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2021 Conference article Restricted

Opinion Dynamic Modeling of Fake News Perception
Toccaceli C., Milli L., Rossetti G.
Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several points of view, primarily to improve our ability to separate them from the legit ones as well as identify their sources. Among such vast literature, a rarely discussed theme is likely to play uttermost importance in our understanding of such a controversial phenomenon: the analysis of fake news' perception. In this work, we approach such a problem by proposing a family of opinion dynamic models tailored to study how specific social interaction patterns concur to the acceptance, or refusal, of fake news by a population of interacting individuals. To discuss the peculiarities of the proposed models, we tested them on several synthetic network topologies, thus underlying when/how they affect the stable states reached by the performed simulations.Source: Complex Networks 2020 - Ninth International Conference on Complex Networks and Their Applications, pp. 370–381, Online Conference, 20/12/2020
DOI: 10.1007/978-3-030-65347-7_31
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: academic.microsoft.com Restricted | link.springer.com Restricted | link.springer.com | CNR ExploRA


2021 Journal article Open Access OPEN

Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Unveiling the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works have underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Different from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
DOI: 10.1109/mis.2021.3051291
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | ieeexplore.ieee.org Open Access | IEEE Intelligent Systems Open Access | CNR ExploRA Open Access | IEEE Intelligent Systems Restricted | IEEE Intelligent Systems Restricted | IEEE Intelligent Systems Restricted | IEEE Intelligent Systems Restricted


2021 Journal article Open Access OPEN

UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Rossetti G., Milli L., Citraro S., Morini V.
Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate "what if" epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns...). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.Source: Journal of intelligent information systems (2021). doi:10.1007/s10844-021-00649-6
DOI: 10.1007/s10844-021-00649-6
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Journal article Open Access OPEN

Toward a standard approach for echo chamber detection: Reddit case study
Morini V., Pollacci L., Rossetti G.
Featured Application: The framework proposed in this paper could be used to detect echo chambers in a standard way across multiple online social networks (i.e., leveraging features they commonly share). Such application then allows for comparative analysis between different platforms, thus discovering if some are more polarized than others. Further, a standard echo chamber characterization could be a starting point for designing a Recommendation System able to recognize and mitigate such a phenomenon. Abstract: In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users' ideology on the controversy; (iii) the construction of users' debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump's presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.Source: Applied sciences 11 (2021). doi:10.3390/app11125390
DOI: 10.3390/app11125390
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.mdpi.com Open Access


2021 Journal article Open Access OPEN

Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on realtime epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087
DOI: 10.1371/journal.pcbi.1009087

See at: journals.plos.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Contribution to conference Open Access OPEN

Sockpuppet detection: a Telegram case study
Pisciotta G., Somenzi M., Barisani E., Rossetti G.
In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users. In literature this problem is known as the Sockpuppet problem. In our work we focus on Telegram, a wide-spread instant messaging application, often known for its exploitation by members of organized crime and terrorism, and more in general for its high presence of people who have offensive behaviors.Source: Complex Networks 2020 - The 9th International Conference on Complex Networks and their Applications, Online conference, 1-3/12/2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Conference article Open Access OPEN

Eva: Attribute-Aware Network Segmentation
Citraro S., Rossetti G.
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbona, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_12
Project(s): SoBigData via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | arxiv.org Open Access | link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | rd.springer.com Restricted | ui.adsabs.harvard.edu Restricted


2020 Conference article Open Access OPEN

Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery
Rossetti G.
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: International Conference on Complex Networks and their Applications, pp. 152–163, Lisbona, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_13
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | rd.springer.com Restricted


2020 Conference article Open Access OPEN

Community-Aware Content Diffusion: Embeddednes and Permeability
Milli L., Rossetti G.
Viruses, opinions, ideas are different contents sharing a common trait: they need carriers embedded into a social context to spread. Modeling and approximating diffusive phenomena have always played an essential role in a varied range of applications from outbreak prevention to the analysis of meme and fake news. Classical approaches to such a task assume diffusion processes unfolding in a mean-field context, every actor being able to interact with all its peers. However, during the last decade, such an assumption has been progressively superseded by the availability of data modeling the real social network of individuals, thus producing a more reliable proxy for social interactions as spreading vehicles. In this work, following such a trend, we propose alternative ways of leveraging apriori knowledge on mesoscale network topology to design community-aware diffusion models with the aim of better approximate the spreading of content over complex and clustered social tissues.Source: International Conference on Complex Networks and their Applications, pp. 362–371, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_30
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | rd.springer.com Restricted


2020 Report Open Access OPEN

Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020, 2020
DOI: 10.32079/isti-tr-2020/005

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Journal article Open Access OPEN

ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks
Rossetti G.
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00270-6
DOI: 10.1007/s41109-020-00270-6

See at: Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
Cintia P., Fadda D., Giannotti F., Pappalardo L., Rossetti G., Pedreschi D., Rinzivillo S., Bonato P., Fabbri F., Penone F., Bavarese M., Checchi D., Chiaromonte F., Vineis P., Gazzetta G., Riccardo F., Marziano V., Poletti P., Trentini F., Bella A., Xanthi A., Del Manso M., Fabiani M., Bellino S., Boros S., Urdiales A. M., Vescia M. F., Brusaferro S., Rezza G., Pezzotti P., Ajelli M., Merler S.
We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.Project(s): SoBigData via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Master thesis Unknown

Modeling Human Mobility considering Spatial, Temporal and Social Dimensions
Cornacchia G.
The analysis of human mobility is crucial in several areas, from urban planning to epidemic modeling, estimation of migratory flows and traffic forecasting.However, mobility data (e.g., Call Detail Records and GPS traces from vehicles or smartphones) are sensitive since it is possible to infer personal information even from anonymized datasets.A solution to dealing with this privacy issue is to use synthetic and realistic trajectories generated by proper generative models.Existing mechanistic generative models usually consider the spatial and temporal dimensions only. In this thesis, we select as a baseline model GeoSim, which considers the social dimension together with spatial and temporal dimensions during the generation of the synthetic trajectories.Our contribution in the field of the human mobility consists of including, incrementally, three mobility mechanisms, specifically the introduction of the distance and the use of a gravity-model in the location selection phase, finally, we include a diary generator, an algorithm capable to capture the tendency of humans to follow or break their routine, improving the modeling capability of the GeoSim model.
We show that the three implemented models, obtained from GeoSim with the introduction of the mobility mechanisms, can reproduce the statistical proprieties of real trajectories, in all the three dimensions, more accurately than GeoSim.
Project(s): SoBigData via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Report Restricted

UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Rossetti G., Milli L., Citraro S., Morini V.
Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing a constantly growing interest from researchers of heterogeneous fields of study. Indeed, the vast literature on computational epidemiology offers solid grounds for analytical studies and the definition of novel models aimed at both predictive and prescriptive scenario descriptions. To ease the access to diffusion modelling, several programming libraries and tools have been proposed during the last decade: however, to the best of our knowledge, none of them is explicitly designed to allow its users to integrate public interventions in their model. In this work, we introduce UTLDR, a framework that can simulate the effects of several public interventions (and their combinations) on the unfolding of epidemic processes. UTLDR enables the design of compartmental models incrementally and to simulate them over complex interaction network topologies. Moreover, it allows integrating external information on the analyzed population (e.g., age, gender, geographical allocation, and mobility patterns. . . ) and to use it to stratify and refine the designed model. After introducing the framework, we provide a few case studies to underline its flexibility and expressive power.Source: ISTI Working Papers, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: CNR ExploRA Restricted


2020 Report Open Access OPEN

Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: ISTI Working Papers, 2020, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: ISTI Technical Reports 2020/009, 2020, 2020
DOI: 10.32079/isti-tr-2020/009
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

Evaluating Community Detection Algorithms for Progressively Evolving Graphs
Cazabet R., Boudebza S., Rossetti G.
Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification, and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.Source: ISTI Technical Reports 2020/013, 2020, 2020
DOI: 10.32079/isti-tr-2020/013
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Journal article Open Access OPEN

Identifying and exploiting homogeneous communities in labeled networks
Citraro S., Rossetti G.
Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate Eva on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that Eva is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better Eva: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00302-1
DOI: 10.1007/s41109-020-00302-1
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Conference article Open Access OPEN

Capturing Political Polarization of Reddit Submissions in the Trump Era
Morini V., Pollacci L., Rossetti G.
The American political situation of the last years, combined with the incredible growth of Social Networks, led to the diffusion of political polarization's phenomenon online. Our work presents a model that attempts to measure the political polarization of Reddit submissions during the first half of Donald Trump's presidency. To do so, we design a text classification task: Political polarization of submissions is assessed by quantifying those who align themselves with pro-Trump ideologies and vice versa. We build our ground truth by picking submissions from subreddits known to be strongly polarized. Then, for model selection, we use a Neural Network with word embeddings and Long Short Time Memory layer and, finally, we analyze how model performances change trying different hyper-parameters and types of embeddings.Source: SEBD 2020 28th SYMPOSIUM ON ADVANCED DATABASE SYSTEMS, pp. 80–87, Villasimius, Sardinia, Italy, June 21-24, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Contribution to book Open Access OPEN

"Know thyself" how personal music tastes shape the last.fm online social network
Guidotti R., Rossetti G.
As Nietzsche once wrote "Without music, life would be a mistake" (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.Source: Formal Methods. FM 2019 International Workshops Porto, Portugal, October 7-11, 2019, Revised Selected Papers, Part I, edited by Sekerinski E. et al., pp. 146–161, 2020
DOI: 10.1007/978-3-030-54994-7_11
Project(s): Track and Know via OpenAIRE, SoBigData via OpenAIRE

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted