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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.