arXiv subjects

Clara Eminente

Publications and source records attributed to Clara Eminente.

3 recordsLinked to original sources

arXiv2026

Interplay between social contact and media exposure in the overestimation of racial diversity in the U.S

The general population systematically overestimates the size of minority groups, yet how these misperceptions vary across racial groups and geographical scales remains poorly understood. Using a purpose-built survey of the U.S. population, we examine overestimation of people of color (PoC) communities across four nested geographical scales: neighborhood, city, state, and nation. Our results demonstrate that overestimation is both scale- and group-dependent: the probability of overestimation increases progressively from local to national levels, and people of color overestimate their own group size more frequently than white people do at both the neighborhood and national levels. Among white respondents, we identify a scale-dependent divide in exposure mechanisms: direct interethnic social contact is the primary correlate of overestimation at local levels, whereas perceived frequency of coverage of people of color in news dominates at the national level. Furthermore, across both groups, frequent news consumption is associated with reduced rates of overestimation, while frequent social media use is associated with higher rates. These findings suggest that overestimation is real and present across scales and groups. This in turn can foster an `illusion of diversity', potentially undermining support for equity-promoting policies by creating the erroneous belief that representation goals have already been achieved.

physics.soc-ph↗

arXiv2024

Exploring the interplay of individual traits and interaction dynamics in preschool social networks

Several studies have investigated human interaction using modern tracking techniques for face-to-face encounters across various settings and age groups. However, little attention has been given to understanding how individual characteristics relate to social behavior. This is particularly important in younger age groups due to its potential effects on early childhood development. In this study, conducted during the Complexity 72h Workshop, we analyze human social interactions in a French preschool, where children's face-to-face interactions were monitored using proximity sensors over an academic year. We use metadata from parent surveys and preschool linguistic tests, covering demographic information and home habits, to examine the interplay between individual characteristics and contact patterns. Using a mixture of approaches, from random forest classifiers to network-based metrics at both dyadic and higher-order (group) levels, we identify sex, age, language scores, and number of siblings as the variables displaying the most significant associations with interaction patterns. We explore these variables' relationships to interactions within and outside classrooms and across mixed and single-grade classes. At the group level, we investigate how group affinity affects group persistence. We also find that higher-order network centrality (hypercoreness) is higher among children with siblings, indicating different group embedding despite similar total contact duration. This study aligns with existing literature on early social development and highlights the importance of integrating individual traits into the study of human interactions. Focusing on 2-5-year-olds offers insights into emerging social preferences during critical phases of cognitive development. Future research could use these findings to enhance mechanistic models of complex social systems by incorporating individual traits.

physics.soc-ph↗

arXiv2022

Interplay between exogenous triggers and endogenous behavioral changes in contagion processes on social networks

In recent years, statistical physics' methodologies have proven extremely successful in offering insights into the mechanisms that govern social interactions. However, the question of whether these models are able to capture trends observed in real-world datasets is hardly addressed in the current literature. With this work we aim at bridging the gap between theoretical modeling and validation with data. In particular, we propose a model for opinion dynamics on a social network in the presence of external triggers, framing the interpretation of the model in the context of misbehavior spreading. We divide our population in aware, unaware and zealot/educated agents. Individuals change their status according to two competing dynamics, referred to as behavioral dynamics and broadcasting. The former accounts for information spreading through contact among individuals whereas broadcasting plays the role of an external agent, modeling the effect of mainstream media outlets. Through both simulations and analytical computations we find that the stationary distribution of the fraction of unaware agents in the system undergoes a phase transition when an all-to-all approximation is considered. Surprisingly, such a phase transition disappears in the presence of a minimum fraction of educated agents. Finally, we validate our model using data collected from the public discussion on Twitter, including millions of posts, about the potential adverse effects of the AstraZeneca vaccine against COVID-19. We show that the intervention of external agents, as accounted for in our model, is able to reproduce some key features that are found in this real-world dataset.

physics.soc-ph↗