2025
Conference article
Restricted
Beyond boundaries: capturing social segregation on hypernetworks
Failla A., Rossetti G., Cauteruccio F.
In recent years, the study of complex social systems has been fueled by the renewed interest in higher-order topologies, thus leading to the emergence of hypernetwork science. A critical and interesting phenomenon often characterizing social complex systems is segregation, i.e., the extent to which network entities are separated or clustered based on certain semantic attributes or features. This paper introduces a novel approach to studying segregation in hypernetworks. Firstly, we propose a general framework to extend classical segregation measures from dyadic to polyadic network structures. Then, we introduce a novel segregation measure called ``Random Walk HyperSegregation'' (RWHS), which exploits random walkers to estimate segregation at multiple scales. Through an extensive experimental study involving synthetic and real-world case studies, we illustrate the applicability and effectiveness of our measure. Moreover, we highlight the limits of classical segregation measures when extended to high-order topologies---conversely from RWHS, which effectively captured highly-segregated scenarios.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15211, pp. 40-55. Rende, Cosenza, Italy, 02–05/09/2024
DOI: 10.1007/978-3-031-78541-2_3
Metrics:
Failla A., Rossetti G., Cauteruccio F.
In recent years, the study of complex social systems has been fueled by the renewed interest in higher-order topologies, thus leading to the emergence of hypernetwork science. A critical and interesting phenomenon often characterizing social complex systems is segregation, i.e., the extent to which network entities are separated or clustered based on certain semantic attributes or features. This paper introduces a novel approach to studying segregation in hypernetworks. Firstly, we propose a general framework to extend classical segregation measures from dyadic to polyadic network structures. Then, we introduce a novel segregation measure called ``Random Walk HyperSegregation'' (RWHS), which exploits random walkers to estimate segregation at multiple scales. Through an extensive experimental study involving synthetic and real-world case studies, we illustrate the applicability and effectiveness of our measure. Moreover, we highlight the limits of classical segregation measures when extended to high-order topologies---conversely from RWHS, which effectively captured highly-segregated scenarios.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15211, pp. 40-55. Rende, Cosenza, Italy, 02–05/09/2024
DOI: 10.1007/978-3-031-78541-2_3
Metrics:
See at:
doi.org 
| Archivio della Ricerca - Università di Salerno | CNR IRIS | CNR IRIS | CNR IRIS | Archivio della Ricerca - Università di Salerno
2025
Conference article
Restricted
Quantifying attraction to extreme opinions in online debates
Perra D., Failla A., Rossetti G.
Opinion polarization and political segregation are key societal concerns, especially on social media. Although these phenomena have been traditionally attributed to homophily—preference for like-minded individuals—recent work in social psychology suggests that acrophily—preference for extreme rather than moderate opinions—might play a role as well. In this work, we introduce a methodology to estimate the degree of preference for connecting with users who hold strong opinions on social media. Our framework is composed of four phases: (i) opinion estimation, (ii) opinion thresholding, (iii) network construction, and (iv) acrophily estimation. We apply it to study the climate change debate on Reddit and find that users show higher-than-expected acrophilic patterns, especially if they are climate skeptics or have extreme opinions. Acrophilic patterns are stable over time, while polarization gradually leaves space for pluralism.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 411-424. Pisa, Italy, 14-16/10/2024
DOI: 10.1007/978-3-031-78980-9_26
Metrics:
Perra D., Failla A., Rossetti G.
Opinion polarization and political segregation are key societal concerns, especially on social media. Although these phenomena have been traditionally attributed to homophily—preference for like-minded individuals—recent work in social psychology suggests that acrophily—preference for extreme rather than moderate opinions—might play a role as well. In this work, we introduce a methodology to estimate the degree of preference for connecting with users who hold strong opinions on social media. Our framework is composed of four phases: (i) opinion estimation, (ii) opinion thresholding, (iii) network construction, and (iv) acrophily estimation. We apply it to study the climate change debate on Reddit and find that users show higher-than-expected acrophilic patterns, especially if they are climate skeptics or have extreme opinions. Acrophilic patterns are stable over time, while polarization gradually leaves space for pluralism.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 411-424. Pisa, Italy, 14-16/10/2024
DOI: 10.1007/978-3-031-78980-9_26
Metrics:
See at:
doi.org | CNR IRIS | 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 Z., Miori V., Tolomei G., Waheed T., Marchetti E., Calabrò A., Rossetti G., Stella M., Cazabet R., 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 A., Sebastiani F., Sperduti G., Nguyen D., Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte G., 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 J., Meyer T., 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 D., Di Giandomenico F., Jee E., 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 G.
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 Z., Miori V., Tolomei G., Waheed T., Marchetti E., Calabrò A., Rossetti G., Stella M., Cazabet R., 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 A., Sebastiani F., Sperduti G., Nguyen D., Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte G., 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 J., Meyer T., 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 D., Di Giandomenico F., Jee E., 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 G.
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
Exploring interconnections among atoms, brain, society, and cosmos with network science and explainable machine learning
Caligiore D., Monreale A., Rossetti G., Bongiorno A., Fisicaro G.
This paper presents a methodology combining Network Science (NS) and Explainable Machine Learning (XML) that could hypothetically uncover shared principles across seemingly disparate scientific domains. As an example, it presents how the approach could be applied to four fields: materials science, neuroscience, social science, and cosmology. The study focuses on criticality, a phenomenon associated with the transition of complex systems between states, characterized by sudden and significant behavioral shifts. By proposing a five-step methodology—ranging from relational data collection to cross-domain analysis with XML—the paper offers a hypothetical framework for potentially identifying criticality-related features in these fields and transferring insights across disciplines. The results of domains cross-fertilization could support practical applications, such as improving neuroprosthetics and brain-machine interfaces by leveraging criticality in materials science and neuroscience or developing advanced materials for space exploration. The parallels between neural and social networks could deepen our understanding of human behavior, while studying cosmic and social systems may reveal shared dynamics in large-scale, interconnected structures. A key benefit could be the possibility of using transfer learning, that is XML models trained in one domain might be adapted for use in another with limited data. For instance, if common aspects of criticality in neuroscience and cosmology are identified, an algorithm trained on brain data could be repurposed to detect critical states in cosmic systems, even with limited cosmic data. This interdisciplinary approach advances theoretical frameworks and fosters practical innovations, laying the groundwork for future research that could transform our understanding of complex systems across diverse scientific fields.Source: FRONTIERS IN COMPLEX SYSTEMS, vol. 3
DOI: 10.3389/fcpxs.2025.1604132
Metrics:
Caligiore D., Monreale A., Rossetti G., Bongiorno A., Fisicaro G.
This paper presents a methodology combining Network Science (NS) and Explainable Machine Learning (XML) that could hypothetically uncover shared principles across seemingly disparate scientific domains. As an example, it presents how the approach could be applied to four fields: materials science, neuroscience, social science, and cosmology. The study focuses on criticality, a phenomenon associated with the transition of complex systems between states, characterized by sudden and significant behavioral shifts. By proposing a five-step methodology—ranging from relational data collection to cross-domain analysis with XML—the paper offers a hypothetical framework for potentially identifying criticality-related features in these fields and transferring insights across disciplines. The results of domains cross-fertilization could support practical applications, such as improving neuroprosthetics and brain-machine interfaces by leveraging criticality in materials science and neuroscience or developing advanced materials for space exploration. The parallels between neural and social networks could deepen our understanding of human behavior, while studying cosmic and social systems may reveal shared dynamics in large-scale, interconnected structures. A key benefit could be the possibility of using transfer learning, that is XML models trained in one domain might be adapted for use in another with limited data. For instance, if common aspects of criticality in neuroscience and cosmology are identified, an algorithm trained on brain data could be repurposed to detect critical states in cosmic systems, even with limited cosmic data. This interdisciplinary approach advances theoretical frameworks and fosters practical innovations, laying the groundwork for future research that could transform our understanding of complex systems across diverse scientific fields.Source: FRONTIERS IN COMPLEX SYSTEMS, vol. 3
DOI: 10.3389/fcpxs.2025.1604132
Metrics:
See at:
CNR IRIS | www.frontiersin.org | CNR IRIS
2025
Conference article
Restricted
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 | CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
Participant behavior and community response in online mental health communities: insights from Reddit
Morini V., Sansoni M., Rossetti G., Pedreschi D., Castillo C.
