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 04 April 2022 and Monday 11 April 2022.
New:+0 Trend:-12
48,660 Fact-checking Tweets
New:+0 Trend:-100
1,444 Fact-checks
112 Fact-checking Organisations
Key Content and Provenance
During the period between Monday 04 April 2022 and Monday 11 April 2022, there was no new misinformation posts shared.
The all time most important domain is afp.com with a total of 19,815 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.
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 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. | 0 | 95 | 49261 |
| United States of America | Sovereign state in north america. | 0 | 39 | 8707 |
| Soviet Union | Federal socialist state in eastern europe and northern asia (1922–1991). | 0 | 24 | 6562 |
| Georgia | Country in the caucasus. | 0 | 18 | 2495 |
| Afghanistan | Sovereign state situated at the confluence of western, central, and south asia. | 0 | 16 | 1208 |
| Earth | Third planet from the sun in the solar system. | 0 | 15 | 5386 |
| Russia | Sovereign state in eastern europe and northern asia. | 0 | 11 | 8897 |
| Europe | Continent on earth, mainly on the northeastern quadrant, i.e. north-western eurasia. | 0 | 8 | 4549 |
| Pakistan | Sovereign state in south asia. | 0 | 8 | 3895 |
| Germany | Sovereign state in central europe. | 0 | 7 | 2901 |
Table 3: Top locations mentioned in misinforming posts.
| Person | Description | Current Week | Previous Week | Total |
|---|---|---|---|---|
| Vladimir Putin | 2nd and 4th president of russia. | 0 | 42 | 21035 |
| Volodymyr Zelenskyi | 6th president of ukraine. | 0 | 13 | 11764 |
| Petro Poroshenko | Ukrainian businessman and politician. | 0 | 7 | 516 |
| Ahed Al-Tamimi | Palestinian activist. | 0 | 7 | 164 |
| Americans | Citizens or residents of the united states of america. | 0 | 7 | 40 |
| Joe Biden | President-elect of the united states, former vice president (2009–2017). | 0 | 6 | 1565 |
| Donald Trump | 45th and current president of the united states. | 0 | 6 | 298 |
| Hunter Biden | American lawyer, investment advisor, and second son of former vice president joe biden. | 0 | 6 | 239 |
| Boris Yeltsin | Soviet and russian politician, 1st president of russia (1931-2007). | 0 | 5 | 677 |
| Mikhail Tolstykh | Commander of the pro-russian forces. | 0 | 5 | 10 |
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.