This paper presents the PoliticIT 2023 shared task, organised at EVALITA 2023 workshop. The task aims to extract politicians’ ideology information from a set of tweets in Italian framed as a binary and a multiclass classification. The task is designed to be privacy-preserving and it is accompanied by a subtask targeting the identification of self-assigned gender as a demographic trait. The PoliticIT task attracted 7 teams that registered for the task, submitted results and presented working notes describing their systems. Most of the teams proposed transformer-based approaches, while some of them also used traditional machine learning algorithms or even a combination of both.

PoliticIT at EVALITA 2023: Overview of the Political Ideology Detection in Italian Texts Task

Daniel Russo
;
Marco Guerini
;
2023-01-01

Abstract

This paper presents the PoliticIT 2023 shared task, organised at EVALITA 2023 workshop. The task aims to extract politicians’ ideology information from a set of tweets in Italian framed as a binary and a multiclass classification. The task is designed to be privacy-preserving and it is accompanied by a subtask targeting the identification of self-assigned gender as a demographic trait. The PoliticIT task attracted 7 teams that registered for the task, submitted results and presented working notes describing their systems. Most of the teams proposed transformer-based approaches, while some of them also used traditional machine learning algorithms or even a combination of both.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/341647
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