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169 results on '"Fontanella, Lara"'

2. Chapter An experimental annotation task to investigate annotators’ subjectivity in a Misogyny dataset

Author

Tontodimamma, Alice, Ignazzi, Elisa, Anzani, Stefano, Stranisci, Marco, Basile, Valerio, and FONTANELLA, Lara

Subjects
subjectivity, misogyny, disagreement, social bias, bic Book Industry Communication::J Society & social sciences
Abstract

In recent years, hatred directed against women has spread exponentially, especially in online social media. Although this alarming phenomenon has given rise to many studies both from the viewpoint of computational linguistics and from that of machine learning, less effort has been devoted to analysing whether models for the detection of misogyny are affected by bias. An emerging topic that challenges traditional approaches for the creation of corpora is the presence of social bias in natural language processing (NLP). Many NLP tasks are subjective, in the sense that a variety of valid beliefs exist about what the correct data labels should be; some tasks, for example misogyny detection, are highly subjective, as different people have very different views about what should or should not be labelled as misogynous. An increasing number of scholars have proposed strategies for assessing the subjectivity of annotators, in order to reduce bias both in computational resources and in NLP models. In this work, we present two corpora: a corpus of messages posted on Twitter after the liberation of Silvia Romano on the 9th of May, 2020 and corpus of comments constructed starting from posts on Facebook that contained misogyny, developed through an experimental annotation task, to explore annotators’ subjectivity. For a given comment, the annotation procedure consists in selecting one or more chunk from each text that is regarded as misogynistic and establishing whether a gender stereotype is present. Each comment is annotated by at least three annotators in order to better analyse their subjectivity. The annotation process was carried by trainees who are engaged in an internship program. We propose a qualitative-quantitative analysis of the resulting corpus, which may include non-harmonised annotations.

Published
2023
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9. What Your Face Says: How Signals of Communion and Agency Inform First Impressions and Behavioural Intentions.

Author

Aquino, Antonio, Fontanella, Lara, Walker, Mirella, Haddock, Geoffrey, and Alparone, Francesca Romana

Abstract

ABSTRACT Two studies investigated how facial signals of communion and agency intersect to shape first impressions. Study 1 primarily investigated how enhancing or reducing facial signals of communion or agency affected impressions of these dimensions. Results demonstrated that the manipulation of facial signals of communion affected impressions of both communion and agency, whereas the manipulation of facial signals of agency affected only impressions of agency. Secondly, data from Study 1 were used to categorize the Basel Face Database stimuli into the four cells of the stereotype content model (SCM). Study 2 investigated how combined facial signals of communion and agency impact individuals’ evaluations, emotions and behavioural intentions towards targets. Results showed that communion signals in faces have a stronger impact on overall evaluations and behavioural intentions compared to agency signals. Furthermore, in line with the SCM, specific combinations of communion and agency evoked distinct emotions: admiration (high communion–high agency), pity (high communion–low agency) and contempt (low communion–low agency). Together, the research emphasizes the primary role of communion in social perception, highlighting theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Facebook Debate on Sea Watch 3 Case: Detecting Offensive Language Through Automatic Topic Mining Techniques

Author

Tontodimamma, Alice, del Gobbo, Emiliano, Russo, Vanessa, Sarra, Annalina, Fontanella, Lara, Gaul, Wolfgang, Managing Editor, Vichi, Maurizio, Managing Editor, Weihs, Claus, Managing Editor, Baier, Daniel, Editorial Board Member, Critchley, Frank, Editorial Board Member, Decker, Reinhold, Editorial Board Member, Diday, Edwin, Editorial Board Member, Greenacre, Michael, Editorial Board Member, Lauro, Carlo Natale, Editorial Board Member, Meulman, Jacqueline, Editorial Board Member, Monari, Paola, Editorial Board Member, Nishisato, Shizuhiko, Editorial Board Member, Ohsumi, Noboru, Editorial Board Member, Opitz, Otto, Editorial Board Member, Ritter, Gunter, Editorial Board Member, Schader, Martin, Editorial Board Member, Mariani, Paolo, editor, and Zenga, Mariangela, editor

Published
2021
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17. Modeling US housing prices by spatial dynamic structural equation models

Author

Valentini, Pasquale, Ippoliti, Luigi, and Fontanella, Lara

Subjects
Statistics - Applications
Abstract

This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity "regions" that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields, while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for dynamic linear models to our model formulation. The fit of the model is discussed for a data set of 48 States for which we model the relationship between housing prices and the macroeconomy, using State level unemployment and per capita personal income., Comment: Published in at http://dx.doi.org/10.1214/12-AOAS613 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2013
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20. Basic Concepts and Definitions

Author

Brombin, Chiara, Salmaso, Luigi, Fontanella, Lara, Ippoliti, Luigi, Fusilli, Caterina, Brombin, Chiara, Salmaso, Luigi, Fontanella, Lara, Ippoliti, Luigi, and Fusilli, Caterina

Published
2016
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22. Applications of NPC Methodology

Author

Brombin, Chiara, Salmaso, Luigi, Fontanella, Lara, Ippoliti, Luigi, Fusilli, Caterina, Brombin, Chiara, Salmaso, Luigi, Fontanella, Lara, Ippoliti, Luigi, and Fusilli, Caterina

Published
2016
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23. Data science skills for the next generation of statisticians

Author

Antonucci, Laura, primary, Balzanella, Antonio, additional, Bruno, Elvira, additional, Crocetta, Crocetta, additional, Zio, Simone Di, additional, Fontanella, Lara, additional, Sanarico, Maurizio, additional, Scarpa, Bruno, additional, Verde, Rosanna, additional, and Vittadini, Giorgio, additional

Published
2023
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