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 17 April 2023 and Monday 24 April 2023.

256,285 Misinforming Tweets
New:+46 Trend:-21
93,004 Fact-checking Tweets
New:+87 Trend:-113
6,130 Fact-checks
144 Fact-checking Organisations

Key Content and Provenance

During the period between Monday 17 April 2023 and Monday 24 April 2023, 46 new URLs have been identified as potential misinforming content. Out of the 301 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from correctiv.org with an increase of +31 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 15min.lt 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 afp.com with a change of +10 compared to the previous total spread for the same domain whereas the domain that saw the least relative change was mythdetector.ge with a change of -75 compared to the previous period.

The all time most important domain is twitter.com with a total of 183,771 URL shares and the least popular domain is 24-post.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://www.peterdutton.com.au/leader-of-the-opposition-transcript-interview-with-sabra-lane-abc-am Australian Associated Press peterdutton.com.au 9 0 10
https://sputniknews.lat/20230420/ucrania-se-beneficia-del-acuerdo-sobre-granos-pero-todo-podria-cambiar-si-rusia-suspende-el-trato--1138391044.html euvsdisinfo.eu sputniknews.lat 8 0 8
https://sputniknews.lat/20230416/defensa-rusa-periodistas-franceses-arriban-a-ucrania-para-realizar-una-operacion-de-falsa-bandera-1138220498.html euvsdisinfo.eu sputniknews.lat 6 40 46
https://www.youtube.com/watch?v=LAU3ktiq5yg Fundacja “Przeciwdziałamy Dezinformacji” youtube.com 4 0 775
https://fr.sputniknews.africa/20230423/la-crimee-etait-tout-au-debut-a-la-russie-lambassadeur-chinois-en-france-rend-paris-consterne-1058797329.html euvsdisinfo.eu sputniknews.africa 3 0 3
https://oroszhirek.hu/a-holland-hirszerzes-szerint-oroszorszag-nem-csak-ukrajnaval-hanem-a-nyugattal-harcol/ euvsdisinfo.eu oroszhirek.hu 3 0 3
https://oroszhirek.hu/zaharova-varso-csak-addig-targyal-kijevvel-amig-szuksege-van-ra-mint-oroszellenes-eszkoztarra/ euvsdisinfo.eu oroszhirek.hu 3 0 3
https://vz.ru/news/2023/4/17/1207901.html euvsdisinfo.eu vz.ru 3 0 3
https://eadaily.com/ru/news/2023/04/23/pervyy-poshel-v-polshe-nabirayut-litovsko-polsko-ukrainskiy-korpus-dlya-okopov-ukrainy euvsdisinfo.eu eadaily.com 2 0 2
https://twitter.com/runews/status/1622170096675299329 AFP Fact Check twitter.com 1 10 2449

Table 1: Top misinforming content.

Fact-check URL Domain Current Week Previous Week Total
https://correctiv.org/faktencheck/2023/04/17/nein-dieses-video-zeigt-nicht-wie-die-ukraine-kriegsaufnahmen-inszeniert/ correctiv.org 17 0 17
https://correctiv.org/faktencheck/2023/04/19/nein-dieses-video-zeigt-keinen-brandanschlag-auf-eine-kirche-in-der-ukraine/ correctiv.org 8 0 8
https://euvsdisinfo.eu/report/ukrainian-grain-does-ot-reach-the-poorest-countries euvsdisinfo.eu 6 0 6
https://factuel.afp.com/doc.afp.com.32KN6HY afp.com 5 2 138
https://factual.afp.com/doc.afp.com.33DK2UR afp.com 5 0 5
https://www.stopfake.org/en/international-media-regurgitating-old-fakes-about-the-azov-battalion-and-neo-nazism-in-ukraine/ stopfake.org 3 2 381
https://factcheck.afp.com/doc.afp.com.33A99GQ afp.com 3 2 13
https://www.factcheck.org/2022/03/marjorie-taylor-greene-parrots-russian-talking-point-on-ukraine/ factcheck.org 3 1 257
https://facta.news/fuori-contesto/2022/03/08/luomo-in-questa-foto-non-e-il-fotoreporter-andrea-rocchelli/ facta.news 3 0 51
https://mythdetector.ge/en/olena-zelenska-or-millionaire-richard-heart-who-is-actually-depicted-on-the-fabricated-photo/ mythdetector.ge 3 0 11

Table 2: Top fact-checked content.

Fact-Checkers and Spreaders Location

The data used for creating the Twitter dataset is obtained from 144 fact-checking organisations.

The largest amount of fact-checked content comes from euvsdisinfo.eu (1,076 fact-checks) and the least from Verify Sy (1 fact-checks). Most fact-checked content are from VoxCheck (352) followed by Factcheck.ge (277) and AFP fact checking (263) (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
U [‘Country in eastern europe’]. 136 261 138903
B [‘Capital city of iraq’]. 39 61 14183
E [‘Third planet from the sun in the solar system’]. 29 82 18843
S [‘Sculpture on liberty island in new york harbor in new york city, new york, united states’]. 29 17 8755
A [‘Country in central and south asia’]. 23 2 6028
M [‘Country in north america’]. 21 11 5608
P [‘Country in central europe’]. 19 23 15771
I [‘Country in western asia’]. 14 11 24706
G [‘Country in central europe’]. 13 59 15941
T [‘Country in southeast asia’]. 12 0 8411

Table 3: Top locations mentioned in misinforming posts.

Person Description Current Week Previous Week Total
V [‘President of russia (1999–2008, 2012–present)’]. 29 37 63301
B [‘King of thailand (1927-2016)’]. 9 2 6203
O [‘Ukrainian politician and entertainer’]. 6 2 2180
A [‘Austrian-born german politician, dictator of germany from 1933 until his death in 1945’]. 3 1 4826
S [‘Ukrainian diplomat’]. 3 1 754
U [‘President of the european commission since 2019’]. 2 10 4962
E [‘President of france since 2017’]. 1 13 8042
H [‘Ukrainian energy minister’]. 1 4 2285
I [‘British actor’]. 1 1 848
P [‘266th pope of the catholic church’]. 1 0 2498

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.