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Between Monday 30 March 2020 and Monday 06 April 2020, misinformation about Causes 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 30 March 2020 and Monday 06 April 2020.

129,702 Misinforming Tweets
New:+11,959 Trend:-8,645
32,187 Fact-checking Tweets
New:+7,588 Trend:+722
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 30 March 2020 and Monday 06 April 2020, 11,959 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 +6,392 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 +161 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 +2,186 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 -3,506 compared to the previous period.

The all time most important topic is Other with a total of 58,549 URL shares and the least popular topic is Symptoms with 1,564 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://twitter.com/soapachu/status/1246793653294686208 PolitiFact Causes 2349 0 2349
https://www.worldometers.info/ Agencia Ocote Authorities 2115 2084 8547
https://twitter.com/OsmarTerra/status/1246474430676643842 AgĂȘncia Lupa Spread 988 0 988
https://twitter.com/OsmarTerra/status/1245079417933742081 AgĂȘncia Lupa Authorities 761 0 761
https://news.sky.com/story/coronavirus-belarus-president-refuses-to-cancel-anything-and-says-vodka-and-saunas-will-ward-off-coronavirus-11965396 Factcheck.Vlaanderen Cure 316 354 670
https://www.check-corona.com/checker Fatabyyano Other 290 627 917
https://biohackinfo.com/news-bill-gates-id2020-vaccine-implant-covid-19-digital-certificates/ Factcheck.kz Conspiracy Theory 275 456 820
https://twitter.com/dmbareilly/status/1244544917566676992 Newsmeter.in Cure 257 0 257
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 244 428 2858
https://www.youtube.com/watch?v=zFN5LUaqxOA LeadStories Conspiracy Theory 204 223 1456

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.washingtonpost.com/politics/2020/03/30/11-100000-what-went-wrong-with-coronavirus-testing-us/ Authorities 655 0 655
https://www.factcheck.org/2020/02/no-link-between-harvard-scientist-charles-lieber-and-coronavirus/ Conspiracy Theory 540 39 696
https://www.politifact.com/factchecks/2020/mar/17/instagram-posts/celebrities-are-sharing-misleading-post-about-trum/ Authorities 235 185 651
https://www.politifact.com/factchecks/2020/mar/04/facebook-posts/president-obama-declared-h1n1-public-health-emerge/ Authorities 221 336 3031
https://www.politifact.com/factchecks/2020/mar/31/donald-trump/trump-blames-past-administrations-flawed-covid-19-/ Authorities 211 0 211
https://www.politifact.com/factchecks/2020/mar/15/joe-biden/ad-watch-biden-video-twists-trumps-words-coronavir/ Authorities 136 92 344
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-21 Other 135 0 135
https://www.buzzfeed.com/jp/yutochiba/tokyo-olympics-pcr Authorities 134 0 134
https://verificado.com.mx/es-falso-el-aumento-de-casos-de-neumonias-en-mexico/ Spread 117 0 117
https://www.factcheck.org/2020/03/social-posts-share-fake-schumer-tweet/ Authorities 114 60 303

Table 2: Top fact-checked content.

Fact-checking

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/).