We believe that fostering global digital activism constitutes a necessary means to fight the current info-pandemic.
The majority of fact-checking and myth-busting sites (e.g. EUvsDisinfo) counter false narratives and news that have already become viral, unable to prevent their spread. Furthermore, AI techniques (such as http://www.fakenewschallenge.org) are currently not accurate enough to replace humans in generalised fact-checking. This is especially the case when the news does not contain fabricated information (disinformation), but it is framed in such a way that true evidence is used to draw false generalizations and evaluations (Wardle 2019), resulting in semi-fake news.
Leveraging NLP techniques for topic modelling and frame analysis (Das et al. 2010) we will trace the topics and frames which characterize semi-fake COVID-19 news using FullFact and the Coronavirus debunking archive built by First Draft as benchmarks.
We will identify the fallacious reasonings in the sample and use the results to compile a set of guidelines about how to detect semi-fake COVID-19 news. These principles will be operationalised in a digital platform with a chatbot for training citizens to spot misinformation. Citizens who have been trained will have access to the Fake News Immunity platform, working together with experts in the common effort of flagging semi-fake news.
Meet the team