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2025
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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.
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 
| www.isti.cnr.it | CNR IRIS 
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
Metrics:
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
Metrics:
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EPJ Data Science | CNR IRIS | link.springer.com | Software Heritage | Software Heritage | Software Heritage | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | CNR IRIS
2025
Conference article
Open Access
Bots of a feather: mixing biases in LLMs’ opinion dynamics
Cau E., Failla A., Rossetti G.
The rapid integration of Large Language Models (LLMs) into everyday applications raises critical questions about their group in- teractions, consensus formation, and potential to mimic human-like be- havior. Although initial research has explored the evolution of opinions within LLM populations, these efforts often rely on simplistic network assumptions, such as uniform connections among agents, thereby over- looking the influence of more realistic network topologies. This paper introduces a framework for examining opinion dynamics among LLM agents within various network structures. We perform several multi- model simulations on network topologies with known locally assorta- tive/disassortative mixing patterns. We find that convergence is quicker in mostly-disassortative networks compared to networks with no mixing biases. However, the joint effect of assortative and disassortative patterns leads to slower/no convergence.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE, pp. 166-176. Istanbul, Turkey, 10-12/12/2024
DOI: 10.1007/978-3-031-82439-5_14
Metrics:
Cau E., Failla A., Rossetti G.
The rapid integration of Large Language Models (LLMs) into everyday applications raises critical questions about their group in- teractions, consensus formation, and potential to mimic human-like be- havior. Although initial research has explored the evolution of opinions within LLM populations, these efforts often rely on simplistic network assumptions, such as uniform connections among agents, thereby over- looking the influence of more realistic network topologies. This paper introduces a framework for examining opinion dynamics among LLM agents within various network structures. We perform several multi- model simulations on network topologies with known locally assorta- tive/disassortative mixing patterns. We find that convergence is quicker in mostly-disassortative networks compared to networks with no mixing biases. However, the joint effect of assortative and disassortative patterns leads to slower/no convergence.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE, pp. 166-176. Istanbul, Turkey, 10-12/12/2024
DOI: 10.1007/978-3-031-82439-5_14
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Trends and topics: characterizing echo chambers’ topological stability and in-group attitudes
Cau E., Morini V., Rossetti G.
Nowadays, online debates focusing on a wide spectrum of topics are often characterized by clashes of polarized communities, each fiercely supporting a specific stance. Such debates are sometimes fueled by the presence of echo chambers, insulated systems whose users’ opinions are exacerbated due to the effect of repetition and by the active exclusion of opposite views. This paper offers a framework to explore how echo chambers evolve through time, considering their users’ interaction patterns and the content/attitude they convey while addressing specific controversial issues. The framework is then tested on three Reddit case studies focused on sociopolitical issues (gun control, American politics, and minority discrimination) during the first two years and a half of Donald Trump’s presidency and on an X/Twitter dataset involving BLM discussion tied to the EURO 2020 football championship. Analytical results unveil that polarized users will likely keep their affiliation to echo chambers in time. Moreover, we observed that the attitudes conveyed by Reddit users who joined risky epistemic enclaves are characterized by a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues (e.g., fascism, school shootings, or police violence) while X/Twitter ones often tend to express more positive feelings w.r.t. those involved into less polarized communities.Source: PLOS COMPLEX SYSTEMS, vol. 1 (issue 2)
DOI: 10.1371/journal.pcsy.0000008
DOI: 10.48550/arxiv.2307.15610
Project(s): SoBigData-PlusPlus
Metrics:
Cau E., Morini V., Rossetti G.
Nowadays, online debates focusing on a wide spectrum of topics are often characterized by clashes of polarized communities, each fiercely supporting a specific stance. Such debates are sometimes fueled by the presence of echo chambers, insulated systems whose users’ opinions are exacerbated due to the effect of repetition and by the active exclusion of opposite views. This paper offers a framework to explore how echo chambers evolve through time, considering their users’ interaction patterns and the content/attitude they convey while addressing specific controversial issues. The framework is then tested on three Reddit case studies focused on sociopolitical issues (gun control, American politics, and minority discrimination) during the first two years and a half of Donald Trump’s presidency and on an X/Twitter dataset involving BLM discussion tied to the EURO 2020 football championship. Analytical results unveil that polarized users will likely keep their affiliation to echo chambers in time. Moreover, we observed that the attitudes conveyed by Reddit users who joined risky epistemic enclaves are characterized by a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues (e.g., fascism, school shootings, or police violence) while X/Twitter ones often tend to express more positive feelings w.r.t. those involved into less polarized communities.Source: PLOS COMPLEX SYSTEMS, vol. 1 (issue 2)
DOI: 10.1371/journal.pcsy.0000008
DOI: 10.48550/arxiv.2307.15610
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | doi.org | CNR IRIS | journals.plos.org | doi.org | CNR IRIS