The growing presence of online mutual-help communities has significantly changed how people access and provide mental health (MH) support. While extensive research has explored self-disclosure and social support dynamics within these communities, less is known about users’ distinctive behavioral patterns, posting intents, and community response. This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits, comprising over 3.4 million posts and 24 million comments from approximately 2.4 million users. We categorized subreddits based on the Diagnostic and Statistical Manual of Mental Disorders and compared the behavioral patterns in these communities with Reddit non-MH ones. Leveraging Reddit's post flair feature, we defined a ground truth for post intents and applied an automated classification method to infer intents across the dataset. We then used causal inference analysis to assess the effect of community responses on subsequent user behavior. Our analysis revealed that MH-related subreddits featured unique characteristics in content length, throwaway account usage, user actions, persistence, and community response. These online behaviors mirrored those in other mutual-help Reddit communities and resonated with offline patterns while diverging from non-support-oriented subreddits. We also found that seeking support and venting are the predominant posting intents, with users tending to maintain consistent intents over time. Furthermore, we observed that receiving comments and reactions significantly influenced users’ follow-up engagement, fostering increased participation. These findings highlight the supportive role of online MH communities and emphasize the need for tailored design to optimize user experience and support for individuals facing MH challenges.Source: COMPUTERS IN HUMAN BEHAVIOR, vol. 165
DOI: 10.1016/j.chb.2024.108544
Project(s): SoBigData-PlusPlus
Metrics:
Morini V., Sansoni M., Rossetti G., Pedreschi D., Castillo C.
The growing presence of online mutual-help communities has significantly changed how people access and provide mental health (MH) support. While extensive research has explored self-disclosure and social support dynamics within these communities, less is known about users’ distinctive behavioral patterns, posting intents, and community response. This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits, comprising over 3.4 million posts and 24 million comments from approximately 2.4 million users. We categorized subreddits based on the Diagnostic and Statistical Manual of Mental Disorders and compared the behavioral patterns in these communities with Reddit non-MH ones. Leveraging Reddit's post flair feature, we defined a ground truth for post intents and applied an automated classification method to infer intents across the dataset. We then used causal inference analysis to assess the effect of community responses on subsequent user behavior. Our analysis revealed that MH-related subreddits featured unique characteristics in content length, throwaway account usage, user actions, persistence, and community response. These online behaviors mirrored those in other mutual-help Reddit communities and resonated with offline patterns while diverging from non-support-oriented subreddits. We also found that seeking support and venting are the predominant posting intents, with users tending to maintain consistent intents over time. Furthermore, we observed that receiving comments and reactions significantly influenced users’ follow-up engagement, fostering increased participation. These findings highlight the supportive role of online MH communities and emphasize the need for tailored design to optimize user experience and support for individuals facing MH challenges.Source: COMPUTERS IN HUMAN BEHAVIOR, vol. 165
DOI: 10.1016/j.chb.2024.108544
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Computers in Human Behavior | CNR IRIS | www.sciencedirect.com | CNR IRIS
2024
Conference article
Restricted
FairNet: A Genetic Framework to Reduce Marginalization in social networks
Mazzoni F., Failla A., Rossetti G.
Discrimination in social networks often assumes the form of marginalization against nodes with specific features, e.g., segregation of/against minorities. In this work, we propose a metric that proxies social discrimination based on salient node features in a social network. Under the assumption that in a fair social system, all individuals should be enclosed in similar social circles representing the network in its entirety, our metric assigns a marginalization score to each node in the network, identifying if they are marginalized by similar nodes (e.g., a man marginalized by other men), by different nodes (e.g., a man marginalized by women), or not marginalized at all (i.e., the node has a fair neighborhood). Moreover, we introduce FairNet, a two-fold framework that aims to reduce network marginalization in partially- and fully-attributed networks by employing genetic algorithms. We evaluate our framework on networks emerging from online social interactions and find that the two components of FairNet are able to consistently reduce marginalization.Source: LECTURE NOTES IN COMPUTER SCIENCE, pp. 139-154. Rende, Cosenza, Italy, 02-05/09/2024
DOI: 10.1007/978-3-031-78541-2_9
Metrics:
Mazzoni F., Failla A., Rossetti G.
Discrimination in social networks often assumes the form of marginalization against nodes with specific features, e.g., segregation of/against minorities. In this work, we propose a metric that proxies social discrimination based on salient node features in a social network. Under the assumption that in a fair social system, all individuals should be enclosed in similar social circles representing the network in its entirety, our metric assigns a marginalization score to each node in the network, identifying if they are marginalized by similar nodes (e.g., a man marginalized by other men), by different nodes (e.g., a man marginalized by women), or not marginalized at all (i.e., the node has a fair neighborhood). Moreover, we introduce FairNet, a two-fold framework that aims to reduce network marginalization in partially- and fully-attributed networks by employing genetic algorithms. We evaluate our framework on networks emerging from online social interactions and find that the two components of FairNet are able to consistently reduce marginalization.Source: LECTURE NOTES IN COMPUTER SCIENCE, pp. 139-154. Rende, Cosenza, Italy, 02-05/09/2024
DOI: 10.1007/978-3-031-78541-2_9
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | CNR IRIS | link.springer.com
2024
Journal article
Open Access
Describing group evolution in temporal data using multi-faceted events
Failla A, Cazabet R., Rossetti G., Citraro S.
Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.Source: MACHINE LEARNING, vol. 113 (issue 10), pp. 7591-7615
DOI: 10.1007/s10994-024-06600-4
Project(s): BITUNAM
Metrics:
Failla A, Cazabet R., Rossetti G., Citraro S.
Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.Source: MACHINE LEARNING, vol. 113 (issue 10), pp. 7591-7615
DOI: 10.1007/s10994-024-06600-4
Project(s): BITUNAM
Metrics:
See at:
Machine Learning | CNR IRIS | link.springer.com | CNR IRIS
2024
Conference article
Open Access
LLM-generated word association norms
Abramski K., Lavorati C., Rossetti G., Stella M.
Word associations have been extensively used in psychology to study the rich structure of human conceptual knowledge. Recently, the study of word associations has been extended to investigating the knowledge encoded in LLMs. However, because of how the LLM word associations are accessed, existing approaches have been limited in the types of comparisons that can be made between humans and LLMs. To overcome this, we create LLM-generated word association norms modeled after the Small World of Words (SWOW) human-generated word association norms consisting of over 12,000 cue words. We prompt the language models with the same cues and participant profiles as those in the SWOW human-generated norms, and we conduct preliminary comparative analyses between humans and LLMs that explore differences in response variability, biases, concreteness effects, and network properties. Our exploration provides insights into how LLM-generated word associations can be used to investigate similarities and differences in how humans and LLMs process information.Source: FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, vol. 386, pp. 3-12. Malmö, Sweden, 10-14/06/2024
DOI: 10.3233/faia240177
Metrics:
Abramski K., Lavorati C., Rossetti G., Stella M.
Word associations have been extensively used in psychology to study the rich structure of human conceptual knowledge. Recently, the study of word associations has been extended to investigating the knowledge encoded in LLMs. However, because of how the LLM word associations are accessed, existing approaches have been limited in the types of comparisons that can be made between humans and LLMs. To overcome this, we create LLM-generated word association norms modeled after the Small World of Words (SWOW) human-generated word association norms consisting of over 12,000 cue words. We prompt the language models with the same cues and participant profiles as those in the SWOW human-generated norms, and we conduct preliminary comparative analyses between humans and LLMs that explore differences in response variability, biases, concreteness effects, and network properties. Our exploration provides insights into how LLM-generated word associations can be used to investigate similarities and differences in how humans and LLMs process information.Source: FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, vol. 386, pp. 3-12. Malmö, Sweden, 10-14/06/2024
DOI: 10.3233/faia240177
Metrics:
See at:
doi.org | ebooks.iospress.nl | CNR IRIS | IRIS - Institutional Research Information System of the University of Trento | IRIS - Institutional Research Information System of the University of Trento | CNR IRIS
2024
Conference article
Restricted
Whose voice matters? Authority and influence in the Italian Twitter debates on Covid-19
Mesina V., Failla A., Morini V., Rossetti G.
The Covid-19 pandemic intensified public discourse on social media, with Twitter becoming a key platform for information exchange. In such environments, authorities—influential figures from various domains—play a crucial role in shaping public opinion, having the power to influence offline behaviors both individually and collectively. In this work, we study the role of pro-vaccine and anti-vaccine authorities within the Italian Twitter debate on Covid-19 in five contextually relevant temporal windows corresponding to different pandemic phases. Analyzing a dataset of over ∼50M tweets, we identify central actors and quantify both their impact and their influence on users’ opinions. Our results suggest that while anti-vax authorities were able to gain more consensus during the vaccination phases, pro-vax authorities became more influential in the latter stage of the vaccination campaign.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE, vol. 1188, pp. 352-363. Istanbul, Türkiye, 10-12/12/2024
DOI: 10.1007/978-3-031-82431-9_29
Metrics:
Mesina V., Failla A., Morini V., Rossetti G.
