Francesco Gesualdo

The model of the attention economy, where content producers compete for the attention of users, relies on two key forces: information supply and demand. This study leverages the feedback loop between these forces to develop a method for detecting and quantifying information voids, i.e., periods in which little or no reliable information is available on a given topic. Using a case study on COVID-19 vaccines rollout in six European countries, and drawing on data from multiple platforms including Facebook, Google, Twitter, Wikipedia, and online news outlets, we examine how information voids emerge, persist and correlate with a decline in the proportion of high-quality information circulating online. By conceptualising information voids as a specific regime of information spreading, we also quantify their counterpart, information overabundance, which constitute a central component of the current definition of infodemic. We show that information voids are associated with a higher prevalence of misinformation, thus representing problematic hotspots in which individuals are more likely to be misled by low-quality online content. Overall, our findings provide empirical support for the inclusion of information voids in mechanistic explanations of misinformation emergence.

Infodemics are a threat to public health, arising from multiple interacting phenomena occurring both online and offline. The continuous feedback loops between the digital information ecosystem and offline contingencies make infodemics particularly challenging to define operationally, measure, and eventually model in quantitative terms. In this study, we present evidence of the effect of various epidemic-related variables on the dynamics of infodemics, using a robust modelling framework applied to data from 30 countries across diverse income groups. We use WHO COVID-19 surveillance data on new cases and deaths, vaccination data from the Oxford COVID-19 Government Response Tracker, infodemic data (volume of public conversations and social media content) from the WHO EARS platform, and Google Trends data to represent information demand. Our findings show that new deaths are the strongest predictor of the infodemic, measured as new document production including social media content and public conversations, and that the epidemic burden in neighbouring countries appears to have a greater impact on document production than the domestic one. Building on these results, we propose a taxonomy that highlights country-specific discrepancies between the evolution of the infodemic and the epidemic. Further, an analysis of the temporal evolution of the relationship between the two phenomena quantifies how much the discussions around vaccine rollouts may have shaped the development of the infodemic. The insights from our quantitative model contribute to advancing infodemic research, highlighting the importance of a holistic approach integrating both online and offline dimensions.