arXiv subjects

Riccardo Gallotti

Publications and source records attributed to Riccardo Gallotti.

At least 19 recordsLinked to original sources

arXiv2026

Ant swarm functional control via stigmergic Reinforcement Learning agents

In this work, we propose a novel framework for the functional controllability of the ant swarm model, a well-known and relevant model of collective behaviour. Our approach introduces a population of controlling stigmergic agents, trained via Reinforcement Learning (RL), that act on the environment to influence the system dynamics and promote the emergence of ordered behaviour. Stigmergic agents are optimized in a centralized-training decentralized-execution setting, interacting with ants only through the shared pheromone field. The reward design promotes trail pheromone structures and alignment of ant positions with high-pheromone paths, without requiring control of specific microscopic configurations. Our results demonstrate that the learned policies effectively shift the phase transition line that characterizes the global behaviour of the system, enabling the emergence of trails scenarios in regimes that are typically dominated by randomness. This study provides insights into the potential of RL based control strategies for complex systems, contributing to the general understanding of functional controllability in this field.

physics.soc-ph↗

arXiv2026

On the Effects of Decentralized Moderation on Network Robustness and Information Diffusion in Mastodon

Decentralized online social networks such as Mastodon distribute moderation power across thousands of independently governed servers, raising fundamental questions about how local block decisions shape global structure and information flow. In this paper, we analyze Mastodon at the instance level by constructing a signed, directed, temporal network in which positive edges aggregate inter-instance follow relationships and negative edges encode daily block actions. Using one year of data, we show that despite continuous moderation activity and changing roles among instances, the network exhibits strong structural stability: signed dyadic motifs and degree distributions display highly persistent dynamics, and aggregated transition matrices satisfy Markovian equilibrium conditions over intermediate time scales. Building on the marked asymmetry between instances that predominantly issue bans and those that are mostly banned, we then study information diffusion on the positive network via a hybrid contagion model that combines simple contagion within groups and complex contagion across groups. We find that information originating in the minority of moderating instances spreads more efficiently, both internally and toward the majority, while the opposite direction is fragile and sensitive to contagion parameters. Echo-chamber effects emerge even in a globally balanced signed network and become stronger under stricter contagion conditions. Together, these results show that decentralized moderation in Mastodon generates a stable macroscopic configuration that both structures and constrains information exchange, effectively isolating norm-violating domains without centralized control.

physics.soc-ph↗

arXiv2026

Collaboration, Integration, and Thematic Exploration in European Framework Programmes: A Longitudinal Network Analysis

Since their inception in 1984, the European Framework Programmes (FPs) have funded collaborative R&D to promote excellence, cohesion, and competitiveness in a growing European Union. However, their integrative impact and the evolution of the research landscape alongside its collaborative structures remain insufficiently understood. In this longitudinal study, we leverage CORDIS data from all nine FPs to reconstruct the evolution of country-level collaboration networks over time. We observe an increasing equity in project participation between FP1 and FP6, although newly included countries systematically tend to be marginal when first joining the programmes. However, we find that the collaborative nature of EU projects progressively integrates marginal countries in the network, even if this integration is still in progress. We also trace the evolution in time of research topics using semantic embeddings of project descriptions, identifying 117 topics grouped into 16 macro-topics. By computing the minimum spanning tree length of project embeddings within yearly time windows, we quantify how European research progressively explores a wider knowledge space. A comparison with a null model with points randomly distributed in the semantic space indicates that this exploration is more focused than a uniform coverage. Moreover, it appears uneven, with few topics mostly attracting industry and others academia. Our findings suggest that, while European funding promotes international cooperation, it has not yet fully resolved core-periphery asymmetries, and European research remains concentrated along established trajectories rather than broadly exploratory, with implications for future programme design and the excellence-cohesion debate.

physics.soc-ph↗

arXiv2026

Beyond dynamic scaling: rare events break universality

Surface growth driven by non-monomeric deposition has remained largely unexplored. We investigate a model based on the deposition of blobs with a power-law size distribution $P(s)\sim s^{-\tau}$. We find that the critical exponents vary continuously with $\tau$, recovering Kardar--Parisi--Zhang behavior only for $\tau \ge 3$. For $\tau<3$, roughness scaling exhibits strong corrections and scale invariance breaks down. We show that this behavior originates from the emergence of a second dynamical length scale $\zeta$, corresponding to the linear size of the largest cluster, in addition to the usual correlation length $\xi$. The coexistence of these two relevant scales signals the breakdown of the usual Family--Vicsek scaling. These results point to a new phenomenology of surface growth beyond the standard scale-invariant paradigm.

cond-mat.stat-mech↗

arXiv2026

Socioeconomic Determinants of the COVID-19 Infodemic

The COVID-19 pandemic has been accompanied by an infodemic of misinformation that impedes effective public health responses. This study examines relationships between socioeconomic factors and infodemic risk patterns across 37 OECD countries using Twitter data from 2020-2022. Employing dimensionality reduction techniques on 20 socioeconomic indicators, we identify complex correlations with infodemic measures that evolve throughout the pandemic. Countries exhibit distinct clustering in their infodemic profiles that transcend conventional socioeconomic categorizations. We find that dynamic information behaviors dominate initial crisis responses, while stable socioeconomic conditions become more influential as the pandemic progresses. News media diet diversity emerges as a significant protective factor, with pluralistic information ecosystems demonstrating greater resilience against misinformation. Additionally, institutional stability correlates strongly with reduced infodemic volatility over time. These findings highlight how infodemics are embedded within broader socioeconomic contexts, providing foundations for targeted interventions to build societal resilience against misinformation during future health emergencies.

physics.soc-ph↗

arXiv2025

Unifying points of interest taxonomies: mapping OpenStreetMap tags to the Foursquare category system

The heterogeneity of Point of Interest (POI) taxonomies is a persistent challenge for the integration of urban datasets and the development of location-based services. OpenStreetMap (OSM) adopts a flexible, community-driven tagging system, while Foursquare (FS) relies on a curated hierarchical structure. Here we present an openly available benchmark and mapping framework that aligns OSM tags with the FS taxonomy. This resource integrates the richness of community-driven OSM data with the hierarchical structure of FS, enabling reproducible and interoperable urban analytics. The dataset is complemented by an evaluation of embedding and LLM-based alignment strategies and a pipeline that supports scalable updates as OSM evolves. Together, these elements provide both a robust reference resource and a practical tool for the community. Our approach is structured around three components: the construction of a manually curated benchmark as a gold standard, the evaluation of pretrained text embedding models for semantic alignment between OSM tags and FS categories, and an LLM-based refinement stage that enhances robustness and adaptability. The proposed methodology provides a scalable and reproducible solution for taxonomy unification, with direct applications to urban analytics, mobility studies, and smart city services.

cs.SI↗

arXiv2025

Universal roughness and the dynamics of urban expansion

Urban sprawl reshapes cities, yet its quantitative laws remain elusive. Analyzing built-up expansion in 19 cities (1985-2015) with tools from surface growth physics in radial geometry, we reveal anisotropic, branch-like growth and a piecewise linear scaling between area and population. We uncover a robust local roughness exponent $α_{\text{loc}}\approx 0.54$, coexisting with variable $β$ and $z$. This unusual coexistence of universal and variable exponents offers a rare empirical testbed for nonequilibrium growth and an empirical basis for modeling urban sprawl.

physics.soc-ph↗

arXiv2025

Mapping the interaction between science and misinformation in COVID-19 tweets

During the COVID-19 pandemic, scientific knowledge evolved rapidly, accompanied by a surge of misinformation, labelled an infodemic by the WHO. In this context, we study the interaction between science and misinformation on Twitter (now X) using a database of ~407M COVID-19-related tweets. We classify URL reliability with Media Bias/Fact Check and used Altmetric data to identify scientific publications. We find that among ~1.2M users who shared science, 45% also shared unreliable content. Scientific papers circulated by these users were more often preprints, slightly more likely to be retracted, less cited, and published in lower-impact journals. Our findings indicate misinformation is not driven by a lack of exposure to science but instead raise critical questions about open science practices, particularly the role of preprints in amplifying misleading narratives. Our results underscore the importance of proactive scientific engagement on social media in countering misinformation and reinforcing trust in science during global crises.

physics.soc-ph↗

arXiv2025

Scale-free Points-of-Interest Distribution in a City Emerging from Homogeneous Poissonian-point Processes

Urban systems often exhibit scale-invariant properties, with power-law distributions observed in various spatial and temporal patterns of human behavior. A prominent example is the distribution of commercial activities and other Points of Interest (POIs) across cities. However, the mechanisms by which such heavy-tailed behaviors emerge from local urban dynamics remain poorly understood. In this work, we demonstrate that global inhomogeneity in the spatial distribution of POIs can arise from the aggregation of locally homogeneous processes. Using Foursquare data from the city of Bologna, we show that POI distributions exhibit clear power-law scaling when analyzed at city scale. We develop a theoretical framework in which this behavior naturally emerges from spatial clusters defined by shared intensity levels across disjoint areas, rather than spatial contiguity. By analytically and empirically linking these local processes to the observed global distribution, we provide a generative explanation for the emergence of scale-free patterns in urban commercial structure. To further relax the assumptions underlying the purely spatial model, and to account for the empirical observation that areas with similar activity intensity can be spatially disjoint, we introduce a hybrid hierarchical approach that combines spatial clustering with statistical heterogeneity across regions of comparable density, modeled via Poisson mixtures. This enables us to capture real-world deviations from local regularity while preserving interpretability. Our findings highlight a key insight: complex global phenomena in cities can arise from the spatial superposition of simple, locally uniform dynamics. This connection between micro-level homogeneity and macro-scale complexity offers new tools for interpreting, modeling, and classifying urban space.

physics.soc-ph↗

arXiv2024

Quantifying the impact of persuasiveness, cautiousness and prior beliefs in (mis)information sharing on online social networks using Drift Diffusion Models

Misleading newsletters can shape individuals' perceptions, and pose a threat to societies; as we witnessed by lowering the severity of follow-up stay-at-home orders and burdening a significant challenge to the fight against COVID-19. In this research, we study (mis)information spreading, reanalyzing behavioral data on online sharing, and analyzing decision-making mechanisms using the Drift Diffusion Model (DDM). We find that subjects display an increased instinctive inclination towards sharing misleading news, but rational thinking significantly curbs this reaction, especially for more cautious and older individuals. On top of network structures with similar characteristics as X, Mastodon, and Facebook, we use an agent-based model to expand this individual knowledge to a large scale where individuals are exposed to (mis)information through friends and share (or not) content with probabilities driven by DDM. We found that the natural shape of these social online networks provides a fertile ground for any news to rapidly become viral. Yet we have found that, for the case of X, limiting the number of followers of the most connected users proves to be an appropriate and feasible containment strategy.

physics.soc-ph↗

arXiv2024

Predicting human cooperation: sensitizing drift-diffusion model to interaction and external stimuli

As humans perceive and actively engage with the world, we adjust our decisions in response to shifting group dynamics and are influenced by social interactions. This study aims to identify which aspects of interaction affect cooperation-defection choices. Specifically, we investigate human cooperation within the Prisoner's Dilemma game, using the Drift-Diffusion Model to describe the decision-making process. We introduce a novel Bayesian model for the evolution of the model's parameters based on the nature of interactions experienced with other players. This approach enables us to predict the evolution of the population's expected cooperation rate. We successfully validate our model using an unseen test dataset and apply it to explore three strategic scenarios: co-player manipulation, use of rewards and punishments, and time pressure. These results support the potential of our model as a foundational tool for developing and testing strategies aimed at enhancing cooperation, ultimately contributing to societal welfare.

physics.soc-ph↗

arXiv2024

Towards Civic Digital Twins: Co-Design the Citizen-Centric Future of Bologna

We introduce Civic Digital Twin (CDT), an evolution of Urban Digital Twins designed to support a citizen-centric transformative approach to urban planning and governance. CDT is being developed in the scope of the Bologna Digital Twin initiative, launched one year ago by the city of Bologna, to fulfill the city's political and strategic goal of adopting innovative digital tools to support decision-making and civic engagement. The CDT, in addition to its capability of sensing the city through spatial, temporal, and social data, must be able to model and simulate social dynamics in a city: the behavior, attitude, and preference of citizens and collectives and how they impact city life and transform transformation processes. Another distinctive feature of CDT is that it must be able to engage citizens (individuals, collectives, and organized civil society) and other civic stakeholders (utilities, economic actors, third sector) interested in co-designing the future of the city. In this paper, we discuss the motivations that led to the definition of the CDT, define its modeling aspects and key research challenges, and illustrate its intended use with two use cases in urban mobility and urban development.

cs.CY↗

arXiv2024

Mixing Individual and Collective Behaviours to Predict Out-of-Routine Mobility

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviours. Our study introduces an approach that dynamically integrates individual and collective mobility behaviours, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across three US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. Spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviours strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviours, our approach offers transparent and accurate predictions, crucial for addressing contemporary mobility challenges.

cs.CY↗

arXiv2024

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

The development and popularization of large language models (LLMs) have raised concerns that they will be used to create tailor-made, convincing arguments to push false or misleading narratives online. Early work has found that language models can generate content perceived as at least on par and often more persuasive than human-written messages. However, there is still limited knowledge about LLMs' persuasive capabilities in direct conversations with human counterparts and how personalization can improve their performance. In this pre-registered study, we analyze the effect of AI-driven persuasion in a controlled, harmless setting. We create a web-based platform where participants engage in short, multiple-round debates with a live opponent. Each participant is randomly assigned to one of four treatment conditions, corresponding to a two-by-two factorial design: (1) Games are either played between two humans or between a human and an LLM; (2) Personalization might or might not be enabled, granting one of the two players access to basic sociodemographic information about their opponent. We found that participants who debated GPT-4 with access to their personal information had 81.7% (p < 0.01; N=820 unique participants) higher odds of increased agreement with their opponents compared to participants who debated humans. Without personalization, GPT-4 still outperforms humans, but the effect is lower and statistically non-significant (p=0.31). Overall, our results suggest that concerns around personalization are meaningful and have important implications for the governance of social media and the design of new online environments.

