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 July 2022 and Monday 01 August 2022.
New:+86 Trend:+39
77,224 Fact-checking Tweets
New:+109 Trend:+10
3,813 Fact-checks
138 Fact-checking Organisations
Key Content and Provenance
During the period between Monday 25 July 2022 and Monday 01 August 2022, 86 new URLs have been identified as potential misinforming content. Out of the 227 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from afp.com with an increase of +38 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 +23 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 -5 compared to the previous period.
The all time most important domain is twitter.com with a total of 154,858 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 138 fact-checking organisations.
The largest amount of fact-checked content comes from euvsdisinfo.eu (295 fact-checks) and the least from Verify Sy (1 fact-checks). Most fact-checked content are from LeadStories (255) followed by AFP fact checking (199) 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. | 120 | 90 | 145553 |
| Russia | Sovereign state in eastern europe and northern asia. | 32 | 25 | 23605 |
| United States of America | Sovereign state in north america. | 26 | 28 | 33912 |
| worldwide | Published or operating in multiple or all jurisdictions on earth; special value for “place of publication” (p291) and “operating area” (p2541). | 26 | 13 | 12975 |
| Americas | Landmass comprising the continents of north america and south america. | 25 | 13 | 6648 |
| Europe | Continent on earth, mainly on the northeastern quadrant, i.e. north-western eurasia. | 20 | 21 | 34550 |
| Spain | Sovereign state in southwestern europe with territories in the mediterranean sea, the atlantic ocean and northern africa. | 18 | 11 | 5226 |
| Madrid | Capital and largest city of spain. | 17 | 11 | 2169 |
| Italy | Sovereign state in southern europe. | 16 | 16 | 1621 |
| Neiva National Telegraph and Post Office Building | . | 16 | 11 | 60 |
Table 3: Top locations mentioned in misinforming posts.
| Person | Description | Current Week | Previous Week | Total |
|---|---|---|---|---|
| Vladimir Putin | 2nd and 4th president of russia. | 36 | 26 | 54032 |
| Anthony Fauci | American immunologist and head of the u.s. National institute of allergy and infectious diseases. | 25 | 13 | 6636 |
| Volodymyr Zelenskyy | Sixth and current president of ukraine. | 22 | 10 | 51891 |
| Leonardo da Vinci | Italian renaissance polymath (1452-1519). | 16 | 11 | 60 |
| President of the United States | Head of state and head of government of the united states of america. | 8 | 9 | 5806 |
| Donald Trump | 45th and current president of the united states. | 7 | 10 | 1869 |
| Bill Clinton | 42nd president of the united states. | 6 | 4 | 945 |
| Boris Yeltsin | Soviet and russian politician, 1st president of russia (1931-2007). | 5 | 6 | 896 |
| Mikhail Gorbachev | President of the soviet union from 1990 to 1991. | 5 | 4 | 796 |
| George H. W. Bush | 41st president of the united states (1924-2018). | 5 | 4 | 774 |
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.