arXiv subjects

Francesco Gesualdo

Publications and source records attributed to Francesco Gesualdo.

3 recordsLinked to original sources

arXiv2026

Modelling infodemics on a global scale: A 30 countries study using epidemiological and social listening data

Infodemics represent a significant threat to public health, arising from complex interactions between online and offline phenomena. The continuous feedback loops between digital information ecosystems and real-world contingencies make infodemics particularly challenging to define operationally, measure, and eventually model in quantitative terms. This study aims to evaluate the effect of various epidemic-related variables on the dynamics of the COVID-19 infodemic, using a regression modeling framework applied to data from 30 countries across diverse income groups. We use World Health Organization (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 document production, and that the epidemic burden in neighboring countries exerts a greater influence on document production than domestic epidemic conditions. Building on these results, we propose a data-driven classification of country-level response 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 the extent to which discussions surrounding vaccine rollouts may have shaped the development of the infodemic. Beyond underscoring the value of a holistic approach that integrates both online and offline dimensions, our results demonstrate that the evolution of infodemics and their relationship with epidemic variables can be closely monitored, even over short time windows.

cs.SI↗

arXiv2026

How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches

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.

cs.CY↗

arXiv2013

An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices

The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios and designing containment strategies. However, the integration of detailed data sources yields models that are less transparent and general. Hence, given a specific disease model, it is crucial to assess which representations of the raw data strike the best balance between simplicity and detail. We consider high-resolution data on the face-to-face interactions of individuals in a hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the contact patterns. We show that a contact matrix that only contains average contact durations fails to reproduce the size of the epidemic obtained with the high-resolution contact data and also to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. Our results mark a step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.

q-bio.PE↗