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Between Monday 09 March 2020 and Monday 16 March 2020, misinformation about Other has increasead whereas misinformation about Symptoms 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 09 March 2020 and Monday 16 March 2020.

65,265 Misinforming Tweets
New:+28,712 Trend:+26,493
11,620 Fact-checking Tweets
New:+5,951 Trend:+4,030
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 09 March 2020 and Monday 16 March 2020, 28,712 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 Other with an increase of +25,489 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 Causes with a change of +217 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 +24,678 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 +89 compared to the previous period.

The all time most important topic is Other with a total of 41,510 URL shares and the least popular topic is Symptoms with 1,051 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://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca LeadStories Other 24200 0 24200
https://www.worldometers.info/ Agencia Ocote Authorities 1027 508 2550
https://twitter.com/Ahmadinejad1956/status/1237072414841937920 Factnameh Conspiracy Theory 400 0 400
https://twitter.com/Madonna/status/1239164134664687616 CheckNews Spread 261 0 261
https://www.youtube.com/watch?v=zFN5LUaqxOA LeadStories Conspiracy Theory 258 0 258
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 255 87 787
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 193 0 193
https://ufospotlight.wordpress.com/2020/02/13/chinese-intelligence-officer-reveals-true-magnitude-of-chinas-coronavirus-crisis/ BOOM FactCheck Conspiracy Theory 146 58 618
https://www.betootaadvocate.com/uncategorized/gold-coast-hospital-staff-roll-in-a-wilson-volleyball-to-keep-tom-hanks-company-in-quarantine/ LeadStories Other 139 0 139
https://www.nature.com/articles/d41586-020-00548-w Maldita.es Causes 133 99 427

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.politifact.com/factchecks/2020/mar/04/facebook-posts/president-obama-declared-h1n1-public-health-emerge/ Authorities 2170 131 2301
https://www.politifact.com/factchecks/2020/mar/06/donald-trump/donald-trump-wrong-saying-barack-obama-did-nothing/ Authorities 462 14 476
https://politica.estadao.com.br/blogs/estadao-verifica/vacina-cubana-contra-coronavirus-e-ficcao/ Cure 348 0 348
https://factuel.afp.com/coronavirus-la-liste-des-pretendus-conseils-simples-et-accessibles-tous-ne-pas-suivre Symptoms 193 0 193
https://maldita.es/malditobulo/2020/03/10/celaa-valerio-manifestacion-8m-guantes-morados-coronavirus/ Authorities 138 0 138
https://www.factcheck.org/2020/03/contrary-to-false-posts-sanitizer-helpful-against-coronavirus/ Cure 96 0 96
https://www.lemonde.fr/les-decodeurs/article/2020/03/10/coronavirus-peut-on-vraiment-dire-que-le-covid-19-n-est-qu-un-gros-rhume-monte-en-epingle_6032483_4355770.html Symptoms 94 0 94
https://factcheck.afp.com/world-health-organization-refutes-viral-claims-holding-your-breath-can-test-covid-19 Symptoms 86 0 86
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-20 Other 78 0 78
https://www.politifact.com/factchecks/2020/mar/11/donald-trump/donald-trumps-wrong-claim-anybody-can-get-tested-c/ Authorities 73 0 73

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/).