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 03 January 2022 and Monday 10 January 2022.
Key Content and Provenance
During the period between Monday 03 January 2022 and Monday 10 January 2022, 35 new URLs have been identified as potential misinforming content. Out of the 32 domains identified by Fact-checking organisations (Figure 1), most of the new shared URLs were from ria.ru with an increase of +32 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 ria.ru with a change of +32 compared to the previous total spread for the same domain whereas the domain that saw the least relative change was sputniknews.com with a change of -7 compared to the previous period.
The all time most important domain is ria.ru with a total of 308 URL shares and the least popular domain is alkhabrpress.com with 1 shares (Figure 2).
The top misinforming content and fact-checking articles shared since the last report are listed in Table 1 and Table 2.
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).
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|
|Person||Description||Current Week||Previous Week||Total|
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.
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.