2023
Journal article
Open Access
Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities
Joseph S. M., 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 (Print) 610 (2023). doi:10.1016/j.physa.2022.128336
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
Joseph S. M., 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 (Print) 610 (2023). doi:10.1016/j.physa.2022.128336
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository 
| Physica A Statistical Mechanics and its Applications 
| www.sciencedirect.com | CNR ExploRA
2023
Journal article
Open Access
Feature-rich multiplex lexical networks reveal mental strategies of early language learning
Citraro S., Vitevitch M. S., 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 (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-022-27029-6
DOI: 10.1038/s41598-022-27029-6
DOI: 10.48550/arxiv.2201.05061
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Vitevitch M. S., 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 (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-022-27029-6
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 | ISTI Repository | www.nature.com | doi.org | CNR ExploRA
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: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, 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: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, 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:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
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: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, 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: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, 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:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Open Access
Will you take the knee? Italian twitter echo chambers' genesis during EURO 2020
Buongiovanni C., Candusso R., Cerretini G., Febbe D., Morini V., Rossetti G.
Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.Source: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 29–40, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_3
Project(s): SoBigData-PlusPlus
Metrics:
Buongiovanni C., Candusso R., Cerretini G., Febbe D., Morini V., Rossetti G.
Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.Source: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 29–40, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_3
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2023
Journal article
Open Access
Advanced analysis technologies for social media
Guidi B., Iglesias C. A., Rossetti G., Koidl K.
Interest in social media has only increased with time. Social media today represent the main channel to communicate and share personal information. Social media analysis usually combines content-based and network-based analysis. While content-based approaches analyze media using media analysis techniques, network-based approaches analyze static and dynamic network properties with the aim of detecting influencers for marketing purposes. The network-based analysis represents a fundamental process in order to understand the dynamics of these platforms. New techniques and technologies have been proposed in order to enrich the social media analytics field. In particular, decentralized approaches have been proposed in order to face privacy issues, and AI has been applied in order to improve analysis over large sets of data. The main goal of this Special Issue is to collect research contributions, applications, analyses, methodologies, or strategies that strengthen or face the knowledge of social media thanks to advanced analyses or new technologies, such as P2P networks or blockchain. In detail, 5 papers have been published in the Special Issue out of a total of 10 submitted. The next sections provide a brief summary of each of the papers published.Source: Applied sciences 13 (2023). doi:10.3390/app13031909
DOI: 10.3390/app13031909
Metrics:
Guidi B., Iglesias C. A., Rossetti G., Koidl K.
Interest in social media has only increased with time. Social media today represent the main channel to communicate and share personal information. Social media analysis usually combines content-based and network-based analysis. While content-based approaches analyze media using media analysis techniques, network-based approaches analyze static and dynamic network properties with the aim of detecting influencers for marketing purposes. The network-based analysis represents a fundamental process in order to understand the dynamics of these platforms. New techniques and technologies have been proposed in order to enrich the social media analytics field. In particular, decentralized approaches have been proposed in order to face privacy issues, and AI has been applied in order to improve analysis over large sets of data. The main goal of this Special Issue is to collect research contributions, applications, analyses, methodologies, or strategies that strengthen or face the knowledge of social media thanks to advanced analyses or new technologies, such as P2P networks or blockchain. In detail, 5 papers have been published in the Special Issue out of a total of 10 submitted. The next sections provide a brief summary of each of the papers published.Source: Applied sciences 13 (2023). doi:10.3390/app13031909
DOI: 10.3390/app13031909
Metrics:
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Applied Sciences | ISTI Repository | www.mdpi.com | CNR ExploRA
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 8 (2023). doi:10.1007/s41109-023-00555-6
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.Source: Applied network science 8 (2023). doi:10.1007/s41109-023-00555-6
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 | ISTI Repository | doi.org | CNR ExploRA
2023
Journal article
Open Access
Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning
Citraro S., Warner-Willich J., Battiston F., Siew C. S. Q., Rossetti G., Stella M.
Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.Source: New ideas in psychology 71 (2023). doi:10.1016/j.newideapsych.2023.101034
DOI: 10.1016/j.newideapsych.2023.101034
Metrics:
Citraro S., Warner-Willich J., Battiston F., Siew C. S. Q., Rossetti G., Stella M.
Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.Source: New ideas in psychology 71 (2023). doi:10.1016/j.newideapsych.2023.101034
DOI: 10.1016/j.newideapsych.2023.101034
Metrics:
See at:
ISTI Repository | New Ideas in Psychology | www.sciencedirect.com | CNR ExploRA
2023
Journal article
Open Access
Towards hypergraph cognitive networks as feature-rich models of knowledge
Citraro S., De Deyne S., Stella M., Rossetti G.
Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading "airplane" makes one think of "air " and "pollution", and this is represented by links "airplane"-"air" and "airplane"-"pollution"). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N = 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.Source: EPJ 12 (2023). doi:10.1140/epjds/s13688-023-00409-2
DOI: 10.1140/epjds/s13688-023-00409-2
Metrics:
Citraro S., De Deyne S., Stella M., Rossetti G.
Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading "airplane" makes one think of "air " and "pollution", and this is represented by links "airplane"-"air" and "airplane"-"pollution"). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N = 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.Source: EPJ 12 (2023). doi:10.1140/epjds/s13688-023-00409-2
DOI: 10.1140/epjds/s13688-023-00409-2
Metrics:
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epjdatascience.springeropen.com | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Mass media impact on opinion evolution in biased digital environments: a bounded confidence model
Pansanella V., Sîrbu A., Kertesz J., Rossetti G.
People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and difusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the efects of the combination of social infuence and mass media infuence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confdence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four diferent media landscapes and found that an openminded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users' biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-39725-y
DOI: 10.1038/s41598-023-39725-y
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Metrics:
Pansanella V., Sîrbu A., Kertesz J., Rossetti G.
People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and difusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the efects of the combination of social infuence and mass media infuence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confdence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four diferent media landscapes and found that an openminded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users' biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-39725-y
DOI: 10.1038/s41598-023-39725-y
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | www.nature.com | CNR ExploRA
2023
Journal article
Open Access
Cognitive network science reveals bias in GPT-3, GPT-3.5 turbo, and GPT-4 mirroring math anxiety in high-school students
Abramski K., Citraro S., Lombardi L., Rossetti G., Stella M.
Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI's models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Source: Big data and cognitive computing 7 (2023). doi:10.3390/bdcc7030124
DOI: 10.3390/bdcc7030124
Metrics:
Abramski K., Citraro S., Lombardi L., Rossetti G., Stella M.
Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI's models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Source: Big data and cognitive computing 7 (2023). doi:10.3390/bdcc7030124
DOI: 10.3390/bdcc7030124
Metrics:
See at:
Big Data and Cognitive Computing | ISTI Repository | www.mdpi.com | CNR ExploRA
2023
Journal article
Open Access
Academic mobility from a big data perspective
Pollacci L., Milli L., Bircan T., Rossetti G.
Understanding the careers and movements of highly skilled people plays an ever-increasing role in today's global knowledgebased economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers' movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.Source: International Journal of Data Science and Analytics (Online) (2023). doi:10.1007/s41060-023-00432-6
DOI: 10.1007/s41060-023-00432-6
DOI: 10.21203/rs.3.rs-1510153/v1
Project(s): HumMingBird, SoBigData-PlusPlus
Metrics:
Pollacci L., Milli L., Bircan T., Rossetti G.
Understanding the careers and movements of highly skilled people plays an ever-increasing role in today's global knowledgebased economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers' movements over time. To this end, we define the Yearly Degree of Collaborations Index, which measures the annual tendency of researchers to collaborate intra-nationally, and two scores to measure the mobility in and out of countries, as well as their balance.Source: International Journal of Data Science and Analytics (Online) (2023). doi:10.1007/s41060-023-00432-6
DOI: 10.1007/s41060-023-00432-6
DOI: 10.21203/rs.3.rs-1510153/v1
Project(s): HumMingBird
, SoBigData-PlusPlus Metrics:
See at:
International Journal of Data Science and Analytics | doi.org | link.springer.com | ISTI Repository | CNR ExploRA
2022
Contribution to book
Open Access
Big Data Analytics and Instagram: an exploratory study on Italian hotel accounts
Pianese T., Rossetti G., Morini V.
