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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis.
Usman Naseem1, Imran Razzak2, Matloob Khushi1
1School of Computer ScienceThe University of Sydney Ultimo NSW 2006 Australia.
IEEE Transactions on Computational Social Systems
|July 5, 2022
Summary
Early social media data reveals public panic and evolving opinions during the COVID-19 pandemic. Analysis of 90,000 tweets highlights the need for proactive public health communication to combat misinformation.
Area of Science:
- Public Health
- Social Media Analysis
- Computational Social Science
Background:
- The COVID-19 pandemic triggered widespread social media activity, leading to concerns about mass hysteria and misinformation.
- Assessing early information dissemination and public opinion shifts on social media is crucial for understanding pandemic responses.
Purpose of the Study:
- To analyze early information flows and public sentiment regarding COVID-19 on social media.
- To inform policy decisions on social media moderation and public health communication strategies.
Main Methods:
- Creation of a large-scale sentiment dataset (COVIDSENTI) with 90,000 COVID-19-related tweets from February-March 2020.
- Sentiment classification of tweets into positive, negative, and neutral categories using various features and classifiers.
Main Results:
- Negative opinions significantly influenced public sentiment during the early pandemic.
- Initial public support for lockdowns observed, with sentiment shifting by mid-March.
- The COVIDSENTI dataset provides valuable insights into early pandemic public opinion.
Conclusions:
- A proactive and agile public health presence is essential to counter negative sentiment on social media during pandemics.
- Effective social media moderation policies are needed to address misinformation.
- Understanding public opinion dynamics on social media is key to pandemic management.
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