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Between Monday 02 March 2020 and Monday 09 March 2020, misinformation about Authorities has increasead whereas misinformation about Other 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 02 March 2020 and Monday 09 March 2020.

36,553 Misinforming Tweets
New:+2,219 Trend:-10,993
5,669 Fact-checking Tweets
New:+1,921 Trend:+1,288
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 02 March 2020 and Monday 09 March 2020, 2,219 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 Authorities with an increase of +1,979 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 +38 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 Authorities with a change of +1,036 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Authorities with a change of -10,091 compared to the previous period.

The all time most important topic is Other with a total of 16,021 URL shares and the least popular topic is Symptoms with 584 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 508 513 1523
https://www.nytimes.com/2020/03/04/us/politics/coronavirus-trump-obama.html PolitiFact Authorities 310 0 310
https://twitter.com/yanwang10508606/status/1236472761129807873 France 24 Observers Conspiracy Theory 138 0 138
https://mcmnt.com/vatican-confirms-pope-francis-and-two-aides-test-positive-for-coronavirus/ Rappler Other 116 575 691
https://www.washingtontimes.com/news/2020/mar/4/dhs-links-coronavirus-border-328-chinese-illegals-/ LeadStories Authorities 109 0 109
https://www.nature.com/articles/d41586-020-00548-w Maldita.es Causes 99 195 294
https://www.presstv.com/Detail/2020/03/05/620213/Coronavirus-was-produced-in-a-laboratory LeadStories Conspiracy Theory 88 0 88
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 87 80 532
https://twitter.com/CNN/status/1233406525491814400 PolitiFact Other 86 8811 8897
https://twitter.com/realdanlyons/status/1232712764856115200 PolitiFact Authorities 67 0 67

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.washingtonpost.com/politics/2020/03/06/trumps-bogus-effort-blame-obama-sluggish-coronavirus-testing/ Authorities 449 0 449
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-19 Other 229 0 229
https://infact.press/2020/03/post-5006/ Authorities 134 46 180
https://www.politifact.com/factchecks/2020/mar/04/facebook-posts/president-obama-declared-h1n1-public-health-emerge/ Authorities 131 0 131
https://www.buzzfeed.com/jp/kensukeseya/covid-mask-fc Other 67 0 67
https://www.lemonde.fr/les-decodeurs/article/2020/03/06/le-coronavirus-arme-biologique-le-vrai-du-faux-d-une-video-virale_6032098_4355770.html Conspiracy Theory 52 0 52
https://www.politifact.com/factchecks/2020/mar/03/facebook-posts/hand-sanitizer-can-be-used-prevent-coronavirus-inf/ Cure 44 0 44
https://www.factcheck.org/2020/03/social-posts-share-fake-schumer-tweet/ Authorities 41 0 41
https://www.politifact.com/factchecks/2020/feb/26/viral-image/book-end-days-described-illness-2020-not-wuhan-400/ Conspiracy Theory 34 26 60
https://www.politifact.com/factchecks/2020/mar/06/donald-trump/trump-wrongly-blames-obama-limits-coronavirus-test/ Authorities 18 0 18

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/).