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Between Monday 28 September 2020 and Monday 05 October 2020, misinformation about Cure 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 28 September 2020 and Monday 05 October 2020.

245,304 Misinforming Tweets
New:+575 Trend:-1,401
98,382 Fact-checking Tweets
New:+1,022 Trend:+31
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 28 September 2020 and Monday 05 October 2020, 575 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 +803 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 Vaccine 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 Other with a change of +93 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Other with a change of -1,070 compared to the previous period.

The all time most important topic is Authorities with a total of 95,899 URL shares and the least popular topic is Vaccine with 958 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 345 378 24504
https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm Détecteur de rumeurs Other 34 23 259
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 24 9 3648
https://www.nytimes.com/2020/04/06/us/politics/coronavirus-trump-malaria-drug.html Factcheck.kz Cure 19 1 3723
https://www.jornaldacidadeonline.com.br/noticias/23291/uma-catastrofica-analise-sobre-as-vacinas-contra-o-virus-chines-interferem-diretamente-no-material-genetico-veja-o-video Agência Lupa Conspiracy Theory 16 383 399
http://www.francesoir.fr/le-confinement-tout-ce-que-lon-ne-vous-pas-dit-aberration-humaine-sanitaire-economique Les Décodeurs Authorities 9 11 327
https://twitter.com/realDonaldTrump/status/1241367239900778501 Salud con lupa Cure 9 0 11285
https://twitter.com/realDonaldTrump/status/1250852583318736896 LeadStories Authorities 8 15 5406
https://www.whitehouse.gov/briefings-statements/remarks-president-trump-vice-president-pence-members-coronavirus-task-force-press-briefing-31/ AFP Cure 8 4 718
https://nypost.com/2020/03/24/new-york-hospitals-treating-coronavirus-patients-with-vitamin-c/ Open Cure 8 2 1281

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.factcheck.org/2020/09/cdc-did-not-admit-only-6-of-recorded-deaths-from-covid-19/ Authorities 74 149 1474
https://www.factcheck.org/2020/05/outdated-fauci-video-on-face-masks-shared-out-of-context/ Authorities 64 16 1059
https://www.factcheck.org/2020/07/herman-cain-died-of-covid-19-not-cancer/ Other 60 11 350
https://www.factcheck.org/2020/05/false-perception-of-covid-19s-impact-on-the-homeless/ Spread 56 4 236
https://www.politifact.com/factchecks/2020/mar/15/joe-biden/ad-watch-biden-video-twists-trumps-words-coronavir/ Authorities 44 2 690
https://www.factcheck.org/2020/10/trump-campaign-didnt-send-email-fundraising-off-covid-19-diagnosis/ Other 28 0 28
https://www.politifact.com/factchecks/2020/mar/27/donald-trump/fact-checking-whether-biden-called-trump-xenophobi/ Authorities 25 5 525
https://www.factcheck.org/2020/07/old-photo-shows-obama-fauci-at-u-s-facility-not-wuhan-lab/ Conspiracy Theory 15 11 184
https://piaui.folha.uol.com.br/lupa/2020/09/29/verificamos-gripe-mortal-covid/ Other 14 0 14
https://maldita.es/malditobulo/2020/10/01/fernando-simon-mascarilla-29-septiembre/ Authorities 13 0 13

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/).