Three dimensions of COVID‐19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis

  1. Shan Qiao
  2. Zhenlong Li
  3. Chen Liang
  4. Xiaoming Li
  5. Caroline Rudisill

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Abstract

Social media analysis provides an alternate approach to monitoring and understanding risk perceptions regarding COVID‐19 over time. Our current understandings of risk perceptions regarding COVID‐19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) as the pandemic has evolved. Data are also limited regarding the impact of social determinants of health (SDOH) on COVID‐19‐related risk perceptions over time. To address these knowledge gaps, we extracted tweets regarding COVID‐19‐related risk perceptions and developed indicators for the three dimensions of risk perceptions based on over 502 million geotagged tweets posted by over 4.9 million Twitter users from January 2020 to December 2021 in the United States. We examined correlations between risk perception indicator scores and county‐level SDOH. The three dimensions of risk perceptions demonstrate different trajectories. Perceived severity maintained a high level throughout the study period. Perceived susceptibility and negative emotion peaked on March 11, 2020 (COVID‐19 declared global pandemic by WHO) and then declined and remained stable at lower levels until increasing once again with the Omicron period. Relative frequency of tweet posts on risk perceptions did not closely follow epidemic trends of COVID‐19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention to perceived severity and susceptibility of COVID‐19 than those from wealthier counties. Examining trends in tweets regarding the multiple dimensions of risk perceptions throughout the COVID‐19 pandemic can help policymakers frame in‐time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in the United States.

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  1. Version published to 10.1111/risa.13993
  2. ScreenIT

    SciScore for 10.1101/2021.01.27.21250654: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: We detected the following sentences addressing limitations in the study:
    (Odlum et al., 2020) This study has several methodological limitations that require attention in interpreting and generalizing from findings. First, we need to be cautious about the representativeness of Twitter users. Twitter is not universally used in the United States, particularly among older and low-income populations. In addition, not all Twitter users share their geolocation information. Therefore, those who geotag their tweets are not representative of the wider Twitter population. (Jiang, Li, & Ye, 2019) Second, we used index scores as proximal indicators to quantify people’s attention (relative frequency of tweet posts) to the three domains of the risk perceptions. We did not use existing validated measure instruments to assess the level of perceived severity, susceptibility, or negative emotions. Third, the keyword-based tweets retrieval method may miss a small number of relevant tweets that that did not include common language regarding risk perceptions. Specifically, keywords-based methods only capture tweets with an exact match of terms. Indirect mentions of risk-perception terms and subtle cues may be missed because human natural language is rich and dynamic. Text-mining methods, such as topic modeling will be needed as a supplement to further strengthen the understanding of people’s opinions. Fourth, in terms of emotion, we only examined negative emotional reaction to COVID-19 in the analysis. According to some theoretical frameworks, self-efficacy and resilie...

    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:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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.

    About SciScore

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  3. Version published to 10.1101/2021.01.27.21250654v1 on medRxiv