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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
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
Other
Open Access
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters
Haim E., Fischer N., Citraro S., Rossetti G., Stella M.
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.DOI: 10.31234/osf.io/6zpre
DOI: 10.31234/osf.io/6zpre_v2
DOI: 10.31234/osf.io/6zpre_v1
DOI: 10.31234/osf.io/6zpre_v3
DOI: 10.48550/arxiv.2412.00530
Metrics:
Haim E., Fischer N., Citraro S., Rossetti G., Stella M.
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.DOI: 10.31234/osf.io/6zpre
DOI: 10.31234/osf.io/6zpre_v2
DOI: 10.31234/osf.io/6zpre_v1
DOI: 10.31234/osf.io/6zpre_v3
DOI: 10.48550/arxiv.2412.00530
Metrics:
See at:
arXiv.org e-Print Archive | doi.org | doi.org | doi.org | doi.org | CNR IRIS | osf.io | doi.org | CNR IRIS