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Between Monday 20 April 2020 and Monday 27 April 2020, misinformation about Causes 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 20 April 2020 and Monday 27 April 2020.

196,416 Misinforming Tweets
New:+27,286 Trend:+3,403
50,099 Fact-checking Tweets
New:+4,488 Trend:-2,826
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 20 April 2020 and Monday 27 April 2020, 27,286 new URLs have been identified as potential misinforming content. Out of the 7 topics identified by Fact-checking organisations (Figure 1), most of the new shared URLs were about Causes with an increase of +14,087 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 +52 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 Causes with a change of +13,296 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Causes with a change of -9,281 compared to the previous period.

The all time most important topic is Other with a total of 75,544 URL shares and the least popular topic is Symptoms with 2,090 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.youtube.com/watch?v=xfLVxx_lBLU Colombiacheck Causes 13620 0 13620
https://www.express.co.uk/news/world/1271028/Angela-Merkel-Germany-China-coronavirus-blame-Wuhan-Xi-Jinping-Trump-latest The Quint Other 2720 899 3619
https://twitter.com/askomartin/status/1252246273794727938 El Surtidor Cure 1826 0 1826
https://twitter.com/RudyGiuliani/status/1254513987196248065 PolitiFact Authorities 1310 0 1310
https://www.worldometers.info/ Agencia Ocote Authorities 1052 1517 12734
https://twitter.com/Goldstatetimes/status/1252390291018878979 CheckNews Conspiracy Theory 1046 0 1046
https://www.youtube.com/watch?v=dQkgXabo-A0 Maldita.es Conspiracy Theory 788 218 1006
https://n5ti.com/stories/1275/ LeadStories Cure 616 0 616
https://twitter.com/SaadiaAfzaal/status/1252930496037822464 PesaCheck Cure 478 0 478
https://www.youtube.com/watch?v=Rzu1AJRZJEI LeadStories Cure 470 2247 2717

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.washingtonpost.com/politics/2020/04/21/trumps-bizarre-effort-tag-obamas-swine-flu-response-disaster/ Authorities 312 0 312
https://www.washingtonpost.com/politics/2020/04/23/trump-versus-pelosi-what-happened-chinatown/ Authorities 300 0 300
https://healthfeedback.org/claimreview/claim-by-nobel-laureate-luc-montagnier-that-the-novel-coronavirus-is-man-made-and-contains-genetic-material-from-hiv-is-inaccurate/ Conspiracy Theory 102 0 102
https://www.liberation.fr/checknews/2020/04/22/covid-19-est-il-vrai-que-la-bacterie-prevotella-joue-un-role-dans-l-infection_1786037 Causes 92 0 92
https://www.lemonde.fr/les-decodeurs/article/2020/04/22/non-le-roquefort-n-est-pas-un-remede-contre-le-covid-19_6037460_4355770.html Cure 72 0 72
https://www.politifact.com/factchecks/2020/mar/15/joe-biden/ad-watch-biden-video-twists-trumps-words-coronavir/ Authorities 65 54 534
https://marathi.factcrescendo.com/fake-news-goes-viral-about-the-death-of-megha-vyas-due-to-coronavirus/ Other 57 0 57
https://www.politifact.com/factchecks/2020/mar/04/facebook-posts/president-obama-declared-h1n1-public-health-emerge/ Authorities 52 126 3400
https://maldita.es/malditobulo/2020/04/17/rajoy-confinamiento-paseo-pp-coronavirus-lasexta-diciembre/ Other 51 97 148
https://www.factcheck.org/2020/04/social-media-posts-make-baseless-claim-on-covid-19-death-toll/ Authorities 51 63 171

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/).