arXiv subjects

Giulio Rossetti

Publications and source records attributed to Giulio Rossetti.

At least 19 recordsLinked to original sources

arXiv2026

Structure, Topics, and Diffusion Effects of Bluesky Starter Packs

User discovery is a central challenge in online social platforms, particularly during onboarding. Bluesky, a decentralized microblogging platform built on the AT Protocol, introduced starter packs: curated collections of accounts that users can follow in a single action to bootstrap their social network. In this paper, we present a large-scale empirical analysis of more than 50,000 English-language starter packs and over 600,000 associated users. We characterize their structural organization, topical composition, and impact on content diffusion. Our results show that starter packs form a highly interconnected ecosystem with substantial overlap across packs that largely reflects pre-existing communities. Topic modeling reveals a skewed landscape dominated by automatically generated personal packs alongside several thematic communities, which exhibit similar structural properties but markedly different adoption patterns. Finally, a matched event-study analysis shows that inclusion in a starter pack is strongly associated with a substantial increase in short-term repost activity.

cs.SI↗

arXiv2026

Social Simulations: from Agent-Based Modeling to Digital Twins

This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly defined behavioral rules, to AI-enhanced simulations based on Large Language Models and, ultimately, Social Digital Twins: high-fidelity, data-driven representations of real-world socio-technical systems. Along this trajectory, we discuss the main methodological foundations, applications, advantages, and limitations of each paradigm, highlighting the progressive shift from abstract models designed to investigate general social mechanisms toward increasingly realistic computational representations of specific social systems.

cs.MA↗

arXiv2026

Joint Effects of Recommender Systems and Network Structure on the Visibility of Content and Creators

Social media algorithms allocate users' visibility by ranking content within their social networks. Yet, how recommendation logic and network structure jointly shape visibility across content and creators remains largely understudied. In this work, we tackle this question through agent-based simulations using YSocial, a social media virtual twin, in which agents interact under 7 recommendation strategies and 2 network topologies. We find that recommender logic sets the visibility regime: popularity creates a reinforcement loop in which early reactions increase later exposure, concentrating visibility on a small subset of content and limiting creator visibility to those whose content enters this loop, while collaborative filtering distributes visibility broadly across the active catalogue and user base. When the follower graph shapes candidate selection, network structure changes the direction of inequality: under popularity ranking, creator-level concentration becomes comparable to global popularity, but visibility is systematically redirected toward creators who are already socially popular. Network topology modulates the magnitude of these effects without changing their qualitative ordering. These results show that visibility allocation should be evaluated across content, creators, network position, and temporal reinforcement, and that controlled simulations can help test how feed design distributes visibility before deployment.

cs.SI↗

arXiv2026

Low-Vocality Engagement Shapes Online Participation

Online participation is often measured through visible expression, especially posting, yet many consequential forms of engagement occur through less vocal actions such as liking and following. Here we study how users inhabit Bluesky by reconstructing participation profiles from more than three billion activity records produced by a near-complete sample accounting for more than 80\% of registered users. We aggregate behavior into monthly user-level observations and distinguish two dimensions that are often conflated in platform analytics: intensity, capturing how much users engage, and style, capturing how engagement is expressed across actions. We find that vocal production is highly concentrated, but low-posting behavior does not imply absence from platform participation. High-intensity engagement is most strongly associated with liking rather than posting, while posting-oriented participation is more common among low-intensity users, indicating that visibility and sustained engagement should not be conflated. Transition patterns suggest that high-intensity likers and posters could be described as attractors; network-building redirects users within the active space; whereas observed inactivity acts as a persistent boundary that selectively limits re-entry. Higher-order motifs further show that inactivity often interrupts rather than erases prior regimes, and that low-intensity liking can precede durable high-intensity engagement. These results show that online participation is structured by differentiated low-vocality practices, calling for a shift from post-centered measures of activity toward dynamic accounts of platform presence. We identify a broader challenge for computational social science: platform participation cannot be adequately understood through the behavior of vocal minorities alone.

cs.SI↗

arXiv2026

Quantifying opinion homophily in online social networks: A bounded confidence perspective

The concept of homophily is pervasive in online social media. While many empirical studies have relied on external sociodemographic traits to investigate it, significantly less is known about homophily at the cognitive level, that is, at the level of shared opinions or values. For such "value homophily", in this paper we study interval-based patterns of opinion homophily from a bounded confidence perspective. We consider three heterogeneous datasets from Reddit and Twitter covering polarizing issues, with user opinions quantified via sentiment analysis and fact-checking, and analyze the interaction networks formed by weaker (reply-based) and stronger (follow-based) social ties. Our findings show that users' interaction neighborhoods are significantly more concentrated in opinion space than expected by chance, with tie strength and issue polarization further amplifying this effect. Moreover, users often exhibit asymmetric tolerance ranges, with asymmetry typically directed toward locally mainstream positions rather than more radical or opposing ones. These findings support a bounded confidence interpretation of online value homophily.

cs.SI↗

arXiv2026

LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies

Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitudinal framework quantifying psycho-social, reasoning and affective dimensions of LLMs' persuasiveness about polarizing societal topics. In a four-way longitudinal setup, Talk2AI's 770 participants engaged in structured conversations with one of four leading LLMs on topics like climate change, social media misinformation, and math anxiety. This produced 3,080 conversations over 60,000 turns. After each wave, participants reported conviction in their initial topic stance, perceived opinion change, LLM's perceived humanness, a self-donation to the topic and a textual explanation. Feedback time series showed longitudinal inertia in convictions, indicating some human anchoring to initial opinions even after repeated exposure to AI-generated arguments. Interestingly, NLP analyses revealed that both humans and LLMs relied on fallacious reasoning in 1 conversational quip every 6, countering the ``LLMs as superior systems" stereotype behind LLMs' cognitive surrender. LLMs' perceived humanness was most learnable from sociodemographic, psychological and engagement features ($R^2=0.44$), followed by opinion change ($R^2=0.34$), conviction ($R^2=0.26$) and personal endowment ($R^2=0.24$). Crucially, explainable AI (XAI) indicated: (i) the presence of individuals more susceptible to LLM-based opinion changes; (ii) psychological susceptibility to LLM-convincing consisted of having more trust in LLMs, being more agreeable and extraverted and with a higher need for cognition. A multiverse approach with mixed-effects models confirmed XAI results, alongside strong individual differences. Talk2AI provides a grounded framework and evidence for detecting how GenAI can influence human opinions via multiple psycho-social pathways in AI-human digital platforms.

cs.AI↗

arXiv2026

The role of System 1 and System 2 semantic memory structure in human and LLM biases

Implicit biases in both humans and large language models (LLMs) pose significant societal risks. Dual process theories propose that biases arise primarily from associative System 1 thinking, while deliberative System 2 thinking mitigates bias, but the cognitive mechanisms that give rise to this phenomenon remain poorly understood. To better understand what underlies this duality in humans, and possibly in LLMs, we model System 1 and System 2 thinking as semantic memory networks with distinct structures, built from comparable datasets generated by both humans and LLMs. We then investigate how these distinct semantic memory structures relate to implicit gender bias using network-based evaluation metrics. We find that semantic memory structures are irreducible only in humans, suggesting that LLMs lack certain types of human-like conceptual knowledge. Moreover, semantic memory structure relates consistently to implicit bias only in humans, with lower levels of bias in System~2 structures. These findings suggest that certain types of conceptual knowledge contribute to bias regulation in humans, but not in LLMs, highlighting fundamental differences between human and machine cognition.

cs.CL↗

arXiv2026

Network Effects and Agreement Drift in LLM Debates

Large Language Models (LLMs) have demonstrated an unprecedented ability to simulate human-like social behaviors, making them useful tools for simulating complex social systems. However, it remains unclear to what extent these simulations can be trusted to accurately capture key social mechanisms, particularly in highly unbalanced contexts involving minority groups. This paper uses a network generation model with controlled homophily and class sizes to examine how LLM agents behave collectively in multi-round debates. Moreover, our findings highlight a particular directional susceptibility that we term \textit{agreement drift}, in which agents are more likely to shift toward specific positions on the opinion scale. Overall, our findings highlight the need to disentangle structural effects from model biases before treating LLM populations as behavioral proxies for human groups.

cs.SI↗

arXiv2026

The Corporate Bond Factor Replication Crisis

Corporate bond factor research faces a replication crisis. The crisis stems from two sources that inflate reported factor premia: transaction prices whose measurement error enters both sorting signals and return denominators, creating a correlated errors-in-variables bias, and asymmetric ex-post return filtering that embeds future information into factor construction. Applying our framework to a 'factor zoo' of 108 signals across nine thematic clusters, we show that the majority of previously documented factors do not produce statistically significant bond CAPM alphas after correction. We provide an open source framework via Open Bond Asset Pricing, including error-corrected TRACE data, bias corrected factors, and software for reproducible research.

q-fin.PR↗

arXiv2025

A word association network methodology for evaluating implicit biases in LLMs compared to humans

As Large language models (LLMs) become increasingly integrated into our lives, their inherent social biases remain a pressing concern. Detecting and evaluating these biases can be challenging because they are often implicit rather than explicit in nature, so developing evaluation methods that assess the implicit knowledge representations of LLMs is essential. We present a novel word association network methodology for evaluating implicit biases in LLMs based on simulating semantic priming within LLM-generated word association networks. Our prompt-based approach taps into the implicit relational structures encoded in LLMs, providing both quantitative and qualitative assessments of bias. Unlike most prompt-based evaluation methods, our method enables direct comparisons between various LLMs and humans, providing a valuable point of reference and offering new insights into the alignment of LLMs with human cognition. To demonstrate the utility of our methodology, we apply it to both humans and several widely used LLMs to investigate social biases related to gender, religion, ethnicity, sexual orientation, and political party. Our results reveal both convergences and divergences between LLM and human biases, providing new perspectives on the potential risks of using LLMs. Our methodology contributes to a systematic, scalable, and generalizable framework for evaluating and comparing biases across multiple LLMs and humans, advancing the goal of transparent and socially responsible language technologies.

cs.CL↗

arXiv2025

SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks

We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool is designed to perform numerical simulations testing structure-function relationships in cognitive processes. By comparing simulation results with grounded theories in knowledge modelling, SpreadPy enables systematic investigations of how activation dynamics reflect cognitive, psychological and clinical phenomena. We demonstrate the library's utility through three case studies: (1) Spreading activation on associative knowledge networks distinguishes students with high versus low math anxiety, revealing anxiety-related structural differences in conceptual organization; (2) Simulations of a creativity task show that activation trajectories vary with task difficulty, exposing how cognitive load modulates lexical access; (3) In individuals with aphasia, simulated activation patterns on lexical networks correlate with empirical error types (semantic vs. phonological) during picture-naming tasks, linking network structure to clinical impairments. SpreadPy's flexible framework allows researchers to model these processes using empirically derived or theoretical networks, providing mechanistic insights into individual differences and cognitive impairments. The library is openly available, supporting reproducible research in psychology, neuroscience, and education research.

cs.CL↗

arXiv2025

A High-Performance Evolutionary Multiobjective Community Detection Algorithm

Community detection in complex networks is fundamental across social, biological, and technological domains. While traditional single-objective methods like Louvain and Leiden are computationally efficient, they suffer from resolution bias and structural degeneracy. Multi-objective evolutionary algorithms (MOEAs) address these limitations by simultaneously optimizing conflicting structural criteria, however, their high computational costs have historically limited their application to small networks. We present HP-MOCD, a High-Performance Evolutionary Multiobjective Community Detection Algorithm built on Non-dominated Sorting Genetic Algorithm II (NSGA-II), which overcomes these barriers through topology-aware genetic operators, full parallelization, and bit-level optimizations, achieving theoretical O(GN_p|V|) complexity. We conduct experiments on both synthetic and real-world networks. Results demonstrate strong scalability, with HP-MOCD processing networks of over 1,000,000 nodes while maintaining high quality across varying noise levels. It outperforms other MOEAs by more than 531 times in runtime on synthetic datasets, achieving runtimes as low as 57 seconds for graphs with 40,000 nodes on moderately powered hardware. Across 14 real-world networks, HP-MOCD was the only MOEA capable of processing the six largest datasets within a reasonable time, with results competitive with single-objective approaches. Unlike single-solution methods, HP-MOCD produces a Pareto Front, enabling individual-specific trade-offs and providing decision-makers with a spectrum of high-quality community structures. It introduces the first open-source Python MOEA library compatible with networkx and igraph for large-scale community detection.

cs.SI↗

arXiv2025

Language-Driven Opinion Dynamics in Agent-Based Simulations with LLMs

Understanding how opinions evolve is crucial for addressing issues such as polarization, radicalization, and consensus in social systems. While much research has focused on identifying factors influencing opinion change, the role of language and argumentative fallacies remains underexplored. This paper aims to fill this gap by investigating how language - along with social dynamics - influences opinion evolution through LODAS, a Language-Driven Opinion Dynamics Model for Agent-Based Simulations. The model simulates debates around the "Ship of Theseus" paradox, in which agents with discrete opinions interact with each other and evolve their opinions by accepting, rejecting, or ignoring the arguments presented. We study three different scenarios: balanced, polarized, and unbalanced opinion distributions. Agreeableness and sycophancy emerge as two main characteristics of LLM agents, and consensus around the presented statement emerges almost in any setting. Moreover, such AI agents are often producers of fallacious arguments in the attempt of persuading their peers and - for their complacency - they are also highly influenced by arguments built on logical fallacies. These results highlight the potential of this framework not only for simulating social dynamics but also for exploring from another perspective biases and shortcomings of LLMs, which may impact their interactions with humans.

cs.SI↗

arXiv2024

The "LLM World of Words" English free association norms generated by large language models

Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the absence of large-scale LLM-generated free association norms that are comparable with human-generated norms is an obstacle to this new research direction. To address this limitation, we create a new dataset of LLM-generated free association norms modeled after the "Small World of Words" (SWOW) human-generated norms consisting of approximately 12,000 cue words. We prompt three LLMs, namely Mistral, Llama3, and Haiku, with the same cues as those in the SWOW norms to generate three novel comparable datasets, the "LLM World of Words" (LWOW). Using both SWOW and LWOW norms, we construct cognitive network models of semantic memory that represent the conceptual knowledge possessed by humans and LLMs. We demonstrate how these datasets can be used for investigating implicit biases in humans and LLMs, such as the harmful gender stereotypes that are prevalent both in society and LLM outputs.

cs.CL↗

arXiv2024

Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters

Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.

cs.AI↗

arXiv2024

Y Social: an LLM-powered Social Media Digital Twin

In this paper we introduce Y, a new-generation digital twin designed to replicate an online social media platform. Digital twins are virtual replicas of physical systems that allow for advanced analyses and experimentation. In the case of social media, a digital twin such as Y provides a powerful tool for researchers to simulate and understand complex online interactions. {\tt Y} leverages state-of-the-art Large Language Models (LLMs) to replicate sophisticated agent behaviors, enabling accurate simulations of user interactions, content dissemination, and network dynamics. By integrating these aspects, Y offers valuable insights into user engagement, information spread, and the impact of platform policies. Moreover, the integration of LLMs allows Y to generate nuanced textual content and predict user responses, facilitating the study of emergent phenomena in online environments. To better characterize the proposed digital twin, in this paper we describe the rationale behind its implementation, provide examples of the analyses that can be performed on the data it enables to be generated, and discuss its relevance for multidisciplinary research.

cs.AI↗

arXiv2024

Characterizing User Archetypes and Discussions on Scored.co

In recent years, the proliferation of social platforms has drastically transformed the way individuals interact, organize, and share information. In this scenario, we experience an unprecedented increase in the scale and complexity of interactions and, at the same time, little to no research about some fringe social platforms. In this paper, we present a multi-dimensional framework for characterizing nodes and hyperedges in social hypernetworks, with a focus on the understudied alt-right platform Scored.co. Our approach integrates the possibility of studying higher-order interactions, thanks to the hypernetwork representation, and various node features such as user activity, sentiment, and toxicity, with the aim to define distinct user archetypes and understand their roles within the network. Utilizing a comprehensive dataset from Scored.co, we analyze the dynamics of these archetypes over time and explore their interactions and influence within the community. The framework's versatility allows for detailed analysis of both individual user behaviors and broader social structures. Our findings highlight the importance of higher-order interactions in understanding social dynamics, offering new insights into the roles and behaviors that emerge in complex online environments.

cs.SI↗

arXiv2024

A survey on the impacts of recommender systems on users, items, and human-AI ecosystems

Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. This is a crucial contribution to the literature because terminologies vary substantially across disciplines and ecosystems, hindering comparison and accumulation of knowledge in the field. We follow the customary steps of qualitative systematic review, gathering 154 articles from different disciplines to develop a parsimonious taxonomy of methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, content degradation, discrimination, diversity, echo chamber, filter bubble, homogenisation, polarisation, radicalisation, volume), and their level of analysis (individual, item, and ecosystem). We systematically discuss substantive and methodological commonalities across ecosystems, and highlight potential avenues for future research. The survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders.

cs.IR↗