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 scie... nce 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. [show more]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 scie... nce 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. [show more]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
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. [show more]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:
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. [show more]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:
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appliednetsci.springeropen.com | Applied Network Science | CNR IRIS | ISTI Repository | doi.org | 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. [show more]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. [show more]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