The Covid-19 pandemic intensified public discourse on social media, with Twitter becoming a key platform for information exchange. In such environments, authorities—influential figures from various domains—play a crucial role in shaping public opinion, having the power to influence offline behaviors both individually and collectively. In this work, we study the role of pro-vaccine and anti-vaccine authorities within the Italian Twitter debate on Covid-19 in five contextually relevant temporal windows corresponding to different pandemic phases. Analyzing a dataset of over ∼50M tweets, we identify central actors and quantify both their impact and their influence on users’ opinions. Our results suggest that while anti-vax authorities were able to gain more consensus during the vaccination phases, pro-vax authorities became more influential in the latter stage of the vaccination campaign.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE, vol. 1188, pp. 352-363. Istanbul, Türkiye, 10-12/12/2024
DOI: 10.1007/978-3-031-82431-9_29
Metrics:
See at:
CNR IRIS | CNR IRIS | CNR IRIS | link.springer.com
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
2024
Journal article
Open Access
“I’m in the Bluesky Tonight”: insights from a year worth of social data
Failla A., Rossetti G.
: Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped "like" interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.Source: PLOS ONE, vol. 19 (issue 11)
DOI: 10.1371/journal.pone.0310330
DOI: 10.48550/arxiv.2404.18984
Project(s): SoBigData-PlusPlus
Metrics:
Failla A., Rossetti G.
: Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped "like" interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.Source: PLOS ONE, vol. 19 (issue 11)
DOI: 10.1371/journal.pone.0310330
DOI: 10.48550/arxiv.2404.18984
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | PLoS ONE | PLoS ONE | CNR IRIS | journals.plos.org | doi.org | CNR IRIS
2024
Journal article
Open Access
International mobility between the UK and Europe around Brexit: a data-driven study
Sîrbu A., Goglia D., Kim J., Magos P. M., Pollacci L., Spyratos S., Rossetti G., Iacus S. M.
Among the multiple effects of Brexit, changes in migration and mobility across Europe were expected. Several studies have analysed these aspects, mostly from the point of view of perceptions, motivations, economic effects, scenarios, and changes in migration from Central and Eastern European countries. In this study we propose an analysis of migration and cross-border mobility using an integrated data-driven approach. We investigate official statistics from Eurostat, together with non-traditional data, to give a more complete view of the changes after Brexit, at EU and regional level. Specifically, we employ scientific publication and Crunchbase data to study highly-skilled migration, Twitter and Air Passenger data to investigate monthly trends. While main trends are preserved across datasets, with a general decrease in migration towards the UK immediately after the referendum approval, we are able to also observe more fine grained trends specific to some data or regions. Furthermore, we relate the changes in mobility observed from Air Passenger data with attention to Brexit from Google Trends data.Source: JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, vol. 7 (issue 2), pp. 1451-1482
DOI: 10.1007/s42001-024-00277-4
Project(s): HumMingBird, SoBigData-PlusPlus
Metrics:
Sîrbu A., Goglia D., Kim J., Magos P. M., Pollacci L., Spyratos S., Rossetti G., Iacus S. M.
Among the multiple effects of Brexit, changes in migration and mobility across Europe were expected. Several studies have analysed these aspects, mostly from the point of view of perceptions, motivations, economic effects, scenarios, and changes in migration from Central and Eastern European countries. In this study we propose an analysis of migration and cross-border mobility using an integrated data-driven approach. We investigate official statistics from Eurostat, together with non-traditional data, to give a more complete view of the changes after Brexit, at EU and regional level. Specifically, we employ scientific publication and Crunchbase data to study highly-skilled migration, Twitter and Air Passenger data to investigate monthly trends. While main trends are preserved across datasets, with a general decrease in migration towards the UK immediately after the referendum approval, we are able to also observe more fine grained trends specific to some data or regions. Furthermore, we relate the changes in mobility observed from Air Passenger data with attention to Brexit from Google Trends data.Source: JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, vol. 7 (issue 2), pp. 1451-1482
DOI: 10.1007/s42001-024-00277-4
Project(s): HumMingBird
, SoBigData-PlusPlus Metrics:
See at:
Journal of Computational Social Science | CNR IRIS | link.springer.com | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS | Archivio della Ricerca - Università di Pisa
2024
Journal article
Open Access
Online posting effects: unveiling the non-linear journeys of users in depression communities on Reddit
Morini V., Citraro S., Sajno E., Sansoni M., Riva G., Stella M., Rossetti G.
