Between Monday 16 December 2019 and Monday 23 December 2019, misinformation about Authorities has increasead whereas misinformation about Other has reduced.

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 16 December 2019 and Monday 23 December 2019.

42 Misinforming Tweets
New:+0 Trend:-20
0 Fact-checking Tweets
New:+0 Trend:+0
16,386 Fact-checks
101 Fact-checking Organisations

Key Content and Topics

During the period between Monday 16 December 2019 and Monday 23 December 2019, there was no new misinformation posts shared.

The all time most important topic is Other with a total of 34 URL shares and the least popular topic is Authorities with 3 shares (Figure 2).

Figure 1: Topic Importance.

Figure 2: Amount of topic shares per week.


The data used for creating the Twitter dataset is obtained from the Poynter Coronavirus Fact Alliance. The alliance consists of 101 fact-checking organisation based in 1000 countries and covering 46 languages.

The largest amount of fact-checked content comes from English (8,706 fact-checks) and the least is Finland (1 fact-checks). Most fact-checked content is in Spanish (4,577) followed by Portuguese (2,801) and Ukrainian (2,073) (Figure 3).

Figure 3: Amount of fact-checks by language.

Figure 4: Amount of fact-checked content per contry.

Determining a direct impact of fact-checking on the spread of misinformation is not easy. However, it is possible to determine how well a particular corrective information is spreading in relation to its corresponding misinformation.

Figure 5 shows how misinformation and fact-checking content has spread in various topics for the last two analysis periods and overall.

Figure 5: Topical misinformation and fact-checks spread.

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 6: 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.