13 min read

Between Monday 06 July 2020 and Monday 13 July 2020, misinformation about Authorities 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 06 July 2020 and Monday 13 July 2020.

232,015 Misinforming Tweets
New:+1,041 Trend:+14
79,034 Fact-checking Tweets
New:+1,711 Trend:+80
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 06 July 2020 and Monday 13 July 2020, 1,041 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 +1,035 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 +5 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 Cure with a change of +117 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Cure with a change of -47 compared to the previous period.

The all time most important topic is Other with a total of 88,434 URL shares and the least popular topic is Vaccine with 332 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 555 439 19424
https://www.bbc.com/mundo/noticias-53332686 Bolivia Verifica Spread 193 0 193
https://twitter.com/JohnBWellsCTM/status/1279036203451416576 INFACT Spread 65 312 377
https://www.youtube.com/watch?v=p_AyuhbnPOI Faktograf Other 22 9 3347
https://www.youtube.com/watch?v=RKt2QDp5SZo Animal PolĂ­tico Conspiracy Theory 16 2 435
https://www.youtube.com/watch?v=R9gzG9S36nM VoxCheck Conspiracy Theory 15 0 15
https://www.mediterranee-infection.com/wp-content/uploads/2020/03/Hydroxychloroquine_final_DOI_IJAA.pdf TjekDet.dk Cure 13 4 1954
https://eldiario.com/2020/06/16/la-dexametasona-el-farmaco-que-podria-reducir-las-muertes-por-covid-19/ Efecto Cocuyo Cure 12 34 161
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 9 13 1706
https://www.whitehouse.gov/briefings-statements/remarks-president-trump-vice-president-pence-members-coronavirus-task-force-press-briefing-31/ AFP Cure 9 5 642

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://colombiacheck.com/chequeos/no-tratamiento-recomendado-por-medico-homeopata-no-es-la-cura-contra-covid-19 Cure 88 0 95
https://www.factcheck.org/2020/05/outdated-fauci-video-on-face-masks-shared-out-of-context/ Authorities 74 62 675
https://www.politifact.com/factchecks/2020/jun/03/facebook-posts/claim-florida-undercounting-covid-19-deaths-uses-f/ Other 64 44 220
https://www.politifact.com/factchecks/2020/may/21/facebook-posts/disposable-homemade-masks-are-effective-stopping-a/ Spread 62 83 314
https://www.factcheck.org/2020/06/fake-aoc-tweet-politicizes-covid-19-business-restrictions/ Authorities 53 50 210
https://www.politifact.com/factchecks/2020/may/06/blog-posting/dont-fall-conspiracy-about-dr-anthony-fauci-hydrox/ Cure 52 8 275
https://www.politifact.com/factchecks/2020/jun/23/viral-image/no-aoc-didnt-tweet-about-closing-businesses-until-/ Authorities 29 17 322
https://efectococuyo.com/cocuyo-chequea/amazon-no-reparte-ayudas-alimentarias/ Other 26 21 178
https://www.factcheck.org/2020/05/false-perception-of-covid-19s-impact-on-the-homeless/ Spread 24 2 61
https://efectococuyo.com/cocuyo-chequea/hipoxia-mascarillas/ Other 22 21 246

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/).