Background: YouTube is the primary global video platform, hosting both authoritative health information and vaccine-skeptic viewpoints. However, engagement dynamics remain poorly understood. Objective: The aim of this study was to investigate the temporal and textual dynamics of engagement of the YouTube viewership with vaccination content, and specifically content that is in favor of or against vaccination. We contextualized these dynamics in the authority signals of the posting channel and the moderation actions taken by the platform. Methods: We conducted a 6-month daily longitudinal analysis of 7213 vaccine-related YouTube videos (November 2024 to May 2025) mentioning vaccination. We used zero-shot large language model classification with manual verification to classify the video stance toward vaccination, and the stance of their comments toward the video. The engagement and disagreement dynamics were modeled using Bayesian regression. Results: Our findings show engagement asymmetry between content supporting and questioning vaccination. Vaccine-hesitant videos in our sample receive substantially higher raw engagement and moderate normalized engagement rates (a 2.5-2.6x difference versus strongly pro-vaccine videos). Vaccine-hesitant videos reach 90% of cumulative views faster (18 vs 32 days; 44% faster), while models adjusting for total engagement volume indicate that approximately 20% of this advantage reflects genuine temporal compression independent of engagement volume. Comment analysis indicated that the vaccine-hesitant videos foster echo chambers, while the pro-vaccine content attracts battlegrounds. Pro-vaccine content tends to originate from organizations, particularly news and health institutions, while vaccine-hesitant discourse is more likely to come from individual creators, even those self-identifying as medical doctors. Moderation, on the rare occasion when it occurs (about 2% of the videos were taken down), comes after engagement saturation, limiting its effectiveness. Conclusions: Our analysis suggests that vaccine-hesitant content can dominate YouTube's engagement ecosystem through rapid early-stage amplification, which has direct implications for public health intervention timing and platform governance policy.

Gauging the extent of public acceptability of reforms is an important concern for policymakers. Timely insights into public perceptions can illuminate how reforms are received and how attitudes evolve over time. In this study, we build on the OECD's Public Acceptability Tool, a framework encompassing four key dimensions of reform acceptability—Economic, Fairness, Behavioural, and Process—to evaluate the public acceptability of policy reforms. We take the 2023 French pension reform as a relevant case study, using online media articles and parliamentary speeches as indicators of discourse surrounding the reform. Using word embeddings, we classify these texts according to the four dimensions and apply matrix factorisation topic algorithms to uncover the latent themes within each. Our analysis shows that the Process dimension dominated media coverage during the discussion and legislative phases of the reform, consistent with previous literature on pension reforms. In contrast, no particular dimension was predominant in parliamentary speeches, suggesting a mismatch between policy and public debates. Finally, we identify the main topics driving public discussion within each dimension, highlighting notable differences between media narratives and parliamentary discourse that offer further insight into the dynamics of public acceptability.

Socioeconomic inequalities significantly influence infectious disease outcomes, as seen with COVID-19, but the pathways through which socioeconomic conditions affect transmission dynamics remain unclear. To address this, we conducted a survey representative of the Italian population, stratified by age, gender, geographical area, city size, employment status, and education level. The survey’s final aim was to estimate differences in contact and protective behaviors across various population strata, both of which are crucial for understanding transmission dynamics. Our initial insights based on the survey indicate that years after the pandemic began, the perceived impact of COVID-19 on professional, economic, social, and psychological dimensions vary across socioeconomic strata, extending beyond the epidemiological outcomes. This reinforces the need for approaches that systematically consider socioeconomic determinants. In this context, using generalized linear models, we identified associations between socioeconomic factors and vaccination status for both COVID-19 and influenza, as well as the influence of socioeconomic conditions on mask-wearing and social distancing. Importantly, we also observed differences in contact behaviors based on employment status while education level did not show a significant association. These findings highlight the complex interplay of socioeconomic and demographic factors in shaping protective behavior and contact patterns. Understanding these dynamics can contribute to the improvement of epidemic models and better guide public health efforts for at-risk groups.

Most countries around the world enforced non-pharmaceutical interventions against COVID-19. Italy was one of the first countries to be affected by the pandemic, imposing a hard lockdown, in the first epidemic wave. During the second wave, the country implemented progressively restrictive tiers at the regional level according to weekly epidemiological risk assessments. This paper quantifies the impact of these restrictions on contacts and on the reproduction number.

Food security is recognized as an inherent human right, enshrined within the principles of the Agenda 2030. The Global Report of Food Crises 2022 points out 193 million people facing severe food insecurity across 53 countries, posing challenges to decision-makers and institutions. Among the many causes of food crises, violent conflict, economic shocks, and environmental pressures are the most influential. In this work, we focus primarily on the conflict-related domain. Finding a stable relationship between conflict and food insecurity is complex for several reasons: first, the relationship is mutually reinforcing; second, the full impact of conflict on food insecurity may take time to have an effect; and third, conflict itself is a multidimensional phenomenon and can include multiple types of violent events. This research set out to comparatively assess the impact of different types of violence on self-reported food insecurity in three prominent food crisis contexts: Burkina Faso, Syria, and Yemen. A measure of food-related classifying events was developed using a rules-based approach. The analysis showed that this approach can effectively code and classify food-related conflict in diverse contexts. By refining the search string, it becomes possible to capture food-related conflict in various food systems. Our findings point out that the new-build measure of food-related conflict is more strongly correlated to subsequent self-reported insufficient food consumption than other forms of violence. The results demonstrate that this relationship is robust across a range of data collection windows and across discrete time periods of analysis. In summary, the research suggests that focusing on the use of food and food systems as tactics in conflict can be highly valuable for understanding and addressing food insecurity.

