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AbstractIn response to the ever-increasing spread of online disinformation and misinformation, several human–computer interaction tools to enhance data literacy have been developed. Among them, many employ elements of gamification to increase user engagement and reach out to a broader audience. However, there are no systematic criteria to analyze their relevance and impact for building fake news resilience, partly due to the lack of a common understanding of data literacy. In this paper we put forward an operationalizable definition of data literacy as a form of multidimensional critical thinking. We then survey 22 existing tools and classify them according to a framework of 10 criteria pointing to their gameful design and educational features. Through a comparative/contrastive analysis informed by a focus group, we provide a principled set of guidelines to develop more efficient human–computer interaction tools to teach how to critically think in the current media ecosystem.

The hype on generative AI has raised concerns about the spread of disinformation but also opened up new opportunities for hybrid journalism. The proliferation of political propaganda campaigns spread across digital media during election periods constitutes a challenge for journalists who struggle to exercise information gatekeeping. AI-based tools can in principle help, but journalists are resistant to using them since they are sceptical about their compliance with news values and their scarce user-friendliness. To cope with such issues, we present Botlitica, a GPT3-based chatbot able to answer users' questions according to the information shared on different social media by a given political party which is, thus, embodied by the AI agent in the conversation. The back-end and front-design of the chatbot are devised to privilege transparency. We report the results of a preliminary evaluation of Botlitica which show that the tool fastens journalists' capabilities to navigate propaganda campaigns inducing them to exercise critical thinking.

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt 'official' information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown (8–8.30 p.m. 23 March 2020) on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n = 2,531,888). We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: 'government and policy', 'symptoms', 'pushing back against misinformation' and 'cures and treatments'.