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 07 March 2022 and Monday 14 March 2022.
New:+1,633 Trend:-5,765
43,913 Fact-checking Tweets
New:+8,962 Trend:-7,854
1,444 Fact-checks
112 Fact-checking Organisations
Key Content and Provenance
During the period between Monday 07 March 2022 and Monday 14 March 2022, 1,633 new URLs have been identified as potential misinforming content. Out of the 123 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from afp.com with an increase of +4,056 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 open.online with a change of +706 compared to the previous total spread for the same domain whereas the domain that saw the least relative change was thefauxy.com with a change of -3,385 compared to the previous period.
The all time most important domain is afp.com with a total of 17,580 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.
Table 1: Top misinforming content.
| Fact-check URL | Domain | Current Week | Previous Week | Total |
|---|---|---|---|---|
| https://www.politifact.com/factchecks/2022/feb/25/tweets/there-are-no-us-run-biolabs-ukraine-contrary-socia/ | politifact.com | 976 | 234 | 1381 |
| https://www.open.online/2022/03/10/video-ospedale-pediatrico-bombardato-mariupol/ | open.online | 655 | 0 | 655 |
| https://www.factcheck.org/2022/03/social-media-posts-misrepresent-u-s-ukraine-threat-reduction-program/ | factcheck.org | 393 | 61 | 454 |
| https://factuel.afp.com/doc.afp.com.324X2ZF | afp.com | 363 | 0 | 363 |
| https://factchecknederland.afp.com/doc.afp.com.324E64F | afp.com | 330 | 9 | 339 |
| https://factuel.afp.com/doc.afp.com.324G3KA | afp.com | 267 | 0 | 267 |
| https://factcheck.afp.com/doc.afp.com.32478V9 | afp.com | 247 | 63 | 310 |
| https://factual.afp.com/doc.afp.com.324Q3EV | afp.com | 239 | 0 | 239 |
| https://ici.radio-canada.ca/nouvelle/1868082/bombardement-hopital-marioupol-propagande-armee-urkainienne-azov-actrie-influenceuse | radio-canada.ca | 222 | 0 | 222 |
| https://factuel.afp.com/doc.afp.com.324G8BG | afp.com | 218 | 0 | 218 |
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 |
|---|---|---|---|---|
| Ukraine | Sovereign state in eastern europe. | 9363 | 18915 | 44595 |
| United States of America | Sovereign state in north america. | 2635 | 1879 | 6594 |
| Russia | Sovereign state in eastern europe and northern asia. | 2549 | 4985 | 8405 |
| Soviet Union | Federal socialist state in eastern europe and northern asia (1922–1991). | 2150 | 2561 | 5675 |
| Earth | Third planet from the sun in the solar system. | 1658 | 1536 | 3856 |
| Moscow | Capital and most populous city of russia. | 1437 | 1458 | 3537 |
| Germany | Sovereign state in central europe. | 1386 | 660 | 2349 |
| Georgia | Country in the caucasus. | 1307 | 344 | 1837 |
| Europe | Continent on earth, mainly on the northeastern quadrant, i.e. north-western eurasia. | 982 | 832 | 4191 |
| France | Sovereign state with mainland in western europe and several overseas territories. | 855 | 679 | 2034 |
Table 3: Top locations mentioned in misinforming posts.
| Person | Description | Current Week | Previous Week | Total |
|---|---|---|---|---|
| Vladimir Putin | 2nd and 4th president of russia. | 3585 | 7356 | 19789 |
| Volodymyr Zelenskyi | 6th president of ukraine. | 2486 | 5064 | 10850 |
| Sergey Lavrov | Russian politician and foreign minister. | 627 | 5 | 649 |
| Joe Biden | President-elect of the united states, former vice president (2009–2017). | 615 | 316 | 1390 |
| Elizabeth Truss | British conservative party politician (born 1975). | 363 | 0 | 363 |
| Petro Poroshenko | Ukrainian businessman and politician. | 356 | 108 | 464 |
| Richard Nixon | 37th president of the united states of america (1913-1994). | 332 | 230 | 562 |
| French | Citizens or residents of france. | 292 | 123 | 415 |
| Filippa Lentzos | Researcher. | 247 | 63 | 310 |
| Nadiya Savchenko | Ukrainian politician. | 224 | 0 | 224 |
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.