ArticleThe American behavioral scientist2021
The "Parallel Pandemic" in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media.
Article in The American behavioral scientist, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
13 citing papers in PubMed.
- Survival analysis of the duration of rumors during the COVID-19 pandemic.BMC public health · 2024Article
- Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China's Exit from the Zero-COVID Policy: Retrospective Infoveillance Study.Journal of medical Internet research · 2023Article
- Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects.Computers in human behavior · 2023Article
- Supply Chain Risk Diffusion in Partially Mapping Double-Layer Hypernetworks.Entropy (Basel, Switzerland) · 2023Article
- "Help Us!": a content analysis of COVID-19 help-seeking posts on Weibo during the first lockdown.BMC public health · 2023Article
- Attitudes and perceptions towards public health safety measures during a global health crisis: Social and personal consequences.PloS one · 2023Article
- How local outbreak of COVID-19 affect the risk of internet public opinion: A Chinese social media case study.Technology in society · 2022Article
- Health-Related Rumor Control through Social Collaboration Models: Lessons from Cases in China during the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2022Article
- Factors influencing fake news rebuttal acceptance during the COVID-19 pandemic and the moderating effect of cognitive ability.Computers in human behavior · 2022Article
- Interaction between official institutions and influential users of rumor control in online social networks.Frontiers in psychology · 2022Article
- Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China.Telematics and informatics · 2021Article
- Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach.Journal of medical Internet research · 2021Article
- Navigating the infodemic: A qualitative study of university students' information strategies during the COVID-19 pandemic.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.
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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.