ArticleComputers in human behavior2021
Perceptions of blame on social media during the coronavirus pandemic.
Article in Computers in human behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 44 citations in OpenAlex.
- Human Papillomavirus Vaccine Acceptance (HPV-VA) and Vaccine Uptake (HPV-VU): assessing the impact of theory, culture, and trusted sources of information in a Hispanic community.BMC public health · 2023Article
- "I See a lot of Crazy Things and I Don't Know What to Believe": Lessons Learned about Health Literacy and Strategies for Communicating with Vaccine-Hesitant College Students.Healthcare (Basel, Switzerland) · 2023Article
- What Factors Will Influence Chinese International Traveling for Leisure in the Post-COVID-19 Era: Role of Health Priorities and Health-Related Information Literacy.Healthcare (Basel, Switzerland) · 2023Article
- Problematic Use of Social Networks during the First Lockdown: User Profiles and the Protective Effect of Resilience and Optimism.Journal of clinical medicine · 2022Article
- Italians locked down: people's responses to early COVID-19 pandemic public health measures.Humanities & social sciences communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of the coronavirus (COVID-19) disease is overwhelming resources, economies and countries around the world. Millions of people have been infected and hundreds of thousands have succumbed to the virus. Research regarding the coronavirus pandemic is published every day. However, there is limited discourse regarding societal perception. Thus, this paper examines blame attribution concerning the origin and propagation of the coronavirus crisis according to public perception. Specifically, data were extracted from the social media platform Twitter concerning the coronavirus during the early stages of the outbreak and further investigated using thematic analysis. The findings revealed the public predominantly blames national governments for the coronavirus pandemic. In addition, the results documented the explosion of conspiracy theories among social media users regarding the virus' origin. In the early stages of the pandemic, the blame tendency was most frequent to conspiracy theories and restriction of information from the government, whilst in the later months, responsibility had shifted to political leaders and the media. The findings indicate an emerging government mistrust that may result in disregard of preventive health behaviours and the amplification of conspiracy theories, and an evolving dynamic of blame. This study argues for a transparent, continuing dialogue between governments and the public to stop the spread of the coronavirus.
Indexed as
Identifiers
What OpenQuestion holds
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.