Italy was the first European country to be hit by COVID-19 in the early 2020, since then losing over 100,000 people to the disease. By the end of the vaccination campaign of 2021, 81% of the public received at least one dose. These dramatic developments were accompanied by a rigorous discussion around vaccination, both about its urgency and its possible negative effects. Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is thus imperative to understand the nature of this discourse, with a specific focus on the vaccination hesitant individuals, whose healthcare decisions may affect their communities and the country at large. In this study we ask, how has the Italian discussion around vaccination changed during the COVID-19 pandemic, and have the unprecedented events of 2020-2021 been able to break the echo chamber around this topic? We use a Twitter dataset spanning September 2019 - November 2021 to examine the state of polarization around vaccination. We propose a hierarchical clustering approach to find the largest communities in the endorsement networks of different time periods, and manually illustrate that it produces communities of users sharing a stance. Examining the structure of these networks, as well as textual content of their interactions, we find the stark division between supporters and hesitant individuals to continue throughout the vaccination campaign. However, we find an increasing commonality in the topical focus of the vaccine supporters and vaccine hesitant, pointing to a possible common set of facts the two sides may agree on. Still, we discover a series of concerns voiced by the hesitant community, ranging from unfounded conspiracies (microchips in vaccines) to public health policy discussion (vaccine passport limitations). We recommend an ongoing surveillance of this debate, especially to uncover concerns around vaccination before the public health decisions and official messaging are made public.

Hypergraphs naturally represent higher-order interactions, which persistently appear in social interactions, neural networks, and other natural systems. Although their importance is well recognized, a theoretical framework to describe general dynamical processes on hypergraphs is not available yet. In this paper, we derive expressions for the stability of dynamical systems defined on an arbitrary hypergraph. The framework allows us to reveal that, near the fixed point, the relevant structure is a weighted graph-projection of the hypergraph and that it is possible to identify the role of each structural order for a given process. We analytically solve two dynamics of general interest, namely, social contagion and diffusion processes, and show that the stability conditions can be decoupled in structural and dynamical components. Our results show that in social contagion process, only pairwise interactions play a role in the stability of the absorbing state, while for the diffusion dynamics, the order of the interactions plays a differential role. Our work provides a general framework for further exploration of dynamical processes on hypergraphs.

SARS-CoV-2 has clearly shown that efficient management of infectious diseases requires a top-down approach which must be complemented with a bottom-up response to be effective. Here we investigate a novel approach to surveillance for transboundary animal diseases using African Swine (ASF) fever as a model. We collected data both at a population level and at the local level on information-seeking behavior respectively through digital data and targeted questionnaire-based surveys to relevant stakeholders such as pig farmers and veterinary authorities. Our study shows how information-seeking behavior and resulting public attention during an epidemic, can be identified through novel data streams from digital platforms such as Wikipedia. Leveraging attention in a critical moment can be key to providing the correct information at the right moment, especially to an interested cohort of people. We also bring evidence on how field surveys aimed at local workers and veterinary authorities remain a crucial tool to assess more in-depth preparedness and awareness among front-line actors. We conclude that these two tools should be used in combination to maximize the outcome of surveillance and prevention activities for selected transboundary animal diseases such as ASF.

BACKGROUND: The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. OBJECTIVE: The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. METHODS: We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19-related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. RESULTS: Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. CONCLUSIONS: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change.

Time-varying network topologies can deeply influence dynamical processes mediated by them. Memory effects in the pattern of interactions among individuals are also known to affect how diffusive and spreading phenomena take place. In this paper we analyze the combined effect of these two ingredients on epidemic dynamics on networks. We study the susceptible-infected-susceptible (SIS) and the susceptible-infected-recovered (SIR) models on the recently introduced activity-driven networks with memory. By means of an activity-based mean-field approach, we derive, in the long-time limit, analytical predictions for the epidemic threshold as a function of the parameters describing the distribution of activities and the strength of the memory effects. Our results show that memory reduces the threshold, which is the same for SIS and SIR dynamics, therefore favoring epidemic spreading. The theoretical approach perfectly agrees with numerical simulations in the long-time asymptotic regime. Strong aging effects are present in the preasymptotic regime and the epidemic threshold is deeply affected by the starting time of the epidemics. We discuss in detail the origin of the model-dependent preasymptotic corrections, whose understanding could potentially allow for epidemic control on correlated temporal networks.