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Between Monday 20 December 2021 and Monday 27 December 2021, misinformation about Spread 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 20 December 2021 and Monday 27 December 2021.

357,707 Misinforming Tweets
New:+134 Trend:-765
184,101 Fact-checking Tweets
New:+632 Trend:-560
15,641 Fact-checks
101 Fact-checking Organisations

Key Content and Topics

During the period between Monday 20 December 2021 and Monday 27 December 2021, 134 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 +378 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 Symptoms 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 Symptoms with a change of -976 compared to the previous period.

The all time most important topic is Authorities with a total of 132,987 URL shares and the least popular topic is Symptoms with 3,160 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.instagram.com/accounts/login/ StopFake.org Authorities 38 96 994
https://www.worldometers.info/ Agencia Ocote Authorities 19 141 36390
https://m.facebook.com/home.php Vistinomer Other 17 37 411
https://1scandal.com/etats-unis-la-cour-supreme-annule-la-vaccination-universelle/ La Silla Vacía Vaccine 14 92 221
https://twitter.com/NICKIMINAJ/status/1437532566945341441 FactCheck.org Vaccine 8 10 30572
https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm Détecteur de rumeurs Other 5 0 1685
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 2 1 2145
https://medicalracism.childrenshealthdefense.org/medical-racism-the-new-apartheid/ FactCheck.org Vaccine 2 0 186
https://www.bitchute.com/video/InH89amCpy7S/ FactCheck.org Vaccine 2 0 41
https://smartairfilters.com/en/blog/microwaving-masks-disinfect-covid-virus/ LeadStories Cure 2 0 32

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.factcheck.org/2021/03/scicheck-viral-posts-misuse-vaers-data-to-make-false-claims-about-covid-19-vaccines/ Vaccine 28 19 640
https://healthfeedback.org/claimreview/the-development-of-mrna-vaccines-was-a-collaborative-effort-robert-malone-contributed-to-their-development-but-he-is-not-their-inventor/ Vaccine 24 51 513
https://factuel.afp.com/non-lunion-europeenne-ne-preparait-pas-un-passeport-vaccinal-bien-avant-lepidemie-de-covid-19 Conspiracy Theory 19 5 93
https://factcheck.afp.com/http%253A%252F%252Fdoc.afp.com%252F9QW7UP-1 Authorities 19 0 19
https://www.politifact.com/factchecks/2021/jul/23/tiktok-posts/biden-harris-doubted-trump-covid-19-vaccines-not-v/ Vaccine 16 21 620
https://www.politifact.com/factchecks/2021/aug/06/instagram-posts/why-covid-19-survival-rate-not-over-99/ Symptoms 16 12 178
https://www.politifact.com/factchecks/2021/oct/29/alex-berenson/covid-19-death-rate-england-much-higher-among-unva/ Vaccine 16 7 42
https://politica.estadao.com.br/blogs/estadao-verifica/discurso-de-premio-nobel-engana-ao-afirmar-que-vacinas-criam-variantes-e-agravam-pandemia/ Vaccine 15 3 129
https://www.factcheck.org/2021/09/covid-19-the-unvaccinated-pose-a-risk-to-the-vaccinated/ Vaccine 14 9 225
https://www.politifact.com/factchecks/2020/may/21/facebook-posts/disposable-homemade-masks-are-effective-stopping-a/ Spread 12 20 1229

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 967 countries and covering 46 languages.

The largest amount of fact-checked content comes from English (8,233 fact-checks) and the least is Finland (1 fact-checks). Most fact-checked content is in Spanish (4,351) followed by Portuguese (2,663) and Ukrainian (1,994) (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.