cs.CY↗

arXiv2024

Decoding the News Media Diet of Disinformation Spreaders

In the digital era, information consumption is predominantly channeled through online news media disseminated on social media platforms. Understanding the complex dynamics of the news media environment and users habits within the digital ecosystem is a challenging task that requires at the same time large bases of data and accurate methodological approaches. This study contributes to this expanding research landscape by employing network science methodologies and entropic measures to analyze the behavioural patterns of social media users sharing news pieces and dig into the diverse news consumption habits within different online social media user groups. Our analyses reveal that users are more inclined to share news classified as fake when they have previously posted conspiracy or junk science content, and vice versa, creating a series of misinformation hot streaks. To better understand these dynamics, we used three different measures of entropy to gain insights into the news media habits of each user, finding that the patterns of news consumption significantly differ among users when focusing on disinformation spreaders, as opposed to accounts sharing reliable or low-risk content. Thanks to these entropic measures, we quantify the variety and the regularity of the news media diet, finding that those disseminating unreliable content exhibit a more varied and at the same time a more regular choice of web domains. This quantitative insight into the nuances of news consumption behaviours exhibited by disinformation spreaders holds the potential to significantly inform the strategic formulation of more robust and adaptive social media moderation policies.

physics.soc-ph↗

arXiv2024

Multilayer Network Science: from Cells to Societies

Networks are convenient mathematical models to represent the structure of complex systems, from cells to societies. In the past decade, multilayer network science -- the branch of the field dealing with units interacting in multiple distinct ways, simultaneously -- was demonstrated to be an effective modeling and analytical framework for a wide spectrum of empirical systems, from biopolymer networks (such as interactome and metabolomes) to neuronal networks (such as connectomes), from social networks to urban and transportation networks. In this Element, a decade after the publication of one of the most seminal papers on this topic, we review the most salient features of multilayer network science, covering both theoretical aspects and direct applications to real-world coupled/interdependent systems, from the point of view of multilayer structure, dynamics, and function. We discuss potential frontiers for this topic and the corresponding challenges in the field for the future.

physics.soc-ph↗

arXiv2023

The connection between the spread of misinformation, time of day, and individual user activity patterns

Social media manipulation poses a significant threat to cognitive autonomy and unbiased opinion formation. Prior literature explored the relationship between online activity and emotional state, cognitive resources, sunlight and weather. However, a limited understanding exists regarding the role of time of day in content spread and the impact of user activity patterns on susceptibility to mis- and disinformation. This work uncovers a strong correlation between user activity patterns and the tendency to spread manipulated content. Through quantitative analysis of Twitter data, we examine how user activity throughout the day aligns with chronotypical archetypes. Evening types exhibit a significantly higher inclination towards spreading potentially manipulated content, which is generally more likely between 2:30 AM and 4:15 AM. This knowledge can become crucial for developing targeted interventions and strategies that mitigate misinformation spread by addressing vulnerable periods and user groups more susceptible to manipulation.

cs.SI↗

arXiv2023

Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables

COVID-19 had a strong and disruptive impact on our society, and yet further analyses on most relevant factors explaining the spread of the pandemic are needed. Interdisciplinary studies linking epidemiological, mobility, environmental, and socio-demographic data analysis can help understanding how historical conditions, concurrent social policies and environmental factors impacted on the evolution of the pandemic crisis. This work deals with a regression analysis linking COVID-19 mortality to socio-demographic, mobility, and environmental data in the US during the first half of 2020, i.e., during the COVID-19 pandemic first wave. This study can provide very useful insights about risk factors enhancing mortality rates before non-pharmaceutical interventions or vaccination campaigns took place. Our cross-sectional ecological regression analysis demonstrates that, when considering the entire US area, the socio-demographic variables globally play the most important role with respect to environmental and mobility variables in describing COVID-19 mortality. Compared to the complete generalized linear model considering all socio-demographic, mobility, and environmental data, the regression based only on socio-demographic data provides a better approximation and proves to be a better explanatory model when compared to the mobility-based and environmental-based models. However, when looking at single entries within each of the three groups, we see that the mobility data can become relevant descriptive predictors at local scale, as in New Jersey where the time spent at work is one of the most relevant explanatory variables, while environmental data play contradictory roles.

physics.soc-ph↗