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Between Monday 15 March 2021 and Monday 22 March 2021, misinformation about Authorities has increasead whereas misinformation about Vaccine has reduced.

The Fact-checking Observatory is an automatic service that collects misinforming content on Twitter using URLs that have been identified as potential misinformation by fact-checking websites. Using this data, the Fact-checking Observatory automatically generates weekly reports that updates the state of misinformation spread of fact-checked misinformation on Twitter.

This analysis is limited to URLs identified by Fact-checking organisations. The collected data only consist of non-blocked Twitter content and may be incomplete.

This report updates the status of misinformation spread between Monday 15 March 2021 and Monday 22 March 2021.

268,798 Misinforming Tweets
New:+374 Trend:+157
128,562 Fact-checking Tweets
New:+675 Trend:+94
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 15 March 2021 and Monday 22 March 2021, 374 new URLs have been identified as potential misinforming content. Out of the 9 topics identified by Fact-checking organisations (Figure 1), most of the new shared URLs were about Authorities with an increase of +425 compared to the previous total spread for the same topic. The topic that saw the least increase in spread compared to the previous period total spread was Face Mask with a change of +0 compared to the previous total spread for the same topic.

The topics used for the analysis are obtained from the COVID-19 specific fact-check alliance database and are defined as follows:

  1. Authorities: Information relating to government or authorities communication and general involvement during the COVID-19 pandemic (e.g., crime, government, aid, lockdown).
  2. Causes: Information about the virus causes and outbreaks (e.g., China, animals).
  3. Conspiracy theories: COVID-19-related conspiracy theories (e.g., 5G, biological weapon).
  4. Cures: Information about potential virus cures (e.g., vaccines, hydroxychloroquine, bleach).
  5. Spread: Information relating to the spread of COVID-19 (e.g., travel, animals).
  6. Symptoms: Information relating to symptoms and symptomatic treatments of COVID-19 (e.g., cough, sore throat).
  7. Other: Any topic that does not fit directly the aforementioned categories.

In relation to the previous week, the topic that saw the biggest relative spread change was Authorities with a change of +125 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Authorities with a change of -25 compared to the previous period.

The all time most important topic is Authorities with a total of 120,437 URL shares and the least popular topic is Face Mask with 1 shares (Figure 2).

Figure 1: Topic Importance.

Figure 2: Amount of topic shares per week.

The top misinforming content and fact-checking articles shared since the last report are listed in Table 1 and Table 2.

Misinforming URL Fact-check URL Topic Current Week Previous Week Total
https://www.worldometers.info/ Agencia Ocote Authorities 198 129 31118
https://www.frontliner.com.br/oms-condena-lockdown-nao-salva-vidas-e-torna-os-pobres-muito-mais-pobres/ Estadão Verifica Other 103 32 770
https://youtu.be/C88Pb7v1NTI VoxCheck Conspiracy Theory 23 0 32
https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm Détecteur de rumeurs Other 12 4 1611
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 8 4 1946
https://www.nature.com/articles/d41586-020-00548-w Maldita.es Causes 3 1 727
http://dilyana.bg/project-g-2101-pentagon-biolab-discovered-mers-and-sars-like-coronaviruses-in-bats/ Myth Detector Conspiracy Theory 3 0 107
https://www.lastampa.it/esteri/2020/05/04/news/test-sul-sangue-effettuati-in-giappone-rivela-la-mortalita-da-coronavirus-e-di-gran-lunga-inferiore-all-influenza-1.38801430 Open Other 2 7 623
https://www.facebook.com/watch/?v=589595028569342 LeadStories Conspiracy Theory 2 4 51
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 2 2 3760

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.factcheck.org/2021/02/biden-hasnt-reduced-covid-19-testing-at-the-border/ Authorities 46 21 152
https://piaui.folha.uol.com.br/lupa/2020/07/01/verificamos-stf-bolsonaro-covid/ Authorities 45 28 926
https://politica.estadao.com.br/blogs/estadao-verifica/para-atacar-lockdown-blog-tira-de-contexto-entrevista-de-representante-da-oms/ Other 22 11 87
https://politica.estadao.com.br/blogs/estadao-verifica/frase-de-enviado-da-oms-e-retirada-de-contexto-para-sugerir-que-entidade-condena-lockdown/ Authorities 12 13 142
https://piaui.folha.uol.com.br/lupa/2020/11/06/verificamos-tratamento-precoce-covid-19/ Cure 12 2 29
https://www.factcheck.org/2020/07/old-photo-shows-obama-fauci-at-u-s-facility-not-wuhan-lab/ Conspiracy Theory 11 2 355
https://piaui.folha.uol.com.br/lupa/2020/05/13/verificamos-hospital-2016/ Other 11 1 34
https://piaui.folha.uol.com.br/lupa/2020/10/02/verificamos-desvio-verbas-covid/ Authorities 11 0 26
https://piaui.folha.uol.com.br/lupa/2021/01/22/verificamos-ivermectina-liverpool/ Cure 10 17 118
https://www.aosfatos.org/noticias/e-falso-que-stf-afastou-bolsonaro-e-deu-governadores-controle-de-acoes-contra-pandemia/ Authorities 9 3 18

Table 2: Top fact-checked content.


The data used for creating the Twitter dataset is obtained from the Poynter Coronavirus Fact Alliance. The alliance consists of 98 fact-checking organisation based in 635 countries and covering 46 languages.

The largest amount of fact-checked content comes from English (6,130 fact-checks) and the least is Finland (1 fact-checks). Most fact-checked content is in Spanish (3,367) followed by Portuguese (1,998) and French (963) (Figure 3).

Figure 3: Amount of fact-checks by language.

Figure 4: Amount of fact-checked content per contry.

Determining a direct impact of fact-checking on the spread of misinformation is not easy. However, it is possible to determine how well a particular corrective information is spreading in relation to its corresponding misinformation.

Figure 5 shows how misinformation and fact-checking content has spread in various topics for the last two analysis periods and overall.

Figure 5: Topical misinformation and fact-checks spread.

Demographic Impact

Using automatic methods, Twitter account demographics are extracted for user age, gender and account type (i.e., identify if an account belong to an individual or organisation).

Figure 6 displays how misinformation and fact-checks are spread by different demographics.

Figure 6: Misinformation and Fact-check spread for different demographics. Top: Gender, Center: Age group, Bottom: Account type.

Data Collection and Methodology

The full methodology and information about the limitation and dataset used for this analysis can be accessed in the [methodology page](https://fcobservatory.org/faq/).