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Between Monday 26 October 2020 and Monday 02 November 2020, misinformation about Authorities 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 26 October 2020 and Monday 02 November 2020.

248,687 Misinforming Tweets
New:+981 Trend:+183
103,422 Fact-checking Tweets
New:+1,233 Trend:-45
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 26 October 2020 and Monday 02 November 2020, 981 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 Authorities with an increase of +1,250 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 Vaccine with a change of +4 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 Authorities with a change of +411 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 -121 compared to the previous period.

The all time most important topic is Authorities with a total of 99,592 URL shares and the least popular topic is Vaccine with 990 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 761 423 26383
https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm D├ętecteur de rumeurs Other 61 150 980
https://twitter.com/realDonaldTrump/status/1250852583318736896 LeadStories Authorities 20 5 5469
https://twitter.com/Lord_Sugar/status/1241770029982593026 BOOM FactCheck Authorities 11 0 1215
https://biohackinfo.com/news-bill-gates-id2020-vaccine-implant-covid-19-digital-certificates/ Factcheck.kz Conspiracy Theory 10 6 1340
https://www.youtube.com/watch?v=V4u-BuJKplU Teyit Conspiracy Theory 9 34 43
http://www.francesoir.fr/le-confinement-tout-ce-que-lon-ne-vous-pas-dit-aberration-humaine-sanitaire-economique Les D├ęcodeurs Authorities 9 17 357
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 7 2 1807
https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca LeadStories Other 6 5 28395
https://www.youtube.com/watch?v=ZAlkwBxJ5vk VoxCheck Conspiracy Theory 4 42 46

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://correctiv.org/faktencheck/2020/09/04/pcr-tests-weisen-corona-infektionen-nach-das-schweizerische-bundesamt-fuer-gesundheit-bestaetigte-nichts-gegenteiliges/ Authorities 89 11 116
https://www.factcheck.org/2020/09/cdc-did-not-admit-only-6-of-recorded-deaths-from-covid-19/ Authorities 77 55 1791
https://piaui.folha.uol.com.br/lupa/2020/10/28/verificamos-foto-coronavac-abril/ Conspiracy Theory 36 0 36
https://www.factcheck.org/2020/10/doctors-in-video-falsely-equate-covid-19-with-a-normal-flu-virus/ Authorities 24 35 59
https://piaui.folha.uol.com.br/lupa/2020/10/23/verificamos-medico-vacina-pandemia/ Other 22 18 40
https://factcheck.afp.com/bacterial-pneumonia-complication-influenza-not-linked-mask-wearing Conspiracy Theory 20 26 46
https://piaui.folha.uol.com.br/lupa/2020/10/27/verificamos-audio-vacina-chinesa/ Conspiracy Theory 20 0 20
https://healthfeedback.org/claimreview/the-group-doctors-for-truth-spreads-misinformation-about-the-impact-of-the-covid-19-pandemic-the-virus-and-the-reliability-of-diagnostic-tests/ Other 18 13 31
https://www.aap.com.au/pcr-inventor-who-died-in-2019-did-not-say-his-test-wont-work-for-covid-19-infections/ Conspiracy Theory 16 20 102
https://www.aosfatos.org/noticias/e-falso-que-lei-brasileira-nao-ampara-obrigatoriedade-de-vacinas/ Other 16 0 16

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 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/).