Public Opinions towards COVID-19 in California and New York on Twitter

  1. Xueting Wang
  2. Canruo Zou
  3. Zidian Xie
  4. Dongmei Li

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Abstract

Background

With the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak.

Objective

This study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States.

Methods

A dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19.

Results

While the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with “protective measures” as the most prevalent topic associated with COVID-19 in both states.

Conclusions

Twitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

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    SciScore for 10.1101/2020.07.12.20151936: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The keywords used to filter locations, such as “Los Angeles, CA”, “Monroe, New York”, are scraped from Wikipedia website (List of cities and towns) [5].
    Wikipedia
    suggested: (Wikipedia, RRID:SCR_004897)
    We applied the vaderSentiment package in python to calculate the sentiment score for each tweet and calculated the average sentiment scores by each day and each hour.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
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    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.07.12.20151936 on medRxiv