ArticleInternational journal of environmental research and public health2020
Social Bots' Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis.Journal of medical Internet research · 2025Article
- Quantifying Bot Impact: An Information-Theoretic Analysis of Complexity and Uncertainty in Online Political Communication Dynamics.Entropy (Basel, Switzerland) · 2025Article
- A thematic analysis of UK COVID-19 vaccine hesitancy discussions on Twitter.BMC public health · 2025Article
- Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.JMIR infodemiology · 2024Article
- The Influence of Social Media and Institutional Trust on Vaccine Hesitancy in France: Examining Direct and Mediating Processes.Vaccines · 2023Article
- Social Bots' Role in the COVID-19 Pandemic Discussion on Twitter.International journal of environmental research and public health · 2023Article
- Sentiment Analysis on Twitter: Role of Healthcare Professionals in the Global Conversation during the AstraZeneca Vaccine Suspension.International journal of environmental research and public health · 2023Article
- Analysis of Healthcare Professionals' and Institutions' Roles in Twitter Colostomy Information.Healthcare (Basel, Switzerland) · 2023Article
- Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- Bots' Activity on COVID-19 Pro and Anti-Vaccination Networks: Analysis of Spanish-Written Messages on Twitter.Vaccines · 2022Article
- Mental health-related communication in a virtual community: text mining analysis of a digital exchange platform during the Covid-19 pandemic.BMC psychiatry · 2022Article
- Online information analysis on pancreatic cancer in Korea using structural topic model.Scientific reports · 2022Article
- Social Bots' Involvement in the COVID-19 Vaccine Discussions on Twitter.International journal of environmental research and public health · 2022Article
- Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine.Journal of medical Internet research · 2022Article
- Botometer 101: social bot practicum for computational social scientists.Journal of computational social science · 2022Article
- A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic.Journal of computational social science · 2022Article
- Understanding the emotional response to COVID-19 information in news and social media: A mental health perspective.Human behavior and emerging technologies · 2021Review
- Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts.JMIR public health and surveillance · 2021Article
- Accuracy of health-related information regarding COVID-19 on Twitter during a global pandemic.World medical & health policy · 2021Article
- Bots and Misinformation Spread on Social Media: Implications for COVID-19.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.