Emotion recognition in Italian political language to predict positionings and crises government.

The paper aims to analyze the political language adopted on Twitter by the main Italian parties’ leaders during the first two waves of Covid-19 pandemic. A two-step model based on sentiment emotion recognition (ER) and Correspondence analysis detected which emotions characterized the political language and which changes happened between the two waves. The results showed the use of a language with a strong emotional weight for some political actors as opposed to others who used a neutral register of political language in both waves. The comparison between two waves denoted a shift from anger to sadness and fear for Meloni and a moving away Salvini by predicting through ER the rift of the right-wing

Chapter Emergency remote teaching: an explorative tool

The worldwide rapid spread and severity of the infectious disease caused by Coronavirus forced the WHO to declare a global state of pandemic emergency during March 2020, by leading the governments around the world to adopt policies that created the widest rift of education systems in human history. Italy have temporarily closed each educational institution, by causing the disruption of tertiary education for 16.89% of the Italian learner’s population. To ensure the “pedagogic continuity”, universities adopted the transitioning from traditional face-to-face to online learning. This paradigm shift to fully remote teaching solutions represents the so-called emergency remote teaching (ERT) in contrast to the traditional teaching inspired by Bologna process principles such as teaching quality and student satisfaction. In a landscape of emerging difficulties connected to ERT contexts, the quality assurance of higher education recalled by the Bologna Process may be not appropriate. We propose an evaluation model for the quality and ERT success across two dimensions used as proxy variables: students’ engagement (SE) and success performance (SP). Within the faculties, we analysed the performance and hence the knowledge, skills and/or attitudes acquired by learners, within the students, the focus was the engagement as interest, motivation and involvement. Under this perspective our research question has an explorative nature: we are interested in detecting empirical evidence about the learning assessment and engagement in higher education with focus on students’ engagement and their success performance during ERT. The investigation carried out on Iulm University’s student population (N=775). We integrated textual data related to the students evaluation of ERT and their career data such as credits, marks before and post disease. The results show the relations between the two dimensions taken into account, with a multidimensional approach we created a factorial plan useful to create an agile tool of analysis in the ERT context

La Violenza contro le donne in Europa: una strategia di Cross-Language Analysis

Il presente lavoro di tesi ha lo scopo di proporre una strategia statistica per l’analisi di linguaggi e culture differenti su uno dei più rilevanti fenomeni di violazione dei diritti umani: la violenza contro le donne. L’obiettivo è quello di evidenziare eventuali similarità e/o dissimilarità tra i registri linguistici, supponendo l’esistenza di un binomio lingua-cultura. L’utilizzo di tecniche di Text Mining ha consentito di condurre uno studio sui contenuti testuali estratti da Twitter in cinque diverse lingue europee: italiano, inglese, francese, spagnolo ed ucraino; il periodo di monitoraggio è compreso tra il primo maggio e il 31 ottobre 2019. I principali metodi e approcci della letteratura multilingue del dato testuale confrontano solo due lingue per volta tramite tecniche di traduzione automatica e l’uso di dizionari bilingue. In questo contesto invece per mantenere le peculiarità proprie di ogni lingua, è sembrato opportuno presentare un confronto multilingue diverso, ossia basato su un’unità di analisi di ordine concettuale: sono stati infatti individuati gruppi di concetti monolingue, classificando in cluster le diverse features (ovvero i termini estratti in uno spazio vettoriale multidimensionale). Nello specifico, per ottenere tali raggruppamenti si è inteso inizialmente assicurare l’orientamento semantico delle features, attraverso il calcolo della polarità semantica. L’assegnazione dei punteggi della polarità si è ottenuta utilizzando il lexicon multilingue NRC (National Research Council Canada) basando lo studio su una preliminare analisi esplorativa dei cinque corpora monolingue. Ogni collezione di dati monolingue è stata suddivisa in base alla polarità, creando così, attraverso l’utilizzo combinato di due tecniche di analisi multivariata: Analisi delle Corrispondenze e Cluster Analysis, la Concepts Extraction. Ne è derivato un raggruppamento di classi di concetti solo positivi e classi di concetti solo negativi. Più propriamente, sulle coordinate dell’Analisi delle Corrispondenze si è adottata una strategia di classificazione mista, che parte dal metodo gerarchico di Ward e si consolida con il metodo non gerarchico del k-means. Dalla Concepts Extraction si è evidenziata una chiave di lettura comune tra le lingue, rivelando un dominio semantico simile.Tuttavia, sebbene dall’Analisi Multivariata siano stati individuati raggruppamenti concettuali simili, si è ritenuto interessante evidenziare ulteriormente se i concetti individuati nei diversi linguaggi avessero relazioni simili tra loro. Nello specifico, si è voluta condurre un’Analisi Procustiana Generalizzata (GPA) per comparare simultaneamente in unico piano fattoriale le cinque configurazioni dei concetti e dare quindi un risultato di consensus o accordo tra le lingue. I risultati della GPA, derivanti dallo studio della rappresentazione fattoriale e dell’Analisi della Varianza (PANOVA) hanno permesso di cogliere similarità o dissimilarità tra le lingue ed individuare quindi quali concetti mettano più d’accordo l’opinione pubblica internazionale riguardo al tema preso in esame. In definitiva, la proiezione fattoriale dei concetti in un comune spazio multidimensionale cross-language e l’Analisi della Varianza hanno evidenziato un maggiore accordo sui concetti della misoginia, della discriminazione, del garantismo e dell’inefficacia giuridica, mentre hanno espresso un minore consenso in relazione all’attivismo, alla cooperazione e alla strumentalizzazione dei Social Media. La propensione alla semantica negativa accomuna in maggior misura le lingue indagate sul fenomeno della violenza contro le donne. Al contrario, i concetti positivi restituiscono il maggiore disaccordo. È altresì emerso che l’italiano e l’inglese sono più simili tra loro nelle relazioni tra i concetti mentre la lingua ucraina è risultata la più dissimile nel confronto multilingue. La proposta di analisi cross-language adottata si è rivelata particolarmente utile a definire un contesto europeo poco diversificato in termini di opinione pubblica sul fenomeno della violenza contro le donne, suggerendo una chiara e comune consapevolezza del problema e dei rischi. Al contempo, ha evidenziato invece una differente percezione circa la fiducia nelle attività di cooperazione e di azione sociale finalizzate alla trasformazione socio-culturale

