Year: 2019
Type: conference-paper
Abstract: Taking as a case study the Hate Speech Detection task at EVALITA 2018, the paper discusses the distribution and typology of the errors made by the five bestscoring systems.The focus is on the subtask where Twitter data was used both for training and testing (HaSpeeDe-TW).In order to highlight the complexity of hate speech and the reasons beyond the failures in its automatic detection, the annotation provided for the task is enriched with orthogonal categories annotated in the original reference corpus, such as aggressiveness, offensiveness, irony and the presence of stereotypes.
Authors: Chiara Francesconi, Cristina Bosco, Fabio Poletto, Manuela Sanguinetti
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Fondazione CRT, Fondazione Giovanni Goria
OpenAlex ID: W2990563486