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Between Monday 10 August 2020 and Monday 17 August 2020, misinformation about Vaccine has increasead whereas misinformation about Authorities 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 10 August 2020 and Monday 17 August 2020.

239,291 Misinforming Tweets
New:+1,515 Trend:-754
89,261 Fact-checking Tweets
New:+1,678 Trend:-199
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 10 August 2020 and Monday 17 August 2020, 1,515 new URLs have been identified as potential misinforming content. Out of the 8 topics identified by Fact-checking organisations (Figure 1), most of the new shared URLs were about Authorities with an increase of +1,106 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 Symptoms with a change of +28 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 Vaccine with a change of +301 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Vaccine with a change of -1,017 compared to the previous period.

The all time most important topic is Other with a total of 91,040 URL shares and the least popular topic is Vaccine with 693 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 438 456 21847
https://www.torontotoday.net/2020/08/15/vladimir-putins-daughter-dies-after-second-dose-of-covid-vaccine VERA Files Vaccine 291 0 291
https://twitter.com/askomartin/status/1252246273794727938 El Surtidor Cure 212 0 2046
https://twitter.com/ColBolivariana/status/1294332783469432832 Colombiacheck Conspiracy Theory 129 0 129
https://twitter.com/carmelonetobr/status/1293580971594395649 Estadão Verifica Authorities 93 0 93
https://terrabrasilnoticias.com/2020/08/reviravolta-usp-comprova-que-pessoas-em-confinamento-sao-mais-vulneraveis-a-contaminacao-por-covid/ Aos Fatos Other 75 0 75
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 34 29 3529
https://twitter.com/vivirsinalergia/status/1291841195430289412 Efecto Cocuyo Cure 33 553 586
https://www.onenews.ph/us-health-agency-says-traditional-chinese-medicine-can-help-covid-19-patients-recover-faster Annie Lab Cure 25 0 25
https://childrenshealthdefense.org/news/vaccine-trial-catastrophe-moderna-vaccine-has-20-serious-injury-rate-in-high-dose-group/ Facta Vaccine 19 8 360

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://piaui.folha.uol.com.br/lupa/2020/07/01/verificamos-stf-bolsonaro-covid/ Authorities 74 57 190
https://factcheck.afp.com/hoax-circulates-online-fauci-knew-about-effective-coronavirus-treatments-2005 Authorities 36 9 45
https://maldita.es/malditaciencia/2020/08/10/afirmaciones-falsas-decano-colegio-biologos-euskadi-covid-19/ Conspiracy Theory 36 0 36
https://www.aosfatos.org/noticias/nao-e-verdade-que-stf-afastou-bolsonaro-de-acoes-para-o-controle-da-pandemia/ Authorities 31 21 72
https://www.factcheck.org/2020/08/face-masks-dont-cause-legionnaires-disease/ Spread 31 0 31
https://politica.estadao.com.br/blogs/estadao-verifica/nao-ha-evidencias-que-80-da-populacao-seja-imune-ao-novo-coronavirus/ Spread 29 0 38
https://www.politifact.com/factchecks/2020/apr/08/donald-trump/trump-said-obama-admin-left-him-bare-stockpile-wro/ Other 24 17 593
https://piaui.folha.uol.com.br/lupa/2020/07/23/verificamos-doria-vacina-covid/ Authorities 23 0 30
https://www.aosfatos.org/noticias/e-falso-que-camila-pitanga-falsificou-diagnostico-de-malaria-para-se-medicar-com-cloroquina/ Other 23 0 23
https://correctiv.org/faktencheck/medizin-und-gesundheit/2020/04/07/coronavirus-nein-aktuelle-pcr-tests-haben-keine-fehlerquote-von-30-bis-50-prozent Other 18 9 122

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/).