Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Wen Shi, Diyi Liu, Jing Yang, Jing Zhang, Sanmei Wen and Jing Su
Additional contact information
Wen Shi: Department of Earth System Science, Tsinghua University, Beijing 100084, China
Diyi Liu: School of Journalism and Communication, Renmin University of China, Beijing 100084, China
Jing Yang: School of Journalism and Communication, Tsinghua University, Beijing 100084, China
Jing Zhang: School of Journalism and Communication, Tsinghua University, Beijing 100084, China
Sanmei Wen: Center for International Communication Studies, Tsinghua University, Beijing 100084, China
Jing Su: School of Humanities, Tsinghua University, Beijing 100084, China

IJERPH, 2020, vol. 17, issue 22, 1-18

Abstract: During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

Keywords: social bots; social media; sentiment analysis; COVID-19 pandemic; health emergency (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/22/8701/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/22/8701/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:22:p:8701-:d:449714

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().