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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 22 November 2021 and Monday 29 November 2021.

144 Misinforming Tweets
New:+50 Trend:+1
24 Fact-checking Tweets
New:+13 Trend:+13
1,444 Fact-checks
112 Fact-checking Organisations

Key Content and Provenance

During the period between Monday 22 November 2021 and Monday 29 November 2021, 50 new URLs have been identified as potential misinforming content. Out of the 22 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from tass.ru with an increase of +27 compared to the previous total spread for the same domain The domain that saw the least increase in spread compared to the previous period total spread was ahram.org.eg with a change of +0 compared to the previous total spread for the same domain

In relation to the previous week, the domain that saw the biggest relative spread change was tass.ru with a change of +17 compared to the previous total spread for the same domain whereas the domain that saw the least relative change was rt.com with a change of -18 compared to the previous period.

The all time most important domain is tass.ru with a total of 37 URL shares and the least popular domain is alkhabrpress.com with 1 shares (Figure 2).

Figure 1: Domain importance.

Figure 2: Amount of domains 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 Domain Current Week Previous Week Total
https://tass.ru/politika/12996745 euvsdisinfo.eu tass.ru 27 0 27
https://ria.ru/20211123/ukraina-1760222035.html euvsdisinfo.eu ria.ru 13 0 13
https://mundo.sputniknews.com/20211126/la-ue-pide-a-rusia-que-deje-morir-a-la-poblacion-de-donbas-1118698509.html euvsdisinfo.eu sputniknews.com 8 0 8
https://de.rt.com/meinung/127342-maas-le-drian-briefwechsel-lawrow/ euvsdisinfo.eu rt.com 2 20 22
https://tass.ru/politika/12949269 euvsdisinfo.eu tass.ru 0 10 10
https://ria.ru/20211118/ukraina-1759559193.html euvsdisinfo.eu ria.ru 0 8 8
https://snanews.de/20211119/russland-provokationen-nato-eu-lawrow-4386461.html euvsdisinfo.eu snanews.de 0 3 3
https://sputniknews.com/20211115/stoltenberg-says-no-consensus-for-ukraine-to-join-nato-warns-russia-against-aggressive-actions-1090750629.html euvsdisinfo.eu sputniknews.com 0 3 3
https://gate.ahram.org.eg/News/3139557.aspx euvsdisinfo.eu ahram.org.eg 0 2 2
https://ria.ru/20211118/london-1759545126.html euvsdisinfo.eu ria.ru 0 1 1

Table 1: Top misinforming content.

Fact-check URL Domain Current Week Previous Week Total
https://www.stopfake.org/ru/fejk-v-ukraine-protokoly-lecheniya-covid-19-prestupnye/ stopfake.org 10 0 10
https://euvsdisinfo.eu/report/the-us-desperately-needs-a-little-war-with-russia-pushes-ukraine-towards-escalation euvsdisinfo.eu 3 0 3
https://www.stopfake.org/ru/manipulyatsiya-v-ukraine-vvodyat-shtrafy-dlya-nevaktsinirovannyh/ stopfake.org 0 0 11

Table 2: Top fact-checked content.

Fact-Checkers and Spreaders Location

The data used for creating the Twitter dataset is obtained from 112 fact-checking organisations.

The largest amount of fact-checked content comes from euvsdisinfo.eu (154 fact-checks) and the least from Verificat (1 fact-checks). Most fact-checked content are from AFP fact checking (138) followed by Fact Crescendo (81) and Check Your Fact (59) (Figure 3).

Figure 3: Amount of fact-checks by fact-checkers.

Figure 4: Identified location of users spreading fact-checks and misinformation.

Locations and Mentions

Using automatic entity extraction methods, we identify key locations and persons mention in the fact-checking articles in order to identify what location and person are the most discussed in misinforming content.

The top mentioned locations and persons are listed in Table 3 and Table 4.

Location Description Current Week Previous Week Total

Table 3: Top locations mentioned in misinforming posts.

Person Description Current Week Previous Week Total

Table 4: Top people mentioned in misinforming posts.

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 5: 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.