A wealth of tourism-related data is available on the Internet, particularly on social networking sites (SNSs) like Facebook and Instagram. Big data analytics (BDA) allows this large quantity of data to be processed, supported by machine learning and artificial intelligence, and gain an in-depth understanding of traveller preferences and behaviours. With regard to hotels, the analysis of data from SNSs provides countless actionable insights into customers'socio-demographic features, habits, daily trends and brand attitudes. This enables communication to be perfectly targeted, besides supplying valuable information to improve customer satisfaction. Nevertheless, the study of the implications of the automatic processing of data from SNSs in the hotel industry is still in its embryonic state. In order to demonstrate the utility of BDA to under- stand how hotels leverage SNSs, we conducted an exploratory study on the Instagram accounts - the photo-sharing SNS known worldwide - of eleven Italian hotels. To this end, the average sentiment score, the average length, lexical diversity and word clouds were calculated on textual data, collected with the instagrapi python package and pre-processed leveraging a standard NLP pipeline. These evidenced different stages of implementation of digital communication on SNSs, shorter text-based messages written on Instagram compared to other SNSs, and specific patterns of user engagement in hotel accounts. BDA also provides information about the online self-promotion process: hotel digital communication is clearly connected to destination, and hashtags are chosen to reach the desired community of travellers.Source: Tourism, Hospitality and Culture 4.0: shifting towards the metaverse, edited by Buonincontri Piera, Luisa Errichiello, Roberto Micera, pp. 13–36. Milano: Mc Graw-Hill, 2022
Project(s): SoBigData-PlusPlus
Pianese T., Rossetti G., Morini V.
A wealth of tourism-related data is available on the Internet, particularly on social networking sites (SNSs) like Facebook and Instagram. Big data analytics (BDA) allows this large quantity of data to be processed, supported by machine learning and artificial intelligence, and gain an in-depth understanding of traveller preferences and behaviours. With regard to hotels, the analysis of data from SNSs provides countless actionable insights into customers'socio-demographic features, habits, daily trends and brand attitudes. This enables communication to be perfectly targeted, besides supplying valuable information to improve customer satisfaction. Nevertheless, the study of the implications of the automatic processing of data from SNSs in the hotel industry is still in its embryonic state. In order to demonstrate the utility of BDA to under- stand how hotels leverage SNSs, we conducted an exploratory study on the Instagram accounts - the photo-sharing SNS known worldwide - of eleven Italian hotels. To this end, the average sentiment score, the average length, lexical diversity and word clouds were calculated on textual data, collected with the instagrapi python package and pre-processed leveraging a standard NLP pipeline. These evidenced different stages of implementation of digital communication on SNSs, shorter text-based messages written on Instagram compared to other SNSs, and specific patterns of user engagement in hotel accounts. BDA also provides information about the online self-promotion process: hotel digital communication is clearly connected to destination, and hashtags are chosen to reach the desired community of travellers.Source: Tourism, Hospitality and Culture 4.0: shifting towards the metaverse, edited by Buonincontri Piera, Luisa Errichiello, Roberto Micera, pp. 13–36. Milano: Mc Graw-Hill, 2022
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
Can you always reap what you sow? Network and functional data analysis of venture capital investments in health-tech companies
Esposito C., Gortan M, Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.
"Success" of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize "success" by introducing progressively more refined definitions, and we find a positive association between such definitions and the centrality of a company. In particular, we are able to cluster funding trajectories of firms into two groups capturing different "success" regimes and to link the probability of belonging to one or the other to their network features (in particular their centrality and the one of their investors). We further investigate this positive association by introducing scalar as well as functional "success" outcomes, confirming our findings and their robustness.Source: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 744–755, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93409-5_61
Metrics:
Esposito C., Gortan M, Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.
"Success" of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize "success" by introducing progressively more refined definitions, and we find a positive association between such definitions and the centrality of a company. In particular, we are able to cluster funding trajectories of firms into two groups capturing different "success" regimes and to link the probability of belonging to one or the other to their network features (in particular their centrality and the one of their investors). We further investigate this positive association by introducing scalar as well as functional "success" outcomes, confirming our findings and their robustness.Source: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 744–755, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93409-5_61
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
From mean-field to complex topologies: network effects on the algorithmic bias model
Pansanella V., Rossetti G., Milli L.
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.Source: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 329–340, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93413-2_28
Project(s): SoBigData-PlusPlus
Metrics:
Pansanella V., Rossetti G., Milli L.
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.Source: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 329–340, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93413-2_28
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
Venture capital investments through the lens of network and functional data analysis
Esposito C., Gortan M., Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.
In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms' and investors' relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large investors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowledge bridges. Finally, based on our analyses, success is not associated with firms' and investors' spreading power (harmonic centrality), nor with the tightness of investors' community (clustering coefficient) and spreading ability (VoteRank).Source: Applied network science (2022). doi:10.1007/s41109-022-00482-y
DOI: 10.1007/s41109-022-00482-y
DOI: 10.17863/cam.85951
DOI: 10.48550/arxiv.2202.12859
Project(s): SoBigData-PlusPlus
Metrics:
Esposito C., Gortan M., Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.
In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms' and investors' relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large investors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowledge bridges. Finally, based on our analyses, success is not associated with firms' and investors' spreading power (harmonic centrality), nor with the tightness of investors' community (clustering coefficient) and spreading ability (VoteRank).Source: Applied network science (2022). doi:10.1007/s41109-022-00482-y
DOI: 10.1007/s41109-022-00482-y
DOI: 10.17863/cam.85951
DOI: 10.48550/arxiv.2202.12859
Project(s): SoBigData-PlusPlus
Metrics:
See at:
appliednetsci.springeropen.com | arXiv.org e-Print Archive | Applied Network Science | ISTI Repository | Apollo | doi.org | Apollo | CNR ExploRA
2022
Journal article
Open Access
Modeling algorithmic bias: simplicial complexes and evolving network topologies
Pansanella V., Rossetti G., Milli L.
Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content--co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-called Algorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erd?s-Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating the Adaptive Algorithmic Bias model. The second one explicitly models a peer-pressure mechanism where a majority--if there is one--can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both "close-mindedness" and algorithmic filtering.Source: Applied network science 7 (2022). doi:10.1007/s41109-022-00495-7
DOI: 10.1007/s41109-022-00495-7
Metrics:
Pansanella V., Rossetti G., Milli L.
Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content--co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-called Algorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erd?s-Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating the Adaptive Algorithmic Bias model. The second one explicitly models a peer-pressure mechanism where a majority--if there is one--can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both "close-mindedness" and algorithmic filtering.Source: Applied network science 7 (2022). doi:10.1007/s41109-022-00495-7
DOI: 10.1007/s41109-022-00495-7
Metrics:
See at:
appliednetsci.springeropen.com | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Where do migrants and natives belong in a community: a Twitter case study and privacy risk analysis
Kim J., Pratesi F., Rossetti G., Sîrbu A., Giannotti F.
Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types of social networks have been studied so far on Twitter, social networks of migrants and natives have not been studied before. This paper aims to fill this gap by studying characteristics and behaviours of migrants and natives on Twitter. To do so, we perform a general assessment of features including profiles and tweets, and an extensive network analysis on the network. We find that migrants have more followers than friends. They have also tweeted more despite that both of the groups have similar account ages. More interestingly, the assortativity scores showed that users tend to connect based on nationality more than country of residence, and this is more the case for migrants than natives. Furthermore, both natives and migrants tend to connect mostly with natives. The homophilic behaviours of users are also well reflected in the communities that we detected. Our additional privacy risk analysis showed that Twitter data can be safely used without exposing sensitive information of the users, and minimise risk of re-identification, while respecting GDPR.Source: Social Network Analysis and Mining 13 (2022). doi:10.1007/s13278-022-01017-0
DOI: 10.1007/s13278-022-01017-0
Project(s): SoBigData-PlusPlus
Metrics:
Kim J., Pratesi F., Rossetti G., Sîrbu A., Giannotti F.
Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types of social networks have been studied so far on Twitter, social networks of migrants and natives have not been studied before. This paper aims to fill this gap by studying characteristics and behaviours of migrants and natives on Twitter. To do so, we perform a general assessment of features including profiles and tweets, and an extensive network analysis on the network. We find that migrants have more followers than friends. They have also tweeted more despite that both of the groups have similar account ages. More interestingly, the assortativity scores showed that users tend to connect based on nationality more than country of residence, and this is more the case for migrants than natives. Furthermore, both natives and migrants tend to connect mostly with natives. The homophilic behaviours of users are also well reflected in the communities that we detected. Our additional privacy risk analysis showed that Twitter data can be safely used without exposing sensitive information of the users, and minimise risk of re-identification, while respecting GDPR.Source: Social Network Analysis and Mining 13 (2022). doi:10.1007/s13278-022-01017-0
DOI: 10.1007/s13278-022-01017-0
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Origin and destination attachment: study of cultural integration on Twitter
Kim J., Sirbu A., Giannotti F., Rossetti G., Rapoport H.
The cultural integration of immigrants conditions their overall socio-economic integration as well as natives' attitudes towards globalisation in general and immigration in particular. At the same time, excessive integration--or assimilation--can be detrimental in that it implies forfeiting one's ties to the origin country and eventually translates into a loss of diversity (from the viewpoint of host countries) and of global connections (from the viewpoint of both host and home countries). Cultural integration can be described using two dimensions: the preservation of links to the origin country and culture, which we call origin attachment, and the creation of new links together with the adoption of cultural traits from the new residence country, which we call destination attachment. In this paper we introduce a means to quantify these two aspects based on Twitter data. We build origin and destination attachment indices and analyse their possible determinants (e.g., language proximity, distance between countries), also in relation to Hofstede's cultural dimension scores. The results stress the importance of language: a common language between origin and destination countries favours origin attachment, as does low proficiency in the host language. Common geographical borders seem to favour both origin and destination attachment. Regarding cultural dimensions, larger differences among origin and destination countries in terms of Individualism, Masculinity and Uncertainty appear to favour destination attachment and lower origin attachment.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00363-5
DOI: 10.1140/epjds/s13688-022-00363-5
Project(s): HumMingBird, SoBigData-PlusPlus
Metrics:
Kim J., Sirbu A., Giannotti F., Rossetti G., Rapoport H.
The cultural integration of immigrants conditions their overall socio-economic integration as well as natives' attitudes towards globalisation in general and immigration in particular. At the same time, excessive integration--or assimilation--can be detrimental in that it implies forfeiting one's ties to the origin country and eventually translates into a loss of diversity (from the viewpoint of host countries) and of global connections (from the viewpoint of both host and home countries). Cultural integration can be described using two dimensions: the preservation of links to the origin country and culture, which we call origin attachment, and the creation of new links together with the adoption of cultural traits from the new residence country, which we call destination attachment. In this paper we introduce a means to quantify these two aspects based on Twitter data. We build origin and destination attachment indices and analyse their possible determinants (e.g., language proximity, distance between countries), also in relation to Hofstede's cultural dimension scores. The results stress the importance of language: a common language between origin and destination countries favours origin attachment, as does low proficiency in the host language. Common geographical borders seem to favour both origin and destination attachment. Regarding cultural dimensions, larger differences among origin and destination countries in terms of Individualism, Masculinity and Uncertainty appear to favour destination attachment and lower origin attachment.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00363-5
DOI: 10.1140/epjds/s13688-022-00363-5
Project(s): HumMingBird
, SoBigData-PlusPlus Metrics:
See at:
EPJ Data Science | epjdatascience.springeropen.com | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Delta-Conformity: multi-scale node assortativity in feature-rich stream graphs
Citraro S., Milli L., Cazabet R., Rossetti G.
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.Source: International Journal of Data Science and Analytics (Print) (2022). doi:10.1007/s41060-077-00175-4
DOI: 10.1007/s41060-077-00175-4
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Milli L., Cazabet R., Rossetti G.
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.Source: International Journal of Data Science and Analytics (Print) (2022). doi:10.1007/s41060-077-00175-4
DOI: 10.1007/s41060-077-00175-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA