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2016
Conference article
Open Access
"Are we playing like Music-Stars?" Placing emerging artists on the Italian music scene
Pollacci L., Guidotti R., Rossetti G.
The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a study of the Italian music scene and how the new generation of musicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional music and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results obtained are a first attempt to outline rules suggesting the importance of those features needed to increase popularity in the Italian music scene.Source: 9th International Workshop on Machine Learning and Music, pp. 51–55, Riva del Garda, Italy, 23 September 2016
Pollacci L., Guidotti R., Rossetti G.
The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a study of the Italian music scene and how the new generation of musicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional music and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results obtained are a first attempt to outline rules suggesting the importance of those features needed to increase popularity in the Italian music scene.Source: 9th International Workshop on Machine Learning and Music, pp. 51–55, Riva del Garda, Italy, 23 September 2016
See at:
sites.google.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
2017
Conference article
Restricted
Sentiment spreading: an epidemic model for lexicon-based sentiment analysis on Twitter
Pollacci L., Sirbu A., Giannotti F., Pedreschi D., Lucchese C., Muntean C. I.
While sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.Source: AI*IA Conference of the Italian Association for Artificial Intelligence, pp. 114–127, Bari, Italy, 14-17 November 2017
DOI: 10.1007/978-3-319-70169-1_9
Project(s): SoBigData
Metrics:
Pollacci L., Sirbu A., Giannotti F., Pedreschi D., Lucchese C., Muntean C. I.
While sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.Source: AI*IA Conference of the Italian Association for Artificial Intelligence, pp. 114–127, Bari, Italy, 14-17 November 2017
DOI: 10.1007/978-3-319-70169-1_9
Project(s): SoBigData
Metrics:
See at:
Lecture Notes in Computer Science 
| Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari | link.springer.com | CNR ExploRA
2019
Journal article
Open Access
The italian music superdiversity. Geography, emotion and language: one resource to find them, one resource to rule them all
Pollacci L., Guidotti R., Rossetti G., Giannotti F., Pedreschi D.
Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs' melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.Source: Multimedia tools and applications (Dordrecht. Online) 78 (2019): 3297–3319. doi:10.1007/s11042-018-6511-6
DOI: 10.1007/s11042-018-6511-6
Project(s): SoBigData
Metrics:
Pollacci L., Guidotti R., Rossetti G., Giannotti F., Pedreschi D.
Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs' melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.Source: Multimedia tools and applications (Dordrecht. Online) 78 (2019): 3297–3319. doi:10.1007/s11042-018-6511-6
DOI: 10.1007/s11042-018-6511-6
Project(s): SoBigData
Metrics:
See at:
Multimedia Tools and Applications | Archivio della Ricerca - Università di Pisa | ISTI Repository | Multimedia Tools and Applications | link.springer.com | CNR ExploRA
2020
Conference article
Open Access
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
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
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA