@inproceedings{luusi-etal-2024-political,
title = "Political Stance Detection in {E}stonian News Media",
author = {L{\"u}{\"u}si, Lauri and
Kangur, Uku and
Chakraborty, Roshni and
Sharma, Rajesh},
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and
Pirinen, Flammie and
Macias, Melany and
Crespo Avila, Mario},
booktitle = "Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages",
month = nov,
year = "2024",
address = "Helsinki, Finland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwclul-1.2",
pages = "12--28",
abstract = "Newspapers have always remained an important medium for disseminating information to the masses. With continuous access and availability of news, there is a severe competition among news media agencies to attract user attention. Therefore, ensuring fairness in news reporting, such as, politically stance neutral reporting has become more crucial than before. Although several research studies have explored and detected political stance in English news articles, there is a lack of research focusing on low-resource languages like Estonian. To address this gap, this paper examines the effectiveness of established stance-detection features that have been successful for English news media, while also proposing novel features tailored specifically for Estonian. Our study consists of 32 different features comprising of lexical, Estonian-specific, framing and sentiment-related features out of which we identify 15 features as useful for stance detection.",
}
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<abstract>Newspapers have always remained an important medium for disseminating information to the masses. With continuous access and availability of news, there is a severe competition among news media agencies to attract user attention. Therefore, ensuring fairness in news reporting, such as, politically stance neutral reporting has become more crucial than before. Although several research studies have explored and detected political stance in English news articles, there is a lack of research focusing on low-resource languages like Estonian. To address this gap, this paper examines the effectiveness of established stance-detection features that have been successful for English news media, while also proposing novel features tailored specifically for Estonian. Our study consists of 32 different features comprising of lexical, Estonian-specific, framing and sentiment-related features out of which we identify 15 features as useful for stance detection.</abstract>
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%0 Conference Proceedings
%T Political Stance Detection in Estonian News Media
%A Lüüsi, Lauri
%A Kangur, Uku
%A Chakraborty, Roshni
%A Sharma, Rajesh
%Y Hämäläinen, Mika
%Y Pirinen, Flammie
%Y Macias, Melany
%Y Crespo Avila, Mario
%S Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages
%D 2024
%8 November
%I Association for Computational Linguistics
%C Helsinki, Finland
%F luusi-etal-2024-political
%X Newspapers have always remained an important medium for disseminating information to the masses. With continuous access and availability of news, there is a severe competition among news media agencies to attract user attention. Therefore, ensuring fairness in news reporting, such as, politically stance neutral reporting has become more crucial than before. Although several research studies have explored and detected political stance in English news articles, there is a lack of research focusing on low-resource languages like Estonian. To address this gap, this paper examines the effectiveness of established stance-detection features that have been successful for English news media, while also proposing novel features tailored specifically for Estonian. Our study consists of 32 different features comprising of lexical, Estonian-specific, framing and sentiment-related features out of which we identify 15 features as useful for stance detection.
%U https://aclanthology.org/2024.iwclul-1.2
%P 12-28
Markdown (Informal)
[Political Stance Detection in Estonian News Media](https://aclanthology.org/2024.iwclul-1.2) (Lüüsi et al., IWCLUL 2024)
ACL
- Lauri Lüüsi, Uku Kangur, Roshni Chakraborty, and Rajesh Sharma. 2024. Political Stance Detection in Estonian News Media. In Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages, pages 12–28, Helsinki, Finland. Association for Computational Linguistics.