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Between Monday 25 May 2020 and Monday 01 June 2020, misinformation about Authorities has increasead whereas misinformation about Causes 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 25 May 2020 and Monday 01 June 2020.

223,313 Misinforming Tweets
New:+1,654 Trend:-358
68,639 Fact-checking Tweets
New:+2,549 Trend:-671
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 25 May 2020 and Monday 01 June 2020, 1,654 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 +1,743 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 +15 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 Symptoms with a change of +3 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Symptoms with a change of -318 compared to the previous period.

The all time most important topic is Other with a total of 84,667 URL shares and the least popular topic is Vaccine with 285 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 620 664 16651
https://twitter.com/necoodi3/status/1265295832674209792 INFACT Authorities 310 0 310
https://www.mediaite.com/tv/cnbc-segment-explodes-as-andrew-ross-sorkin-accuses-joe-kernen-of-being-in-the-tank-for-trump-during-crisis-100000-people-died/ PolitiFact Spread 135 0 135
https://childrenshealthdefense.org/news/vaccine-trial-catastrophe-moderna-vaccine-has-20-serious-injury-rate-in-high-dose-group/ Facta Vaccine 91 180 271
https://www.medyaradar.com/whoya-itaat-etmeyen-italyan-doktorlar-koronavirus-sirlarini-kesfetti-haberi-2027358 Teyit Cure 62 0 62
https://www.galluranews.org/iss-istituto-superiore-di-sanita-i-dati-reali-che-smentiscono-la-pandemia/ Facta Conspiracy Theory 48 0 48
https://twitter.com/Jo0sef/status/1264888352895107073 Misbar Other 33 0 33
https://www.freep.com/story/news/2020/05/05/michigan-capitol-building-protest-picture/3084192001/ PolitiFact Other 32 6 158
https://twitter.com/qaroon111/status/1265152262646771718 Misbar Other 21 0 21
https://youtu.be/d9GbVZOcT18 Open Causes 19 274 1060

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.factcheck.org/2020/05/outdated-fauci-video-on-face-masks-shared-out-of-context/ Authorities 133 234 367
https://piaui.folha.uol.com.br/lupa/2020/05/25/verificamos-bar-brahma-covid/ Other 112 0 112
https://www.indiatoday.in/fact-check/story/blot-clot-coronavirus-italian-doctors-who-1681512-2020-05-25 Conspiracy Theory 61 0 61
https://chequeado.com/el-explicador/no-c5n-no-publico-en-los-ultimos-dias-imagenes-extranjeras-sobre-el-coronavirus-como-si-fueran-locales/ Other 59 0 59
https://www.washingtonpost.com/politics/2020/04/07/trumps-claim-that-he-imposed-first-china-ban/ Authorities 55 16 556
https://www.buzzfeed.com/jp/kensukeseya/covid-mask-fc Other 48 0 121
https://www.factcheck.org/2020/05/covid-19-isnt-caused-by-bacteria/ Cure 45 0 45
https://www.politifact.com/factchecks/2020/mar/04/facebook-posts/president-obama-declared-h1n1-public-health-emerge/ Authorities 39 20 3591
https://www.lemonde.fr/les-decodeurs/article/2020/05/29/caniculaire-plus-chaud-que-2019-ce-que-l-on-peut-prevoir-ou-non-pour-l-ete-2020_6041195_4355770.html Other 32 0 32
https://efectococuyo.com/cocuyo-chequea/amazon-no-reparte-ayudas-alimentarias/ Other 30 0 30

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/).