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Between Monday 18 October 2021 and Monday 25 October 2021, misinformation about Conspiracy Theory 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 18 October 2021 and Monday 25 October 2021.

350,171 Misinforming Tweets
New:+216 Trend:-14
174,463 Fact-checking Tweets
New:+690 Trend:-30
14,819 Fact-checks
102 Fact-checking Organisations

Key Content and Topics

During the period between Monday 18 October 2021 and Monday 25 October 2021, 216 new URLs have been identified as potential misinforming content. Out of the 9 topics identified by Fact-checking organisations (Figure 1), most of the new shared URLs were about Vaccine with an increase of +366 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 Masks 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 Spread with a change of +18 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Spread with a change of -48 compared to the previous period.

The all time most important topic is Authorities with a total of 129,676 URL shares and the least popular topic is Masks with 12 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 128 127 35521
https://m.facebook.com/home.php Vistinomer Other 23 43 154
http://dilyana.bg/project-g-2101-pentagon-biolab-discovered-mers-and-sars-like-coronaviruses-in-bats/ Myth Detector Conspiracy Theory 20 1 161
https://filiperafaeli.substack.com/p/hidroxicloroquina-para-profilaxia Agência Lupa Cure 9 3 133
https://twitter.com/NICKIMINAJ/status/1437532566945341441 FactCheck.org Vaccine 4 10 30357
https://www.nytimes.com/2021/04/26/us/florida-centner-academy-vaccine.html Détecteur de rumeurs Vaccine 4 1 927
https://traugott-ickeroth.com/liveticker/ Correctiv Conspiracy Theory 3 1 529
https://twitter.com/stkirsch/status/1437912807442321413 PolitiFact Vaccine 3 0 64
https://c19study.com Détecteur de rumeurs Cure 2 3 29276
https://www.youtube.com/watch?v=tCZu8a5r5y4 MediaWise Other 2 3 167

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 29 7 1110
https://www.factcheck.org/2021/04/scicheck-idaho-doctor-makes-baseless-claims-about-safety-of-covid-19-vaccines/ Vaccine 26 28 696
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 25 26 291
https://factcheck.afp.com/http%253A%252F%252Fdoc.afp.com%252F9M48JR-1 Cure 22 8 160
https://www.factcheck.org/2021/03/scicheck-viral-posts-misuse-vaers-data-to-make-false-claims-about-covid-19-vaccines/ Vaccine 19 20 502
https://www.factcheck.org/2021/08/scicheck-researcher-distorts-facts-on-covid-19-vaccine-approval-liability/ Vaccine 18 14 153
https://www.factcheck.org/2021/09/covid-19-the-unvaccinated-pose-a-risk-to-the-vaccinated/ Vaccine 17 21 141
https://www.politifact.com/factchecks/2021/aug/06/instagram-posts/why-covid-19-survival-rate-not-over-99/ Symptoms 14 21 93
https://www.factcheck.org/2021/10/scicheck-white-house-and-hhs-employees-arent-exempt-from-vaccine-mandate/ Vaccine 12 22 57
https://correctiv.org/faktencheck/2020/04/02/keine-belege-dass-die-ard-saerge-von-2013-in-aktueller-corona-berichterstattung-zeigte/ Other 10 0 33

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 102 fact-checking organisation based in 895 countries and covering 48 languages.

The largest amount of fact-checked content comes from English (7,811 fact-checks) and the least is Finland (1 fact-checks). Most fact-checked content is in Spanish (4,178) followed by Portuguese (2,525) and Ukrainian (1,823) (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.