methods:

Analysis of 100,000 tweets using hashtags related to COVID-19.
Application of programming languages (Python), Google NLP, and NVivo for sentiment and thematic analysis.
Categorization of identified topics into Public Health, COVID-19 globally, and Case/Death numbers.

Main Results:

Sentiment analysis revealed 29.61% positive, 29.49% mixed, 23.23% neutral, and 18.07% negative sentiments.
Popular keywords included "cases", "home", "people", and "help".
Thirty topics were identified and grouped into three main themes.

Conclusions:

Twitter data, analyzed with NLP, offers valuable insights into public sentiment and discussions during health crises like COVID-19.
Real-time analysis of social media can aid in disseminating accurate public health guidelines and combating misinformation.