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 / Russo, D.; Jimenez-Zafra, S. M.; Garcia-Diaz, J. A.; Caselli, T.; Guerini, M.; Alfonso Urena-Lopez, L.; Valencia-Garcia, R.. - 3473:(2023). (Intervento presentato al convegno 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023 tenutosi a Parma, Italy nel 7–8 September 2023).
PoliticIT at EVALITA 2023: Overview of the Political Ideology Detection in Italian Texts Task
Russo D.;Guerini M.;
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
