| Literature DB >> 34162357 |
Priscila Biancovilli1, Lilla Makszin2,3, Claudia Jurberg4,5.
Abstract
BACKGROUND: One of the challenges posed by the novel coronavirus pandemic is the infodemic risk, that is, a huge amount of information being published on the topic, along with misinformation and rumours; with social media, this phenomenon is amplified, and it goes faster and further. Around 100 million people in Brazil (50% of the inhabitants) are users of social media networks - almost half of the country's population. Most of the information on the Internet is unregulated, and its quality remains questionable.Entities:
Keywords: Brazil; COVID-19; Coronavirus; Fact check; Misinformation; Pandemic; Politics; Social media
Year: 2021 PMID: 34162357 PMCID: PMC8220426 DOI: 10.1186/s12889-021-11165-1
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Fig. 1Characteristics of misinformation about COVID-19 in Brazil, between January 1, 2020 and July 4, 2020
Relationship between content classification and type of rumour among all 232 pieces of misinformation analysed
| Real life stories | Conspiracy theories | Health Tips | Scientific/epidemiologic data | Virtual scams | Warnings | Politics | Total | |||
|---|---|---|---|---|---|---|---|---|---|---|
| Type of rumour | Satire or Parody | Frequency | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| % | 0,0% | 0,0% | 0,0% | 0,0% | 11,1% | 0,0% | 0 | 0,4% | ||
| Adjusted Residual | -,9 | -,2 | -,2 | -,4 | 5,1 | -,2 | -,5 | |||
| Misleading, imposter, manipulated content | Frequency | 5 | 1 | 1 | 14 | 0 | 2 | 3 | 26 | |
| % | 4,3% | 10,0% | 7,1% | 43,8% | 0,0% | 28,6% | 5,4% | 10,7% | ||
| Adjusted Residual | −3,0 | -,1 | -,4 | 6,5 | −1,1 | 1,6 | −1,5 | |||
| Fabricated content | Frequency | 44 | 7 | 13 | 17 | 8 | 5 | 36 | 130 | |
| % | 38,3% | 70,0% | 92,9% | 53,1% | 88,9% | 71,4% | 64,3% | 53,5% | ||
| Adjusted Residual | −4,5 | 1,1 | 3,0 | ,0 | 2,2 | 1,0 | 1,8 | |||
| False connection or false context | Frequency | 66 | 2 | 0 | 1 | 0 | 0 | 17 | 86 | |
| % | 57,4% | 20,0% | 0,0% | 3,1% | 0,0% | 0,0% | 30,4% | 35,4% | ||
| Adjusted Residual | 6,8 | −1,0 | −2,9 | −4,1 | −2,3 | −2,0 | -,9 | |||
| Total | Frequency | 115 | 10 | 14 | 32 | 9 | 7 | 56 | 243 | |
| % | 100,0% | 100,0% | 100,0% | 100,0% | 100,0% | 100,0% | 100,0% | 100,0% | ||
Relationship between sentiment and content classification among all 232 pieces of misinformation analysed
| Real life stories | Conspiracy theories | Health Tips | Scientific/epidemiologic data | Virtual scams | Warnings | Politics | Total | |||
|---|---|---|---|---|---|---|---|---|---|---|
| Sentiment | Negative | Frequency | 75 | 9 | 0 | 13 | 1 | 6 | 43 | 147 |
| % | 65,2% | 90,0% | 0,0% | 40,6% | 11,1% | 85,7% | 76,8% | 60,5% | ||
| Adjusted Residual | 1,4 | −1,9 | −4,8 | −2,5 | −3,1 | 1,4 | 2,8 | |||
| Neutral | Frequency | 25 | 1 | 9 | 11 | 5 | 1 | 9 | 61 | |
| % | 21,7% | 10,0% | 64,3% | 34,4% | 55,6% | 14,3% | 16,1% | 25,1% | ||
| Adjusted Residual | −1,1 | − 1,1 | 3,5 | 1,3 | 2,1 | -,7 | −1,8 | |||
| Positive | Frequency | 15 | 0 | 5 | 8 | 3 | 0 | 4 | 35 | |
| % | 13,0% | 0,0% | 35,7% | 25,0% | 33,3% | 0,0% | 7,1% | 14,4% | ||
| Adjusted Residual | -,6 | −1,3 | 2,3 | 1,8 | 1,6 | −1,1 | − 1,8 | |||
| Total | Frequency | 115 | 14 | 32 | 9 | 7 | 56 | 243 | ||
| % | 100,0% | 10 | 100,0% | 100,0% | 100,0% | 100,0% | 100,0% | 100,0% | ||
Fig. 2Number of fact-checked misinformation items detected by Lupa agency, since the first case recognised on February 26, 2020 (week 9) until the week 27, in July 4, 2020
Fig. 3Number of new diagnosed coronavirus cases per week in the same period