Between Monday 13 December 2021 and Monday 20 December 2021, misinformation about Symptoms has increasead whereas misinformation about Vaccine 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 13 December 2021 and Monday 20 December 2021.
Key Content and Topics
During the period between Monday 13 December 2021 and Monday 20 December 2021, 899 new URLs have been identified as potential misinforming content. Out of the 8 topics identified by Fact-checking organisations (Figure 1), most of the new shared URLs were about Vaccine with an increase of +1,338 compared to the previous total spread for the same topic. The topic that saw the least increase in spread compared to the previous period total spread was Causes with a change of +1 compared to the previous total spread for the same topic.
The topics used for the analysis are obtained from the COVID-19 specific fact-check alliance database and are defined as follows:
- Authorities: Information relating to government or authorities communication and general involvement during the COVID-19 pandemic (e.g., crime, government, aid, lockdown).
- Causes: Information about the virus causes and outbreaks (e.g., China, animals).
- Conspiracy theories: COVID-19-related conspiracy theories (e.g., 5G, biological weapon).
- Cures: Information about potential virus cures (e.g., vaccines, hydroxychloroquine, bleach).
- Spread: Information relating to the spread of COVID-19 (e.g., travel, animals).
- Symptoms: Information relating to symptoms and symptomatic treatments of COVID-19 (e.g., cough, sore throat).
- Vaccines: Information relating to vaccines (e.g., side effects, effectiveness).
- Masks: Information concerning the usage of masks.
- Other: Any topic that does not fit directly the aforementioned categories.
In relation to the previous week, the topic that saw the biggest relative spread change was Conspiracy Theory with a change of +25 compared to the previous total spread for the same topic whereas the topic that saw the least relative change was Conspiracy Theory with a change of -910 compared to the previous period.
The all time most important topic is Authorities with a total of 132,729 URL shares and the least popular topic is Symptoms with 3,134 shares (Figure 2).
The top misinforming content and fact-checking articles shared since the last report are listed in Table 1 and Table 2.
|Misinforming URL||Fact-check URL||Topic||Current Week||Previous Week||Total|
|https://1scandal.com/etats-unis-la-cour-supreme-annule-la-vaccination-universelle/||La Silla Vacía||92||22||207|
|https://c19study.com||Détecteur de rumeurs||1||2||29285|
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 945 countries and covering 46 languages.
The largest amount of fact-checked content comes from English (8,207 fact-checks) and the least is Finland (1 fact-checks). Most fact-checked content is in Spanish (4,317) followed by Portuguese (2,541) and Ukrainian (1,956) (Figure 3).
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.
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.