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Between Monday 10 February 2020 and Monday 17 February 2020, misinformation about Symptoms 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 10 February 2020 and Monday 17 February 2020.

18,354 Misinforming Tweets
New:+1,261 Trend:-5,384
2,636 Fact-checking Tweets
New:+738 Trend:+123
10,803 Fact-checks
98 Fact-checking Organisations

Key Content and Topics

During the period between Monday 10 February 2020 and Monday 17 February 2020, 1,261 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 Conspiracy Theory with an increase of +617 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 Causes with a change of +24 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 +36 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 -1,954 compared to the previous period.

The all time most important topic is Conspiracy Theory with a total of 7,092 URL shares and the least popular topic is Symptoms with 80 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://twitter.com/inteldotwav/status/1226267582740811777 Teyit Other 424 1419 1843
https://www.taiwannews.com.tw/en/news/3871594 Décrypteurs - Radio-Canada Spread 162 2124 2286
https://twitter.com/BoysBluegrass/status/1226494284603412480 BuzzFeed Japan Conspiracy Theory 116 0 116
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 95 631 726
https://www.worldometers.info/ Agencia Ocote Authorities 84 79 390
https://ufospotlight.wordpress.com/2020/02/13/chinese-intelligence-officer-reveals-true-magnitude-of-chinas-coronavirus-crisis/ BOOM FactCheck Conspiracy Theory 83 0 83
https://www.washingtontimes.com/news/2020/jan/26/coronavirus-link-china-biowarfare-program-possible/ BuzzFeed Japan Conspiracy Theory 42 75 666
https://www.thailandmedical.news/news/breaking-news-latest-research-published-by-chinese-scientists-say-coronavirus-will-render-most-male-patients-infertile LeadStories Symptoms 37 0 37
https://www.zerohedge.com/geopolitical/coronavirus-contains-hiv-insertions-stoking-fears-over-artificially-created-bioweapon FactCheck.org Conspiracy Theory 27 200 2539
https://www.the-scientist.com/news-opinion/lab-made-coronavirus-triggers-debate-34502 LeadStories Conspiracy Theory 25 37 314

Table 1: Top misinforming content.

Fact-check URL Topic Current Week Previous Week Total
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-10 Spread 138 0 138
https://www.factcheck.org/2020/02/baseless-conspiracy-theories-claim-new-coronavirus-was-bioengineered/ Conspiracy Theory 56 36 92
https://fullfact.org/health/satellites-wuhan-sulphur-dioxide-coronavirus/ Conspiracy Theory 46 0 46
https://www.buzzfeed.com/jp/kotahatachi/unknown-cause-china-11 Conspiracy Theory 41 0 41
https://healthfeedback.org/claimreview/2019-novel-coronavirus-2019-ncov-does-not-contain-pshuttle-sn-sequence-no-evidence-that-virus-is-man-made/ Conspiracy Theory 27 0 27
https://www.boomlive.in/fake-news/video-shows-chinese-policemen-killing-coronavirus-patients-factcheck-6885 Authorities 27 0 27
https://factcheck.afp.com/hoax-report-claims-china-sought-supreme-court-approval-euthanise-20000-coronavirus-patients Other 25 0 25
https://www.indiatoday.in/fact-check/story/china-permission-court-kill-thousands-coronavirus-petients-viral-1644347-2020-02-07 Authorities 18 11 29
https://efectococuyo.com/cocuyo-chequea/medicamento-cubano-coronavirus/ Cure 15 0 15
https://www.buzzfeed.com/jp/yutochiba/coronavirus-medical-fact-check Conspiracy Theory 11 56 67

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/).