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 30 January 2023 and Monday 06 February 2023.
New:+429 Trend:+366
90,741 Fact-checking Tweets
New:+263 Trend:+72
5,776 Fact-checks
143 Fact-checking Organisations
Key Content and Provenance
During the period between Monday 30 January 2023 and Monday 06 February 2023, 429 new URLs have been identified as potential misinforming content. Out of the 287 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from twitter.com with an increase of +308 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 twitter.com with a change of +305 compared to the previous total spread for the same domain whereas the domain that saw the least relative change was correctiv.org with a change of -19 compared to the previous period.
The all time most important domain is twitter.com with a total of 180,880 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.
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 143 fact-checking organisations.
The largest amount of fact-checked content comes from euvsdisinfo.eu (840 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 (255) (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’]. | 797 | 255 | 130909 |
| P | [‘Country in central europe’]. | 407 | 64 | 12869 |
| G | [‘Country in central europe’]. | 403 | 71 | 12747 |
| I | [‘Country in western asia’]. | 311 | 1 | 22433 |
| L | [‘Sovereign state in western asia’]. | 307 | 2 | 2446 |
| S | [‘Sculpture on liberty island in new york harbor in new york city, new york, united states’]. | 89 | 29 | 8360 |
| R | [‘Statue in chadron, united states of america - frédéric auguste bartholdi - 1950’]. | 49 | 16 | 8044 |
| E | [‘Continent’]. | 37 | 60 | 17704 |
| F | [‘Italian comune’]. | 26 | 27 | 3029 |
| H | [‘Special administrative region of china’]. | 23 | 21 | 349 |
Table 3: Top locations mentioned in misinforming posts.
| Person | Description | Current Week | Previous Week | Total |
|---|---|---|---|---|
| E | [‘President of france since 2017’]. | 323 | 13 | 5697 |
| U | [‘President of the european commission since 2019’]. | 307 | 0 | 2774 |
| V | [‘President of russia (1999–2008, 2012–present)’]. | 82 | 81 | 60754 |
| J | [‘American educator, wife of joe biden and first lady of the united states’]. | 45 | 20 | 15309 |
| M | [‘Ukrainian politician’]. | 39 | 13 | 3049 |
| R | [‘President of turkey since 2014’]. | 38 | 2 | 2610 |
| H | [‘Ukrainian energy minister’]. | 24 | 10 | 2218 |
| D | [‘President of the united states from 2017 to 2021’]. | 18 | 10 | 3504 |
| O | [‘Ukrainian politician and entertainer’]. | 9 | 2 | 2100 |
| B | [‘King of thailand (1927-2016)’]. | 4 | 6 | 2188 |
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.