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Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis

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  • Lingyao Li
  • Abdolmajid Erfani
  • Yu Wang
  • Qingbin Cui

Abstract

Reopening amid the COVID-19 pandemic has triggered a battle on social media. The supporters perceived that the lockdown policy could damage the economy and exacerbate social inequality. By contrast, the opponents believed it was necessary to contain the spread and ensure a safe environment for recovery. Anatomy into the battle is of importance to address public concerns, beliefs, and values, thereby enabling policymakers to determine the appropriate solutions to implement reopening policy. To this end, we investigated over 1.5 million related Twitter postings from April 17 to May 30, 2020. With the aid of natural language processing (NLP) techniques and machine learning classifiers, we classified each tweet into either a “supporting” or “opposing” class and then investigated the public perception from temporal and spatial perspectives. From the temporal dimension, we found that both political and scientific news that were extensively discussed on Twitter led to the perception of opposing reopening. Further, being the first mover with full reopen adversely affected the public reaction to reopening policy, while being the follower or late mover resulted in positive responses. From the spatial dimension, the correlation and regression analyses suggest that the state-level perception was very likely to be associated with political affiliation and health value.

Suggested Citation

  • Lingyao Li & Abdolmajid Erfani & Yu Wang & Qingbin Cui, 2021. "Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-23, July.
  • Handle: RePEc:plo:pone00:0254359
    DOI: 10.1371/journal.pone.0254359
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    Cited by:

    1. Jingjing Gao & Gabriela A. Gallegos & Joe F. West, 2023. "Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S," IJERPH, MDPI, vol. 20(21), pages 1-14, October.
    2. Li, Lingyao & Mao, Yujie & Wang, Yu & Ma, Zihui, 2022. "How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis," Journal of Air Transport Management, Elsevier, vol. 105(C).

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