Social Network to analyse the relationship between ‘victim-author’ and ‘motivation’ of violence against women in Italy.

The paper aims to analyse the phenomenon of Violence against women in the Italian context during 2020. It proposes to study the relationship between ‘victim-author’ and ‘motivation’ in femicides committed in domestic environment. By means of the properties of the Social Network Analysis on bimodal data, the study detected main actors and motivations that generated the homicides with female victims. At the same time, the structural relationships allowed to investigate the existence of motivations that better characterized the action of the various actors. The bipartite graph visualization and centrality scores calculated have demonstrated the effectiveness of the methodology for the pursued objectives

CSR & Sentiment Analysis: a new customized dictionary

Communication concerning the CSR pillars is key to sustainable corporate development. Sentiment analysis (SA) is a sub-area of natural language processing for studying communication through the classification of negative or positive opinions. Measuring sentiment is characterized by pitfalls related to: a) the context, where the polarity classification depends on the domain; b) the methods, if lexicon-based, machine learning, or their combination; c) the language, where the lack of resources (different from English) in literature was observed. Strategic communication based on CSR has no domain resources for investigating sentiment, neither in English nor in other languages. Our contribution is placed within the methodological setting of SA for the sustainability framework. We combined lexicon-based methods with machine-learning ones to build a customized lexicon for analyzing the CSR. The innovation concerns: 1) a domain corpus-based approach for improving a general pre-constructed dictionary; 2) the application for Italian; and 3) the performance assessment through machine learning. We developed an algorithm characterized by a multi-stage model that combines text analysis with network analysis and captures semantic concordances through an index of keyword content in the text. To validate our model from a machine learning perspective, we divided our data collection into five random samples: one sample was utilized as a train set or baseline for the lexicon’s implementation, and four were used as test sets. The study showed a notable increase in performance metrics across all samples, demonstrating the effectiveness of our proposal in building a customized lexicon for analyzing CSR in the Italian context

A Proposal for Cross-language Analysis: violence against women and the Web.

Aim of the paper is investigating the mood on the Web with respect to one of the most relevant Human Rights violation, without any geographic distinction: the violence against women. While the literature that studies the phenomenon is rapidly growing, the action field is still fragile and the question marks are about the relationship between the public opinion and the contextual factors. In a first look at the phenomenon, we aim at mapping gender violence on the Web, in a Big Data perspective. The peculiar problem we deal with consists in analysing short documents (tweets) written in six European different languages, in the occasion of a common event: the International Day for the Elimination of Violence against Women, 25 November 2017. For our statistical analysis, we choose a multi-linguistic, cross-national perspective. The basic idea is that there are some common structures, language independent ("concepts"), which are declined in the different national natural language expressions ("terms"). Investigating those structure (e.g. factors of lexical correspondence analyses separately performed on the different collections), enables a double level analysis trying to understand and visualise national peculiarities and communalities. The statistical tool is given by Procrustes rotations. Keywords: Big Data, Text Mining, Cross-national study, Procrustes rotation

Data Quality and Violence Against Women: The Causes and Actors of Femicide

The paper examines domestic ’femicide’ in Italy. Under an exploratory statistical approach, we investigated: (1) difficulties and strategies for reconstructing a historical dataset on fam- ily crimes for studies over time; (2) the main causes of family femicides; and (3) groups of actors driven by the same motivations interpreted as patterns of criminal behavior. First, we integrated and systematised data from official sources to guarantee comparison over time; second, we used Social Network Analysis properties to study the relationships between ’motivations’ and ’victim-perpetrator’; and third, we applied and compared com- munity detection algorithms to the linkages between ’actors’ and ’motivations’ to detect groups of criminal behavior. From 2015 to 2020 in Italy, the cohabitant was the major fam- ily murderer, but in 2020, passion motivation also surfaced. Mental problems connected to parents-children and cohabitants, jealousy of ex-partners or rivals, and economic issues for blood relations were observed in 2015. Psychopathologies and money characterised par- ents-children in 2020, while passion and disagreements caused cohabitants or ex-partners