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2026
Conference article
Open Access
Generalizing hypergraph Ego-networks and their temporal stability
Cauteruccio Francesco, Citraro Salvatore, Failla Andrea, Rossetti Giulio
Ego-networks provide a local perspective on networked systems by focusing on a central entity and its immediate relational context. While extensively studied in static pairwise interaction models, their analysis in dynamic and higher-order interaction contexts remains limited. In this paper, we introduce a formal analytical framework to generalize ego-networks to temporal hypergraphs, capable of modeling complex, non-dyadic group interactions over time. Our framework consists of two main components, namely, the concept of Rooted Ego-Networks (RENs), and generalized similarity criterion. RENs extend traditional ego-networks by capturing temporal and structural characteristics of groups centered on one or more nodes. We define multiple inclusion functions and similarity criteria to compare RENs across time, and we introduce the notion of stability to identify persistent local structures. Through empirical evaluation on real-world temporal hypergraphs from the SocioPatterns database, we illustrate the expressiveness of our approach in capturing and analyzing the evolution of localized group dynamics. To the best of our knowledge, this is the first comprehensive framework for analyzing the temporal and non-monotonic evolution of ego structures in higher-order networks.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16322, pp. 53-67. Niagara Falls, ON, Canada, 25-28/08/2025
DOI: 10.1007/978-3-032-13513-1_5
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Cauteruccio Francesco, Citraro Salvatore, Failla Andrea, Rossetti Giulio
Ego-networks provide a local perspective on networked systems by focusing on a central entity and its immediate relational context. While extensively studied in static pairwise interaction models, their analysis in dynamic and higher-order interaction contexts remains limited. In this paper, we introduce a formal analytical framework to generalize ego-networks to temporal hypergraphs, capable of modeling complex, non-dyadic group interactions over time. Our framework consists of two main components, namely, the concept of Rooted Ego-Networks (RENs), and generalized similarity criterion. RENs extend traditional ego-networks by capturing temporal and structural characteristics of groups centered on one or more nodes. We define multiple inclusion functions and similarity criteria to compare RENs across time, and we introduce the notion of stability to identify persistent local structures. Through empirical evaluation on real-world temporal hypergraphs from the SocioPatterns database, we illustrate the expressiveness of our approach in capturing and analyzing the evolution of localized group dynamics. To the best of our knowledge, this is the first comprehensive framework for analyzing the temporal and non-monotonic evolution of ego structures in higher-order networks.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16322, pp. 53-67. Niagara Falls, ON, Canada, 25-28/08/2025
DOI: 10.1007/978-3-032-13513-1_5
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
See at:
CNR IRIS 
| link.springer.com | doi.org 
| CNR IRIS | CNR IRIS
2025
Conference article
Open Access
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:
CNR IRIS | link.springer.com | doi.org | Archivio della Ricerca - Università di Salerno | CNR IRIS | CNR IRIS | Archivio della Ricerca - Università di Salerno
2025
Journal article
Open Access
Characterizing user archetypes and discussions on social hypernetworks
Failla A., Citraro S., Rossetti G., Cauteruccio F.
In recent years, the proliferation of social platforms has drastically transformed how individuals interact, organize, and share information. In this scenario, there has been an unprecedented increase in the scale and complexity of interactions and, at the same time, little to no research about certain fringe social platforms. In this paper, we present a multi-dimensional framework for characterizing nodes and hyperedges in social hypernetworks, with a focus on the understudied alt-right platform Scored.co. Our approach integrates the possibility of studying higher-order interactions, thanks to the hypernetwork representation, and various node features such as user activity, sentiment, and toxicity, with the aim of defining distinct user archetypes and understanding their roles within the network. Utilizing a comprehensive dataset from Scored.co, consisting of more than 4.4 M posts and 36.9 M comments, we analyze the dynamics of these archetypes over time and explore their interactions and influence within the community. We identify eight archetypes, with the largest group comprising over 15,000 users, and observe that 44% of interactions involve at least five participants, highlighting the importance of higher-order modeling. Furthermore, we find significant archetype transitions and stable yet locally dense interaction patterns, with users exposed to roughly 1000 unique peers on average. The framework’s versatility allows for detailed analysis of both individual user behaviors and broader social structures. Our findings highlight the importance of higher-order interactions and node features in understanding social dynamics, and offer new insights into the roles and behaviors that emerge in complex online environments.Source: BIG DATA AND COGNITIVE COMPUTING, vol. 9 (issue 9)
DOI: 10.3390/bdcc9090236
Metrics:
Failla A., Citraro S., Rossetti G., Cauteruccio F.
