@inproceedings{russo-etal-2024-click,
title = "To Click It or Not to Click It: An {I}talian Dataset for Neutralising Clickbait Headlines",
author = "Russo, Daniel and
Araque, Oscar and
Guerini, Marco",
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-1.90/",
pages = "829--841",
ISBN = "979-12-210-7060-6",
abstract = "Clickbait is a common technique aimed to attract reader`s attention, although it can result inaccurate and lead to misinformation. This work explores the role of current Natural Language Processing methods to reduce its negative impact. To do so, a novel Italian dataset is generated, containing manual annotations for classification, spoiling, and neutralisation of clickbait. Besides, several experimental evaluations are performed, assessing the performance of current language models. On the one hand, we evaluate the performance in the task of clickbait detection in a multilingual setting, showing that augmenting the data with English instance largely improves overall performance. On the other hand, the generation tasks of clickbait spoiling and neutralisation are explored. The latter is a novel task that is designed to increase the informativeness of a headline, thus removing the information gap. This work opens a new research avenue that has been largely uncharted in the Italian language."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="russo-etal-2024-click">
<titleInfo>
<title>To Click It or Not to Click It: An Italian Dataset for Neutralising Clickbait Headlines</title>
</titleInfo>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Russo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oscar</namePart>
<namePart type="family">Araque</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="family">Guerini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Felice</namePart>
<namePart type="family">Dell’Orletta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simonetta</namePart>
<namePart type="family">Montemagni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rachele</namePart>
<namePart type="family">Sprugnoli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>CEUR Workshop Proceedings</publisher>
<place>
<placeTerm type="text">Pisa, Italy</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-12-210-7060-6</identifier>
</relatedItem>
<abstract>Clickbait is a common technique aimed to attract reader‘s attention, although it can result inaccurate and lead to misinformation. This work explores the role of current Natural Language Processing methods to reduce its negative impact. To do so, a novel Italian dataset is generated, containing manual annotations for classification, spoiling, and neutralisation of clickbait. Besides, several experimental evaluations are performed, assessing the performance of current language models. On the one hand, we evaluate the performance in the task of clickbait detection in a multilingual setting, showing that augmenting the data with English instance largely improves overall performance. On the other hand, the generation tasks of clickbait spoiling and neutralisation are explored. The latter is a novel task that is designed to increase the informativeness of a headline, thus removing the information gap. This work opens a new research avenue that has been largely uncharted in the Italian language.</abstract>
<identifier type="citekey">russo-etal-2024-click</identifier>
<location>
<url>https://aclanthology.org/2024.clicit-1.90/</url>
</location>
<part>
<date>2024-12</date>
<extent unit="page">
<start>829</start>
<end>841</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T To Click It or Not to Click It: An Italian Dataset for Neutralising Clickbait Headlines
%A Russo, Daniel
%A Araque, Oscar
%A Guerini, Marco
%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 russo-etal-2024-click
%X Clickbait is a common technique aimed to attract reader‘s attention, although it can result inaccurate and lead to misinformation. This work explores the role of current Natural Language Processing methods to reduce its negative impact. To do so, a novel Italian dataset is generated, containing manual annotations for classification, spoiling, and neutralisation of clickbait. Besides, several experimental evaluations are performed, assessing the performance of current language models. On the one hand, we evaluate the performance in the task of clickbait detection in a multilingual setting, showing that augmenting the data with English instance largely improves overall performance. On the other hand, the generation tasks of clickbait spoiling and neutralisation are explored. The latter is a novel task that is designed to increase the informativeness of a headline, thus removing the information gap. This work opens a new research avenue that has been largely uncharted in the Italian language.
%U https://aclanthology.org/2024.clicit-1.90/
%P 829-841
Markdown (Informal)
[To Click It or Not to Click It: An Italian Dataset for Neutralising Clickbait Headlines](https://aclanthology.org/2024.clicit-1.90/) (Russo et al., CLiC-it 2024)
ACL