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Between Monday 20 July 2020 and Monday 27 July 2020, misinformation about Cure has increasead whereas misinformation about Spread 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 July 2020 and Monday 27 July 2020.

234,419 Misinforming Tweets
New:+1,310 Trend:+216
83,500 Fact-checking Tweets
New:+2,319 Trend:+172
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 20 July 2020 and Monday 27 July 2020, 1,310 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 +1,790 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 +8 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 +325 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 -157 compared to the previous period.

The all time most important topic is Other with a total of 89,367 URL shares and the least popular topic is Vaccine with 346 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 472 563 20459
https://twitter.com/ndtv/status/1286591741345378305 Newsmeter.in Cure 230 0 230
https://twitter.com/realDonaldTrump/status/1250852583318736896 LeadStories Authorities 134 30 5342
https://youtu.be/d9GbVZOcT18 Open Causes 130 3 1225
https://twitter.com/TimesNow/status/1286260343765794816 Newsmeter.in Spread 86 0 86
https://www.facebook.com/BenSwannRealityCheck/videos/705845830268772/ FactCheck.org Spread 26 139 165
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 25 74 3446
http://web.archive.org/web/20200531151033/https:/www.xandernieuws.net/algemeen/artsen-in-italie-duitsland-en-india-zeggen-dat-meeste-covid-19-doden-niet-door-virus-maar-door-longbacterie-zijn-overleden/ Factcheck.Vlaanderen Cure 14 0 14
http://www.francesoir.fr/le-confinement-tout-ce-que-lon-ne-vous-pas-dit-aberration-humaine-sanitaire-economique Les D├ęcodeurs Authorities 12 12 211
https://biohackinfo.com/news-bill-gates-id2020-vaccine-implant-covid-19-digital-certificates/ Factcheck.kz Conspiracy Theory 10 2 1264

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.washingtonpost.com/politics/2020/07/23/devoss-claim-that-children-are-stoppers-covid-19/ Authorities 598 0 598
https://www.factcheck.org/2020/06/fake-aoc-tweet-politicizes-covid-19-business-restrictions/ Authorities 67 47 324
https://www.politifact.com/factchecks/2020/may/21/facebook-posts/disposable-homemade-masks-are-effective-stopping-a/ Spread 58 99 471
https://www.factcheck.org/2020/05/outdated-fauci-video-on-face-masks-shared-out-of-context/ Authorities 56 107 838
https://www.politifact.com/factchecks/2020/jun/13/andrew-cuomo/new-yorks-nursing-home-policy-was-not-line-cdc/ Authorities 40 27 193
https://efectococuyo.com/cocuyo-chequea/amazon-no-reparte-ayudas-alimentarias/ Other 34 33 245
https://www.politifact.com/factchecks/2020/may/06/blog-posting/dont-fall-conspiracy-about-dr-anthony-fauci-hydrox/ Cure 34 23 332
https://www.politifact.com/factchecks/2020/jun/23/viral-image/no-aoc-didnt-tweet-about-closing-businesses-until-/ Authorities 33 25 380
https://piaui.folha.uol.com.br/lupa/2020/07/22/verificamos-voluntaria-vacina-covid/ Conspiracy Theory 33 0 33
https://www.factcheck.org/2020/07/video-misrepresents-the-science-behind-face-masks/ Spread 33 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 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/).