@inproceedings{tonini-etal-2024-counter,
title = "How Do We Counter Hate Speech in {I}taly?",
author = "Tonini, Vittoria and
Frenda, Simona and
Stranisci, Marco Antonio and
Patti, Viviana",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.103/",
pages = "955--966",
ISBN = "979-12-210-7060-6",
abstract = "The phenomenon of online hate speech is a growing challenge and various organisations try to prevent its spread answering promptly to hateful messages online. In this context, we propose a new dataset of activists' and users' comments on Facebook reacting to specific news headlines: AmnestyCounterHS. Taking into account the literature on counterspeech, we defined a new schema of annotation and applied it to our dataset, in order to examine the most used counter-narrative strategies in Italy. This research aims to support the future development of automatic counterspeech generation. This paper presents also a comparative analysis of our dataset with other two datasets in Italian (Counter-TWIT and multilingual CONAN) containing hate speech and counter narratives. Through this analysis, we will understand how the environment (artificial vs. ecological) and the topics of discussions online influence the nature of counter narratives. Our findings highlight the predominance of negative sentiment and emotions, the varying presence of stereotypes, and the strategic differences in counter narratives across dataset."
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<abstract>The phenomenon of online hate speech is a growing challenge and various organisations try to prevent its spread answering promptly to hateful messages online. In this context, we propose a new dataset of activists’ and users’ comments on Facebook reacting to specific news headlines: AmnestyCounterHS. Taking into account the literature on counterspeech, we defined a new schema of annotation and applied it to our dataset, in order to examine the most used counter-narrative strategies in Italy. This research aims to support the future development of automatic counterspeech generation. This paper presents also a comparative analysis of our dataset with other two datasets in Italian (Counter-TWIT and multilingual CONAN) containing hate speech and counter narratives. Through this analysis, we will understand how the environment (artificial vs. ecological) and the topics of discussions online influence the nature of counter narratives. Our findings highlight the predominance of negative sentiment and emotions, the varying presence of stereotypes, and the strategic differences in counter narratives across dataset.</abstract>
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%0 Conference Proceedings
%T How Do We Counter Hate Speech in Italy?
%A Tonini, Vittoria
%A Frenda, Simona
%A Stranisci, Marco Antonio
%A Patti, Viviana
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F tonini-etal-2024-counter
%X The phenomenon of online hate speech is a growing challenge and various organisations try to prevent its spread answering promptly to hateful messages online. In this context, we propose a new dataset of activists’ and users’ comments on Facebook reacting to specific news headlines: AmnestyCounterHS. Taking into account the literature on counterspeech, we defined a new schema of annotation and applied it to our dataset, in order to examine the most used counter-narrative strategies in Italy. This research aims to support the future development of automatic counterspeech generation. This paper presents also a comparative analysis of our dataset with other two datasets in Italian (Counter-TWIT and multilingual CONAN) containing hate speech and counter narratives. Through this analysis, we will understand how the environment (artificial vs. ecological) and the topics of discussions online influence the nature of counter narratives. Our findings highlight the predominance of negative sentiment and emotions, the varying presence of stereotypes, and the strategic differences in counter narratives across dataset.
%U https://aclanthology.org/2024.clicit-1.103/
%P 955-966
Markdown (Informal)
[How Do We Counter Hate Speech in Italy?](https://aclanthology.org/2024.clicit-1.103/) (Tonini et al., CLiC-it 2024)
ACL
- Vittoria Tonini, Simona Frenda, Marco Antonio Stranisci, and Viviana Patti. 2024. How Do We Counter Hate Speech in Italy?. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 955–966, Pisa, Italy. CEUR Workshop Proceedings.