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 25 April 2022 and Monday 02 May 2022.
New:+1,574 Trend:+357
71,559 Fact-checking Tweets
New:+1,055 Trend:-371
3,576 Fact-checks
135 Fact-checking Organisations
Key Content and Provenance
During the period between Monday 25 April 2022 and Monday 02 May 2022, 1,574 new URLs have been identified as potential misinforming content. Out of the 205 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from twitter.com with an increase of +681 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 24-post.com 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.com with a change of +222 compared to the previous total spread for the same domain whereas the domain that saw the least relative change was facta.news with a change of -360 compared to the previous period.
The all time most important domain is twitter.com with a total of 110,309 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://twitter.com/tjournal/status/1518604476566933505 | LeadStories | twitter.com | 454 | 0 | 454 |
| https://rumble.com/v10miez-world-premiere-watch-the-water.html | LeadStories | rumble.com | 272 | 392 | 6194 |
| https://tass.com/politics/1445755 | euvsdisinfo.eu | tass.com | 225 | 0 | 225 |
| https://twitter.com/jcokechukwu/status/1518351056987697157 | LeadStories | twitter.com | 187 | 27 | 214 |
| https://ria.ru/20220425/zelenskiy-1785329873.html | Stopfake.org | ria.ru | 143 | 0 | 143 |
| https://www.youtube.com/watch?v=6LQSYmda0Sc | LeadStories | youtube.com | 62 | 96 | 8513 |
| https://www.youtube.com/watch?v=X1b-RKDHZOo | LeadStories | youtube.com | 61 | 43 | 2257 |
| https://www.kp.ru/daily/27385/4579744 | LeadStories | kp.ru | 48 | 0 | 48 |
| https://thefauxy.com/pakistan-imposes-economic-sanctions-on-russia-refuses-to-repay-loans | Taiwan FactCheck Center | thefauxy.com | 14 | 14 | 3940 |
| https://www.globaltimes.cn/page/202203/1254217.shtml | StopFake.org | globaltimes.cn | 12 | 31 | 1396 |
Table 1: Top misinforming content.
Table 2: Top fact-checked content.
Fact-Checkers and Spreaders Location
The data used for creating the Twitter dataset is obtained from 135 fact-checking organisations.
The largest amount of fact-checked content comes from euvsdisinfo.eu (262 fact-checks) and the least from Verificat (1 fact-checks). Most fact-checked content are from LeadStories (206) followed by AFP fact checking (188) and Factcheck.ge (138) (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. | 10 | 27 | 4006 |
| Volodymyr-Volynskyi | City located in volyn oblast, in north-western ukraine. | 6 | 0 | 6 |
| United States of America | Sovereign state in north america. | 0 | 8 | 3203 |
| Crimean Peninsula | Peninsula on the northern coast of the black sea. | 0 | 8 | 141 |
| Moscow | Capital and most populous city of russia. | 0 | 7 | 3088 |
| Black Sea | Sea between europe and asia. | 0 | 7 | 7 |
| Russia | Sovereign state in eastern europe and northern asia. | 0 | 1 | 3490 |
| Japan | Sovereign state in east asia, situated on an archipelago of five main and over 6,800 smaller islands. | 0 | 1 | 409 |
| Europe | Continent on earth, mainly on the northeastern quadrant, i.e. north-western eurasia. | 0 | 1 | 154 |
| Spain | Sovereign state in southwestern europe with territories in the mediterranean sea, the atlantic ocean and northern africa. | 0 | 1 | 154 |
Table 3: Top locations mentioned in misinforming posts.
| Person | Description | Current Week | Previous Week | Total |
|---|---|---|---|---|
| Vladimir Putin | 2nd and 4th president of russia. | 6 | 0 | 7 |
| Elon Musk | South african-born american entrepreneur. | 6 | 0 | 6 |
| Petro Poroshenko | Ukrainian businessman and politician. | 6 | 0 | 6 |
| Donald Trump | 45th and current president of the united states. | 4 | 19 | 291 |
| Hunter Biden | American lawyer, investment advisor, and second son of former vice president joe biden. | 4 | 19 | 291 |
| Joe Biden | President-elect of the united states, former vice president (2009–2017). | 4 | 19 | 291 |
| Volodymyr Zelenskyi | 6th president of ukraine. | 0 | 2 | 583 |
| Ukraine | Sovereign state in eastern europe. | 0 | 1 | 409 |
| Barack Obama | 44th president of the united states. | 0 | 1 | 134 |
| Donald Trump Jr. | American businessman and son of u.s. President donald trump. | 0 | 1 | 134 |
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.