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Between Monday 25 January 2021 and Monday 01 February 2021, misinformation about Other 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 25 January 2021 and Monday 01 February 2021.

266,392 Misinforming Tweets
New:+827 Trend:+410
122,430 Fact-checking Tweets
New:+1,822 Trend:+44
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 25 January 2021 and Monday 01 February 2021, 827 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 Other with an increase of +711 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 Symptoms with a change of +9 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 Other with a change of +476 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Other with a change of -161 compared to the previous period.

The all time most important topic is Authorities with a total of 117,797 URL shares and the least popular topic is Symptoms with 2,819 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://caracol.com.co/programa/2021/01/28/6am_hoy_por_hoy/1611839595_965667.html La Silla Vacía Other 490 0 490
https://www.worldometers.info/ Agencia Ocote Authorities 177 187 30073
https://brasilsemmedo.com/tratamento-precoce-comeca-a-ser-visto-com-outros-olhos/ Agência Lupa Cure 57 0 57
https://www.zerohedge.com/geopolitical/coronavirus-contains-hiv-insertions-stoking-fears-over-artificially-created-bioweapon FactCheck.org Conspiracy Theory 13 6 3004
https://www.lastampa.it/esteri/2020/05/04/news/test-sul-sangue-effettuati-in-giappone-rivela-la-mortalita-da-coronavirus-e-di-gran-lunga-inferiore-all-influenza-1.38801430 Open Other 12 3 571
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 10 8 1896
https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm Détecteur de rumeurs Other 8 7 1540
https://twitter.com/marthaperaltae/status/1352249681976418309 La Silla Vacía Vaccine 7 100 107
https://www.frontliner.com.br/oms-condena-lockdown-nao-salva-vidas-e-torna-os-pobres-muito-mais-pobres/ Estadão Verifica Other 6 5 171
https://www.contrafatos.com.br/181-americanos-morreram-de-vacinas-contra-covid-19-em-apenas-2-semanas/ Agência Lupa Vaccine 6 0 6

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.buzzfeed.com/jp/yutochiba/covid-19-vaccine Vaccine 455 0 455
https://efectococuyo.com/cocuyo-chequea/gargaras-cafe-sal-crema-dental-licor-ayudan-prevenir-covid-19/ Cure 37 14 51
https://www.aosfatos.org/noticias/e-falso-que-doria-fechou-contrato-com-chineses-em-2019-para-vacina-contra-o-novo-coronavirus/ Conspiracy Theory 36 3 83
https://piaui.folha.uol.com.br/lupa/2021/01/22/verificamos-estados-unidos-recomendar-hidroxicloroquina/ Cure 33 186 219
https://factuel.afp.com/covid-19-des-tests-positifs-sur-du-coca-ou-de-la-compote-ne-prouvent-pas-leur-inutilite Conspiracy Theory 32 1 54
https://www.politifact.com/factchecks/2020/mar/27/donald-trump/fact-checking-whether-biden-called-trump-xenophobi/ Authorities 31 6 665
https://www.factcheck.org/2021/01/scicheck-viral-posts-distort-who-guidance-on-covid-19-tests/ Authorities 28 0 28
https://maldita.es/malditobulo/20210125/estudio-nature-entre-lineas-pcr-falso-positivo-lopez-mirones/ Spread 24 0 24
https://lasillavacia.com/detector-no-las-vacunas-covax-no-son-regaladas-oms-79834 Vaccine 22 26 48
https://www.factcheck.org/2021/01/scicheck-hank-aarons-death-attributed-to-natural-causes/ Cure 22 0 22

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/).