Social media platforms have become pivotal as self-help forums, enabling individuals to share personal experiences and seek support. However, on topics as sensitive as depression, what are the consequences of online self-disclosure? Here, we delve into the dynamics of mental health discourse on various Reddit boards focused on depression. To this aim, we introduce a data-informed framework reconstructing online dynamics from 303k users interacting over two years. Through user-generated content, we identify 4 distinct clusters representing different psychological states. Our analysis unveils online posting effects: a user can transition to another psychological state after online exposure to peers’ emotional/semantic content. As described by conditional Markov chains and different levels of social exposure, users’ transitions reveal navigation through both positive and negative phases in a spiral rather than a linear progression. Interpreted in light of psychological literature, related particularly to the Patient Health Engagement (PHE) model, our findings can provide evidence that the type and layout of online social interactions have an impact on users’ “journeys” when posting about depression.Source: COMPUTERS IN HUMAN BEHAVIOR REPORTS, vol. 17 (issue 100542 (n. articolo))
DOI: 10.1016/j.chbr.2024.100542
Project(s): SoBigData-PlusPlus
Metrics:
Morini V., Citraro S., Sajno E., Sansoni M., Riva G., Stella M., Rossetti G.
Social media platforms have become pivotal as self-help forums, enabling individuals to share personal experiences and seek support. However, on topics as sensitive as depression, what are the consequences of online self-disclosure? Here, we delve into the dynamics of mental health discourse on various Reddit boards focused on depression. To this aim, we introduce a data-informed framework reconstructing online dynamics from 303k users interacting over two years. Through user-generated content, we identify 4 distinct clusters representing different psychological states. Our analysis unveils online posting effects: a user can transition to another psychological state after online exposure to peers’ emotional/semantic content. As described by conditional Markov chains and different levels of social exposure, users’ transitions reveal navigation through both positive and negative phases in a spiral rather than a linear progression. Interpreted in light of psychological literature, related particularly to the Patient Health Engagement (PHE) model, our findings can provide evidence that the type and layout of online social interactions have an impact on users’ “journeys” when posting about depression.Source: COMPUTERS IN HUMAN BEHAVIOR REPORTS, vol. 17 (issue 100542 (n. articolo))
DOI: 10.1016/j.chbr.2024.100542
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Computers in Human Behavior Reports | CNR IRIS | www.sciencedirect.com | CNR IRIS
2024
Journal article
Open Access
Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges
Stella M., Citraro S., Rossetti G., Marinazzo D., Kenett Y. N., Vitevitch M. S.
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.Source: PSYCHONOMIC BULLETIN & REVIEW, vol. 31, pp. 1981-2004
DOI: 10.3758/s13423-024-02473-9
Metrics:
Stella M., Citraro S., Rossetti G., Marinazzo D., Kenett Y. N., Vitevitch M. S.
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.Source: PSYCHONOMIC BULLETIN & REVIEW, vol. 31, pp. 1981-2004
DOI: 10.3758/s13423-024-02473-9
Metrics:
See at:
Psychonomic Bulletin & Review | CNR IRIS | link.springer.com | Ghent University Academic Bibliography | Ghent University Academic Bibliography | IRIS - Institutional Research Information System of the University of Trento | IRIS - Institutional Research Information System of the University of Trento | Ghent University Academic Bibliography | CNR IRIS
2024
Journal article
Open Access
Voices of rape: cognitive networks link passive voice usage to psychological distress in online narratives
Abramski K., Ciringione L., Rossetti G., Stella M.
Past studies of sexual assault have found that passive voice descriptions of rape elicit an increased perception of victim responsibility compared to active voice narratives (Bohner, 2001), contributing to victim blaming and the perpetuation of rape myths. Building on this, we investigate the relationship between passive/active voice usage and perception, but from the perspective of rape survivors as disclosed in their online rape narratives. We collect rape narratives from Reddit's r/sexualassault board and group them into a passive voice group and an active voice group. We detect differences between the two groups of text using a cognitive network science approach that creates network representations from text such that nodes represent words/concepts while links represent syntactic and semantic relationships between them. We systematically identify nodes that are significantly more central to one network compared to the other, thus identifying characteristic concepts that semantically differentiate the two groups of narratives. We then investigate the contexts of these concepts applying semantic frame analysis. We find that concepts related to psychological distress (e.g. PTSD, flashback) are significantly more central to passive voice narratives, providing quantitative evidence of a link between passive voice usage and an increased focus on psychological distress. We also find that family members (e.g. parent, brother) are more central to active voice narratives, suggesting a connection between active voice usage and an increased focus on others' roles in rape survivors' experiences. Our quantitative results reveal an important link between language and mental health that has valuable implications for therapeutic interventions.Source: COMPUTERS IN HUMAN BEHAVIOR, vol. 158 (issue 108266)
DOI: 10.1016/j.chb.2024.108266
DOI: 10.31234/osf.io/92bek
Project(s): SoBigData-PlusPlus
Metrics:
Abramski K., Ciringione L., Rossetti G., Stella M.
Past studies of sexual assault have found that passive voice descriptions of rape elicit an increased perception of victim responsibility compared to active voice narratives (Bohner, 2001), contributing to victim blaming and the perpetuation of rape myths. Building on this, we investigate the relationship between passive/active voice usage and perception, but from the perspective of rape survivors as disclosed in their online rape narratives. We collect rape narratives from Reddit's r/sexualassault board and group them into a passive voice group and an active voice group. We detect differences between the two groups of text using a cognitive network science approach that creates network representations from text such that nodes represent words/concepts while links represent syntactic and semantic relationships between them. We systematically identify nodes that are significantly more central to one network compared to the other, thus identifying characteristic concepts that semantically differentiate the two groups of narratives. We then investigate the contexts of these concepts applying semantic frame analysis. We find that concepts related to psychological distress (e.g. PTSD, flashback) are significantly more central to passive voice narratives, providing quantitative evidence of a link between passive voice usage and an increased focus on psychological distress. We also find that family members (e.g. parent, brother) are more central to active voice narratives, suggesting a connection between active voice usage and an increased focus on others' roles in rape survivors' experiences. Our quantitative results reveal an important link between language and mental health that has valuable implications for therapeutic interventions.Source: COMPUTERS IN HUMAN BEHAVIOR, vol. 158 (issue 108266)
DOI: 10.1016/j.chb.2024.108266
DOI: 10.31234/osf.io/92bek
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Computers in Human Behavior | doi.org | IRIS Cnr | IRIS Cnr | IRIS - Institutional Research Information System of the University of Trento | IRIS - Institutional Research Information System of the University of Trento | CNR IRIS
2023
Journal article
Open Access
Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities
Joseph Sm, Citraro S, Morini V, Rossetti G, Stella M
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of "regret", "unhealthy lifestyle" and "low mental well-being". Importantly, negative associations and emotions co-existed with trustful/positive language, focused on "getting better". This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.Source: PHYSICA. A, vol. 610 (issue 128336)
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
Joseph Sm, Citraro S, Morini V, Rossetti G, Stella M
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of "regret", "unhealthy lifestyle" and "low mental well-being". Importantly, negative associations and emotions co-existed with trustful/positive language, focused on "getting better". This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.Source: PHYSICA. A, vol. 610 (issue 128336)
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | Physica A Statistical Mechanics and its Applications | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Feature-rich multiplex lexical networks reveal mental strategies of early language learning
Citraro S, Vitevitch Ms, Stella M, Rossetti G
Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms--fragmented across linguistics, psychology and computer science--by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.Source: SCIENTIFIC REPORTS, vol. 13 (issue 1)
DOI: 10.1038/s41598-022-27029-6
DOI: 10.48550/arxiv.2201.05061
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S, Vitevitch Ms, Stella M, Rossetti G
Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms--fragmented across linguistics, psychology and computer science--by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.Source: SCIENTIFIC REPORTS, vol. 13 (issue 1)
DOI: 10.1038/s41598-022-27029-6
DOI: 10.48550/arxiv.2201.05061
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | Scientific Reports | CNR IRIS | ISTI Repository | www.nature.com | doi.org | CNR IRIS
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
Metrics:
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
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit
Failla A, Citraro S, Rossetti G
Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 150-161. Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_13
Project(s): SoBigData-PlusPlus
Metrics:
Failla A, Citraro S, Rossetti G
Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 150-161. Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_13
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS