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

Menghan Zhang, Ze Chen, Xue Qi and Jun Liu
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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

Sustainability, 2022, vol. 14, issue 9, 1-16

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.

Keywords: social bots; sentimental engagement; COVID-19; sentiment analysis; Twitter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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