13 min read

Between Monday 18 April 2022 and Monday 25 April 2022, misinformation about Authorities 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 18 April 2022 and Monday 25 April 2022.

404,517 Misinforming Tweets
New:+409 Trend:-36
198,424 Fact-checking Tweets
New:+299 Trend:+77
16,372 Fact-checks
101 Fact-checking Organisations

Key Content and Topics

During the period between Monday 18 April 2022 and Monday 25 April 2022, 409 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 +364 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 +1 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 Authorities with a change of +79 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 -111 compared to the previous period.

The all time most important topic is Authorities with a total of 136,602 URL shares and the least popular topic is Symptoms with 3,262 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 238 358 45349
https://www.worldometers.info/ Agencia Ocote Authorities 87 7 38097
https://m.facebook.com/home.php Vistinomer Other 30 24 1013
https://www.instagram.com/accounts/login/ StopFake.org Authorities 26 25 1925
https://1scandal.com/etats-unis-la-cour-supreme-annule-la-vaccination-universelle/ La Silla Vacía Vaccine 8 3 388
https://c19study.com Détecteur de rumeurs Cure 3 0 29307
https://www.armstrongeconomics.com/world-news/corruption/belarusian-president-claims-imf-world-bank-offered-him-a-bribe-to-impose-covid-restrictions/ TEMPO Conspiracy Theory 3 0 890
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 2 1 2160
https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm Détecteur de rumeurs Other 2 1 1701
https://twitter.com/NICKIMINAJ/status/1437532566945341441 FactCheck.org Vaccine 1 5 30668

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.politifact.com/factchecks/2020/may/21/facebook-posts/disposable-homemade-masks-are-effective-stopping-a/ Spread 21 1 1381
https://www.factcheck.org/2022/04/scicheck-covid-19-is-caused-by-a-virus-not-snake-venom/ Conspiracy Theory 21 0 21
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 13 22 1175
https://www.factcheck.org/2021/04/scicheck-idaho-doctor-makes-baseless-claims-about-safety-of-covid-19-vaccines/ Vaccine 11 2 1071
https://www.politifact.com/factchecks/2022/feb/11/blog-posting/no-covid-19-vaccines-arent-responsible-increase-de/ Vaccine 11 2 37
https://www.factcheck.org/2021/12/scicheck-article-makes-unfounded-claims-linking-athletes-injuries-deaths-to-vaccines/ Vaccine 10 13 329
https://www.factcheck.org/2021/11/scicheck-desantis-comments-social-media-posts-mislead-on-covid-19s-toll-in-florida/ Other 9 7 242
https://healthfeedback.org/claimreview/study-lund-university-didnt-show-covid-19-mrna-vaccines-change-dna-epoch-times/ Vaccine 6 10 16
https://www.politifact.com/factchecks/2020/dec/22/tweets/viral-tweet-cites-made-cdc-covid-19-survival-rates/ Other 6 3 325
https://www.factcheck.org/2021/03/scicheck-viral-posts-misuse-vaers-data-to-make-false-claims-about-covid-19-vaccines/ Vaccine 6 0 770

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 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.