In recent years, the proliferation of social platforms has drastically transformed how individuals interact, organize, and share information. In this scenario, there has been an unprecedented increase in the scale and complexity of interactions and, at the same time, little to no research about certain fringe social platforms. In this paper, we present a multi-dimensional framework for characterizing nodes and hyperedges in social hypernetworks, with a focus on the understudied alt-right platform Scored.co. Our approach integrates the possibility of studying higher-order interactions, thanks to the hypernetwork representation, and various node features such as user activity, sentiment, and toxicity, with the aim of defining distinct user archetypes and understanding their roles within the network. Utilizing a comprehensive dataset from Scored.co, consisting of more than 4.4 M posts and 36.9 M comments, we analyze the dynamics of these archetypes over time and explore their interactions and influence within the community. We identify eight archetypes, with the largest group comprising over 15,000 users, and observe that 44% of interactions involve at least five participants, highlighting the importance of higher-order modeling. Furthermore, we find significant archetype transitions and stable yet locally dense interaction patterns, with users exposed to roughly 1000 unique peers on average. The framework’s versatility allows for detailed analysis of both individual user behaviors and broader social structures. Our findings highlight the importance of higher-order interactions and node features in understanding social dynamics, and offer new insights into the roles and behaviors that emerge in complex online environments.Source: BIG DATA AND COGNITIVE COMPUTING, vol. 9 (issue 9)
DOI: 10.3390/bdcc9090236
Metrics:
See at:
Big Data and Cognitive Computing | CNR IRIS | www.mdpi.com | Archivio della Ricerca - Università di Salerno | Archivio della Ricerca - Università di Salerno | CNR IRIS | Archivio della Ricerca - Università di Salerno
2025
Conference article
Open Access
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:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
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.
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
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
Conference article
Open Access
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:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
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
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 | link.springer.com | CNR IRIS | 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
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
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2023
Journal article
Open Access
Attributed stream hypergraphs: temporal modeling of node-attributed high-order interactions
Failla A, Citraro S, Rossetti G
Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates into a network all those elements that are driven by information which is external to the structure, like node properties or the flow of time. This paper proposes a novel toolbox, that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and feature-rich elements for representing, mining, and analyzing complex networks. Applied to social network analysis, ASHs can characterize complex social phenomena along topological, dynamic and attributive elements. Experiments on real-world face-to-face and online social media interactions highlight that ASHs can easily allow for the analyses, among others, of high-order groups' homophily, nodes' homophily with respect to the hyperedges in which nodes participate, and time-respecting paths between hyperedges.Source: APPLIED NETWORK SCIENCE, vol. 8 (issue 1)
DOI: 10.1007/s41109-023-00555-6
DOI: 10.48550/arxiv.2303.18226
Project(s): SoBigData-PlusPlus
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Failla A, Citraro S, Rossetti G
Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates into a network all those elements that are driven by information which is external to the structure, like node properties or the flow of time. This paper proposes a novel toolbox, that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and feature-rich elements for representing, mining, and analyzing complex networks. Applied to social network analysis, ASHs can characterize complex social phenomena along topological, dynamic and attributive elements. Experiments on real-world face-to-face and online social media interactions highlight that ASHs can easily allow for the analyses, among others, of high-order groups' homophily, nodes' homophily with respect to the hyperedges in which nodes participate, and time-respecting paths between hyperedges.Source: APPLIED NETWORK SCIENCE, vol. 8 (issue 1)
DOI: 10.1007/s41109-023-00555-6
DOI: 10.48550/arxiv.2303.18226
Project(s): SoBigData-PlusPlus
Metrics:
See at:
appliednetsci.springeropen.com | Applied Network Science | CNR IRIS | ISTI Repository | doi.org | CNR IRIS