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2026 Journal article Open Access

Navigating the lobbying landscape: insights from opinion dynamics models
Giachini Daniele, Ciambezi Leonardo, Rosso Verdiana Del, Fornari Fabrizio, Pansanella Valentina, Popoyan Lilit, Sîrbu Alina
While lobbying has been demonstrated to have an important effect on public opinion and policy making, existing models of opinion formation do not specifically include its effect. In this work, we introduce a new model of lobbying-driven opinion influence within opinion dynamics, where lobbyists can implement complex strategies and are characterized by a finite budget. Individuals update their opinions through a learning process resembling Bayes-rule updating but using signals generated by the other agents (a form of social learning), modulated by under-reaction and confirmation bias. We study the model theoretically and numerically, demonstrating rich dynamics both with and without lobbyists. In the presence of lobbying, we observe two regimes: one in which lobbyists can have full influence on the agent network, and another where the peer-effect generates polarization. When lobbyists are symmetric, the lobbyist-influence regime is characterized by prolonged opinion oscillations. If lobbyists temporally differentiate their strategies, frontloading is advantageous in the peer-effect regime, whereas backloading is advantageous in the lobbyist-influence regime. These rich dynamics pave the way for studying real lobbying strategies to validate the model in practice.Source: IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
DOI: 10.1109/tcss.2026.3683772
DOI: 10.48550/arxiv.2507.13767
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2026 Journal article Open Access

Quantifying opinion homophily in online social networks from a bounded confidence perspective
Luan Yangyang, Ancona Camilla, Bernardo Carmela, Pansanella Valentina, Lo Iudice Francesco, Rossetti Giulio, Vasca Francesco, Wu Xiaoqun, Altafini Claudio
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”, 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 value homophily and provide a basis for future studies of exposure and polarization.Source: COMMUNICATIONS PHYSICS, vol. 9 (issue 1)
DOI: 10.1038/s42005-026-02760-y
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See at: Communications Physics Open Access | CNR IRIS | www.nature.com | CNR IRIS Restricted

2025 Conference article Restricted

Structure-attribute similarity interplay in diffusion dynamics on social networks
Citraro S., Pansanella V., Rossetti G.
Social interactions are shaped by homophily, the tendency for individuals to connect with others who share similar attributes. Exploring this phenomenon is crucial for understanding a wide spectrum of social behaviors, including the spread of misinformation and the dynamics of societal debates. In this study, we leverage a graph transformation strategy—which analyzes the interplay between individuals’ personal preferences and their structural connections—to investigate mechanisms of opinion/information diffusion. Among these latter ones, we focus on the Deffuant-Weisbuch model to simulate opinion dynamics and the Independent Cascade model to simulate information spread. Our findings on real-world social networks suggest that emphasizing attribute similarities enhances graph cohesion, whereas forcing structural similarities leads to fragmentation. Moreover, we observe a trend towards consensus opinion formation when enhancing attribute similarities, and faster as well as complete coverage of information spread in the same setup. These results motivate the importance of considering both individual attributes and network structure in studying social dynamics.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 425-439. ita, 2024
DOI: 10.1007/978-3-031-78980-9_27
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See at: doi.org Restricted | CNR IRIS | CNR IRIS | link.springer.com

2025 Other Open Access

ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.

See at: CNR IRIS Open Access | www.isti.cnr.it | CNR IRIS Restricted

2025 Journal article Open Access

Unveiling the drivers of active participation in social media discourse
Baqir A., Chen Y., Diaz-Diaz F., Kiyak S., Louf T., Morini V., Pansanella V., Torricelli M., Galeazzi A.
The emergence of new public forums in the form of online social media has introduced unprecedented challenges to public discourse, including polarization, misinformation, and the rise of echo chambers. Existing research has extensively examined these topics by focusing on the active actions performed by users, without accounting for the share of individuals who consume content without actively interacting with it. In contrast, this study incorporates passive consumption data to investigate the prevalence of active participation in online discourse. We introduce a metric to quantify the share of active engagement and analyze over 17 million pieces of content linked to a polarized Twitter debate to understand its relationship with several features of online environments, such as echo chambers, coordinated behavior, political bias, and source reliability. Our findings reveal a significant proportion of users who consume content without active interactions, underscoring the importance of considering also passive consumption proxies in the analysis of online debates. Furthermore, we found that increased active participation is primarily correlated with the presence of multimedia content and unreliable news sources, rather than with the ideological stance of the content producer, suggesting that active engagement is independent of echo chambers. Our work highlights the significance of passive consumption proxies for quantifying active engagement, which influences platform feed algorithms and, consequently, the development of online discussions. Moreover, it highlights the factors that may encourage active participation, which can be utilized to design more effective communication campaigns.Source: SCIENTIFIC REPORTS, vol. 15 (issue 1)
DOI: 10.1038/s41598-025-88117-x
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2025 Journal article Open Access

Selective agreement, not sycophancy: investigating opinion dynamics in LLM interactions
Cau E., Pansanella V., Pedreschi D., Rossetti G.
Understanding how opinions evolve is essential for addressing phenomena such as polarization, radicalization, and consensus formation. In this work, we investigate how language shapes opinion dynamics among Large Language Model (LLM) agents by simulating multi-round debates.Using our framework, we find that agent populations consistently converge toward agreement, not through sycophancy or blind conformity, but via a structured and asymmetric persuasion process. Agents are more likely to accept, and thus be persuaded by, opinions that are more agreeable relative to the discussion framing, revealing a directional bias in how opinions evolve. LLM agents selectively adopt peers' views, showing neither bounded confidence nor indiscriminate agreement. Moreover, agents frequently produce fallacious arguments, and are significantly influenced by them: logical fallacies, especially those of relevance and credibility, play a measurable role in driving opinion change. These results not only uncover emergent behaviours in agents' dynamics, but also highlight the dual role of LLMs as both generators and victims of flawed reasoning, raising important considerations for their deployment in socially sensitive contexts.Source: EPJ DATA SCIENCE, vol. 14 (issue 1)
DOI: 10.1140/epjds/s13688-025-00579-1
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2023 Contribution to book Metadata Only Access

Towards a social Artificial Intelligence
Pedreschi D, Dignum F, Morini V, Pansanella V, Cornacchia G
Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.DOI: 10.1007/978-3-031-24349-3_21
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See at: CNR IRIS Restricted | link.springer.com

2023 Conference article Open Access

Change my mind: data driven estimate of open-mindedness from political discussions
Pansanella V, Morini V, Squartini T, Rossetti G
One of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals' personalities and relate it to emerging phenomena like polarization, radicalization, and ideology fragmentation. However, one of their major limitations lies in the strong assumptions they make on the initial distribution of such characteristics, often fixed so as to satisfy a normality hypothesis. Here we propose a data-driven methodology to estimate users' open-mindedness from online discussion data. Our analysis--focused on the political discussion taking place on Reddit during the first two years of the Trump presidency--unveils the existence of statistically diverse distributions of open-mindedness in annotated sub-populations (i.e., Republicans, Democrats, and Moderates/Neutrals). Moreover, such distributions appear to be stable across time and generated by individual users' behaviors that remain consistent and underdispersed.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 86-97. Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_8
Project(s): SoBigData-PlusPlus via OpenAIRE

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2023 Journal article Open Access

Mass media impact on opinion evolution in biased digital environments: a bounded confidence model
Pansanella V, Sîrbu A, Kertesz J, Rossetti G
People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and difusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena.Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the efects of the combination of social infuence and mass media infuence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confdence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets,modeled as stubborn agents. We analyzed four diferent media landscapes and found that an openminded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users' biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-39725-y
Project(s): HumanE-AI-Net via OpenAIRE

, SoBigData-PlusPlus via OpenAIRE

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See at: CNR IRIS Open Access | ISTI Repository | www.nature.com | CNR IRIS Restricted

2022 Conference article Open Access

From mean-field to complex topologies: network effects on the algorithmic bias model
Pansanella V, Rossetti G, Milli L
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 329-340. Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93413-2_28
Project(s): SoBigData-PlusPlus via OpenAIRE

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2022 Journal article Open Access

Modeling algorithmic bias: simplicial complexes and evolving network topologies
Pansanella V, Rossetti G, Milli L
Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content--co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-called Algorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erd?s-Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating the Adaptive Algorithmic Bias model. The second one explicitly models a peer-pressure mechanism where a majority--if there is one--can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both "close-mindedness" and algorithmic filtering.Source: APPLIED NETWORK SCIENCE, vol. 7
DOI: 10.1007/s41109-022-00495-7
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See at: appliednetsci.springeropen.com Open Access | CNR IRIS | ISTI Repository | CNR IRIS Restricted