13 min read

Between Monday 03 February 2020 and Monday 10 February 2020, misinformation about Spread has increasead whereas misinformation about Conspiracy Theory 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 03 February 2020 and Monday 10 February 2020.

17,093 Misinforming Tweets
New:+6,645 Trend:+911
1,898 Fact-checking Tweets
New:+615 Trend:-465
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 03 February 2020 and Monday 10 February 2020, 6,645 new URLs have been identified as potential misinforming content. Out of the 7 topics identified by Fact-checking organisations (Figure 1), most of the new shared URLs were about Spread with an increase of +2,343 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 +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 Spread with a change of +1,844 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Spread with a change of -3,381 compared to the previous period.

The all time most important topic is Conspiracy Theory with a total of 6,475 URL shares and the least popular topic is Symptoms with 33 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.taiwannews.com.tw/en/news/3871594 Décrypteurs - Radio-Canada Spread 2124 0 2124
https://twitter.com/inteldotwav/status/1226267582740811777 Teyit Other 1419 0 1419
https://twitter.com/globaltimesnews/status/1224569239253569538 Maldita.es Authorities 760 0 760
https://ab-tc.com/china-seek-for-courts-approval-to-kill-the-over-20000-coronavirus-patients-to-avoid-further-spread-of-the-virus/ Dubawa Authorities 631 0 631
https://twitter.com/brajeshlive/status/1225830935079161856 FactCrescendo Authorities 336 0 336
https://twitter.com/DiazCanelB/status/1225847292541968384 Efecto Cocuyo Cure 246 0 246
https://indeep.jp/found-hiv-in-wuhan-coronavirus/ BuzzFeed Japan Conspiracy Theory 214 562 776
https://www.zerohedge.com/geopolitical/coronavirus-contains-hiv-insertions-stoking-fears-over-artificially-created-bioweapon FactCheck.org Conspiracy Theory 200 2312 2512
https://twitter.com/iWolowitz/status/1224008311957147648 Colombiacheck Other 132 79 211
https://anonymous-post.mobi/archives/19466 BuzzFeed Japan Spread 80 0 80

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-4 Conspiracy Theory 57 0 57
https://www.buzzfeed.com/jp/yutochiba/coronavirus-medical-fact-check Conspiracy Theory 56 0 56
https://www.factcheck.org/2020/02/baseless-conspiracy-theories-claim-new-coronavirus-was-bioengineered/ Conspiracy Theory 36 0 36
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-8 Spread 26 0 26
https://www.boomlive.in/fake-news/false-china-seeks-court-approval-to-kill-over-20000-coronavirus-patients-6812 Authorities 22 0 22
https://www.liberation.fr/checknews/2020/02/07/l-entreprise-chinoise-tencent-a-t-elle-revele-par-accident-les-vrais-chiffres-du-coronavirus_1777479 Spread 19 0 19
https://www.lemonde.fr/les-decodeurs/article/2020/02/06/coronavirus-une-affiche-du-ministere-ecarte-trop-vite-le-risque-de-contagion-lors-de-l-incubation_6028658_4355770.html Causes 17 0 17
https://www.factcheck.org/2020/01/new-coronavirus-wasnt-predicted-in-simulation/ Conspiracy Theory 13 40 53
https://factcheck.afp.com/novel-coronavirus-health-experts-warn-against-steaming-face-masks-reuse-after-misinformation-chinese Cure 12 0 12
https://efectococuyo.com/cocuyo-chequea/no-reportado-coronavirus-monagas-guajira/ Spread 11 0 11

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/).