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Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?

Author

Listed:
  • Menghan Zhang

    (School of Communication, Soochow University, Suzhou 215123, China)

  • Ze Chen

    (School of Communication, Soochow University, Suzhou 215123, China)

  • Xue Qi

    (School of Communication, Soochow University, Suzhou 215123, China)

  • Jun Liu

    (Center for Tracking and Society & Department of Communication, University of Copenhagen, DK-2300 Copenhagen, Denmark)

Abstract

During the COVID-19 pandemic, social media has become an emerging platform for the public to find information, share opinions, and seek coping strategies. Vaccination, one of the most effective public health interventions to control the COVID-19 pandemic, has become the focus of public online discussions. Several studies have demonstrated that social bots actively involved in topic discussions on social media and expressed their sentiments and emotions, which affected human users. However, it is unclear whether social bots’ sentiments affect human users’ sentiments of COVID-19 vaccines. This study seeks to scrutinize whether the sentiments of social bots affect human users’ sentiments of COVID-19 vaccines. The work identified social bots and built an innovative computational framework, i.e., the BERT-CNN sentiment analysis framework, to classify tweet sentiments at the three most discussed stages of COVID-19 vaccines on Twitter from December 2020 to August 2021, thus exploring the impacts of social bots on online vaccine sentiments of humans. Then, the Granger causality test was used to analyze whether there was a time-series causality between the sentiments of social bots and humans. The findings revealed that social bots can influence human sentiments about COVID-19 vaccines. Their ability to transmit the sentiments on social media, whether in the spread of positive or negative tweets, will have a corresponding impact on human sentiments.

Suggested Citation

  • Menghan Zhang & Ze Chen & Xue Qi & Jun Liu, 2022. "Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5566-:d:809283
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    References listed on IDEAS

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    1. Massimo Stella & Emilio Ferrara & Manlio De Domenico, 2018. "Bots increase exposure to negative and inflammatory content in online social systems," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 12435-12440, December.
    2. David Flores-Ruiz & Adolfo Elizondo-Salto & María de la O. Barroso-González, 2021. "Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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