In this contribution we describe the system(i.e. a statistical model) used to participatein Evalita conference 2020, SardiStance(Tasks A and B) and Haspeede2 (TasksA and B). We first developed a classifierby extracting features from the texts andthe social network of users. Then, wefit the data through an extreme gradientboosting, with cross-validation tuning ofthe hyper-parameters. A key factor for agood performance in SardiStance Task Bwas the features extraction by using Mul-tidimensional Scaling of the distance ma-trix (minimum path, undirected graph) ap-plied on each network. The second sys-tem exploits the same features above, butit trains and performs predictions in two-steps.The performances proved to belower than those of the single-step model.
TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization / Ferraccioli, Federico; Sciandra, Andrea; Da Pont, Mattia; Girardi, Paolo; Solari and Livio Finos, Dario. - 2765:(2020). (Intervento presentato al convegno 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2020 tenutosi a Online event nel December 17th, 2020).
TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization
Andrea Sciandra;
2020
Abstract
In this contribution we describe the system(i.e. a statistical model) used to participatein Evalita conference 2020, SardiStance(Tasks A and B) and Haspeede2 (TasksA and B). We first developed a classifierby extracting features from the texts andthe social network of users. Then, wefit the data through an extreme gradientboosting, with cross-validation tuning ofthe hyper-parameters. A key factor for agood performance in SardiStance Task Bwas the features extraction by using Mul-tidimensional Scaling of the distance ma-trix (minimum path, undirected graph) ap-plied on each network. The second sys-tem exploits the same features above, butit trains and performs predictions in two-steps.The performances proved to belower than those of the single-step model.
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