@article {1483,
	title = {Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study},
	journal = {Applied Sciences},
	volume = {11},
	number = {12},
	year = {2021},
	pages = {5390},
	author = {Morini, Virginia and Pollacci, Laura and Giulio Rossetti}
}
@conference {1371,
	title = {Capturing Political Polarization of Reddit Submissions in the Trump Era},
	booktitle = {SEBD},
	year = {2020},
	author = {Giulio Rossetti and Morini, Virginia and Pollacci, Laura}
}
@article {1404,
	title = {Human migration: the big data perspective},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	month = {2020/03/23},
	pages = {1{\textendash}20},
	abstract = {How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.},
	isbn = {2364-4168},
	doi = {https://doi.org/10.1007/s41060-020-00213-5},
	url = {https://link.springer.com/article/10.1007\%2Fs41060-020-00213-5},
	author = {Alina Sirbu and Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Conti, Marco and Fosca Giannotti and Riccardo Guidotti and Bertoli, Simone and Jisu Kim and Muntean, Cristina Ioana and Luca Pappalardo and Passarella, Andrea and Dino Pedreschi and Pollacci, Laura and Francesca Pratesi and Sharma, Rajesh}
}
@conference {1039,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a {\textquotedblleft}fractal{\textquotedblright} musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians{\textquoteright} popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.},
	doi = {10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {1133,
	title = {The italian music superdiversity},
	journal = {Multimedia Tools and Applications},
	year = {2018},
	pages = {1{\textendash}23},
	abstract = {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{\textquoteright} melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.},
	doi = {10.1007/s11042-018-6511-6},
	url = {https://link.springer.com/article/10.1007/s11042-018-6511-6},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {1129,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a {\textquotedblleft}fractal{\textquotedblright} musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians{\textquoteright} popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

},
	doi = {https://doi.org/10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {1050,
	title = {Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter},
	booktitle = {Conference of the Italian Association for Artificial Intelligence},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-319-70169-1_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-70169-1_9},
	author = {Pollacci, Laura and Alina Sirbu and Fosca Giannotti and Dino Pedreschi and Claudio Lucchese and Muntean, Cristina Ioana}
}
@proceedings {874,
	title = {{\textquotedblleft}Are we playing like Music-Stars?{\textquotedblright} Placing Emerging Artists on the Italian Music Scene},
	year = {2016},
	month = {09/2016},
	address = {Riva del Garda},
	abstract = {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 deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti}
}
@proceedings {825,
	title = {ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon},
	volume = {II},
	year = {2015},
	abstract = {In  recent  years  computational  linguistics  has  seen  a  rising  interest  in subjectivity,  opinions,  feelings and  emotions.  Even  though  great attention  has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper,  we  present  a  methodology  to  build emotive   lexicons   by  jointly   exploiting vector  space  models  and  human  annotation,  and  we  provide  the  first  results  of the  evaluation  with  a  crowdsourcing  experiment.},
	isbn = {978-88-99200-62-6},
	author = {Lucia Passaro and Pollacci, Laura and Lenci, Alessandro}
}
@proceedings {793,
	title = {The CoLing Lab system for Sentiment Polarity Classification of tweets},
	volume = {II},
	year = {2014},
	author = {Lucia Passaro and Lebani, Gianluca E and Pollacci, Laura and Chersoni, Emmanuele and Lenci, Alessandro}
}
