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Between Monday 11 April 2022 and Monday 18 April 2022, misinformation about Causes has increasead whereas misinformation about Vaccine 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 11 April 2022 and Monday 18 April 2022.

404,108 Misinforming Tweets
New:+445 Trend:-2,846
198,125 Fact-checking Tweets
New:+222 Trend:-71
16,372 Fact-checks
101 Fact-checking Organisations

Key Content and Topics

During the period between Monday 11 April 2022 and Monday 18 April 2022, 445 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 Vaccine with an increase of +475 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 +0 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. Vaccines: Information relating to vaccines (e.g., side effects, effectiveness).
  8. Masks: Information concerning the usage of masks.
  9. 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 -1 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 -2,668 compared to the previous period.

The all time most important topic is Authorities with a total of 136,470 URL shares and the least popular topic is Symptoms with 3,258 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.facebook.com/watch/ StopFake.org Vaccine 358 3004 45111
https://www.instagram.com/accounts/login/ StopFake.org Authorities 25 35 1899
https://m.facebook.com/home.php Vistinomer Other 24 28 983
https://archive.ph/ StopFake.org Other 14 114 957
https://www.worldometers.info/ Agencia Ocote Authorities 7 93 38010
https://twitter.com/NICKIMINAJ/status/1437532566945341441 FactCheck.org Vaccine 5 1 30667
https://traugott-ickeroth.com/liveticker/ Correctiv Conspiracy Theory 3 4 578
https://1scandal.com/etats-unis-la-cour-supreme-annule-la-vaccination-universelle/ La Silla VacĂ­a Vaccine 3 0 380
https://twitter.com/brajeshlive/status/1225830935079161856 FactCrescendo Authorities 1 3 355
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 1 2 2158

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://healthfeedback.org/claimreview/joe-rogan-interview-with-peter-mccullough-contains-multiple-false-and-unsubstantiated-claims-about-the-covid-19-pandemic-and-vaccines/ Conspiracy Theory 22 12 1162
https://www.factcheck.org/2021/12/scicheck-article-makes-unfounded-claims-linking-athletes-injuries-deaths-to-vaccines/ Vaccine 13 7 319
https://healthfeedback.org/claimreview/study-lund-university-didnt-show-covid-19-mrna-vaccines-change-dna-epoch-times/ Vaccine 10 0 10
https://healthfeedback.org/claimreview/no-scientific-evidence-for-claim-by-pathologist-ryan-cole-that-covid-19-vaccines-weaken-the-immune-system/ Vaccine 9 1 317
https://www.politifact.com/factchecks/2021/nov/08/blog-posting/repeatedly-debunked-idea-shedding-covid-19-vaccine/ Vaccine 9 0 41
https://www.factcheck.org/2021/11/scicheck-desantis-comments-social-media-posts-mislead-on-covid-19s-toll-in-florida/ Other 7 2 233
https://healthfeedback.org/claimreview/robert-malone-misleading-unsubstantiated-claims-covid-19-safety-efficacy-vaccines-joe-rogan-experience-spotify-podcast/ Vaccine 7 2 76
https://www.factcheck.org/es/2022/01/scicheck-articulo-hace-afirmaciones-infundadas-que-vinculan-lesiones-y-muertes-de-atletas-con-las-vacunas/ Vaccine 7 0 21
https://factcheck.afp.com/us-cardiologist-makes-false-claims-about-covid-19-vaccination Vaccine 6 2 827
https://www.factcheck.org/2022/01/scicheck-posts-about-cross-reactants-misrepresent-accuracy-of-covid-19-pcr-tests/ Other 5 1 22

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 101 fact-checking organisation based in 999 countries and covering 46 languages.

The largest amount of fact-checked content comes from English (8,704 fact-checks) and the least is Finland (1 fact-checks). Most fact-checked content is in Spanish (4,570) followed by Portuguese (2,801) and Ukrainian (2,069) (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.