TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization

In this contribution we describe the system (i.e. a statistical model) used to participate in Evalita conference 2020, SardiStance (Tasks A and B) and Haspeede2 (Tasks A and B). We first developed a classifier by extracting features from the texts and the social network of users. Then, we fit the data through an extreme gradient boosting, with cross-validation tuning of the hyper-parameters. A key factor for a good performance in SardiStance Task B was the features extraction by using Multidimensional Scaling of the distance matrix (minimum path, undirected graph) applied on each network. The second system exploits the same features above, but it trains and performs predictions in two-steps. The performances proved to be lower than those of the single-step model

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization

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