Through the analysis of millions of geolocated tweets collected during the Covid-19 pandemic we were able to identify the existence of structural and functional network features supporting an “illusion of the majority" on Twitter. Our results suggest that the majority of fake (and other) contents related to the pandemic are produced by a minority of users and that there is a structural segmentation in a small “core” of very active users responsible for large amount of fake news and a larger "periphery" that mainly retweets the contents produced by the core. This discrepancy between the size and identity of users involved in the production and diffusion of fake news suggests that a distorted perception of what users believe is the majority opinion may pressure users (especially those in the periphery) to comply with the group norm and further contribute to the spread of misinformation in the network.

Top-down “debunking” interventions have been applied to limit the spread of fake news, but so far with limited power. Recognizing the role of social norms in the context of misinformation fight may offer a novel approach to solve such a challenge, shifting to bottom-up solutions that help people to correct misperceptions about how widely certain opinions are truly held. The results of this microproject can inform new strategies to improve the quality of debates in online communities and counteract polarization in online communities (WP4). These results can be also relevant for WP2 (T 2.4), e.g., by giving insights about how human interactions can influence and are influenced by AI technology, WP3 (T 3.3) by offering tools to study the reactions of humans within hybrid human-AI systems and WP5 (T 5.4) by evaluating the role of social norms dynamics for a responsible development of AI technology.