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Between Monday 24 August 2020 and Monday 31 August 2020, 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 24 August 2020 and Monday 31 August 2020.

240,626 Misinforming Tweets
New:+515 Trend:-305
91,526 Fact-checking Tweets
New:+991 Trend:-283
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 24 August 2020 and Monday 31 August 2020, 515 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 +705 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 +11 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 Symptoms with a change of +0 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 -224 compared to the previous period.

The all time most important topic is Other with a total of 91,750 URL shares and the least popular topic is Vaccine with 939 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 358 365 22570
https://twitter.com/FinaRomanDiaz/status/1300071791264751617 Animal Político Other 19 0 19
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 12 30 3571
https://www.freep.com/story/news/2020/05/05/michigan-capitol-building-protest-picture/3084192001/ PolitiFact Other 11 2 260
https://biohackinfo.com/news-bill-gates-id2020-vaccine-implant-covid-19-digital-certificates/ Factcheck.kz Conspiracy Theory 9 4 1298
https://nypost.com/2020/03/24/new-york-hospitals-treating-coronavirus-patients-with-vitamin-c/ Open Cure 9 4 1268
https://www.torontotoday.net/2020/08/15/vladimir-putins-daughter-dies-after-second-dose-of-covid-vaccine VERA Files Vaccine 6 225 522
http://www.francesoir.fr/le-confinement-tout-ce-que-lon-ne-vous-pas-dit-aberration-humaine-sanitaire-economique Les Décodeurs Authorities 6 18 284
https://traugott-ickeroth.com/liveticker/ Correctiv Conspiracy Theory 6 2 235
https://vixra.org/pdf/2006.0044v1.pdf Détecteur de rumeurs Spread 4 3 72

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.buzzfeed.com/jp/kotahatachi/who-tedros-abe Authorities 24 0 24
https://www.politifact.com/factchecks/2020/mar/27/donald-trump/fact-checking-whether-biden-called-trump-xenophobi/ Authorities 22 16 388
https://efectococuyo.com/cocuyo-chequea/remdesivir-tratamiento-redes-sociales-covid19/ Cure 20 14 34
https://www.factcheck.org/2020/07/herman-cain-died-of-covid-19-not-cancer/ Other 20 7 209
https://healthfeedback.org/claimreview/current-covid-19-mortality-rate-does-not-predict-the-future-probability-of-dying-from-the-disease/ Other 16 5 21
https://www.boomlive.in/fake-news/pictures-of-rahul-gandhi-with-migrant-workers-peddled-with-false-claims-8175 Other 16 0 29
https://piaui.folha.uol.com.br/lupa/2020/07/01/verificamos-stf-bolsonaro-covid/ Authorities 15 15 220
https://piaui.folha.uol.com.br/lupa/2020/08/27/verificamos-post-sistema-imunologico/ Causes 15 0 15
https://www.factcheck.org/2020/05/outdated-fauci-video-on-face-masks-shared-out-of-context/ Authorities 14 11 935
https://www.politifact.com/factchecks/2020/jun/23/viral-image/no-aoc-didnt-tweet-about-closing-businesses-until-/ Authorities 14 8 435

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/).