Science For All? Relating Actors, Links, and Discourses wit (Fake) Scientific Claims About COVID-19 on Twitter
Canadian Journal of Communication, ISSN: 1499-6642, Vol: 48, Issue: 3, Page: 581-608
2023
- 1Citations
- 6Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Policy Citations1
- Policy Citation1
- Captures6
- Readers6
Article Description
Background: This article looks at discourses using alleged scientific sources to support or oppose political positions on the COVID-19 pandemic in Brazil. Analysis: The authors analyzed more than 3.3 million tweets, sorted according to linguistic rules, from a broader database of tweets related to the pandemic. The focus of this analysis was tweets containing affirmations, allusions, or questionings allegedly referring to scientific studies and hypotheses or authoritative sources in order to legitimize a position as being based on scientific truth. Conclusion and implication: The study shows that scientific sources are largely mobilized in networks of information and disinformation and are heavily present in a vast proportion of anti-science and negationist arguments.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know