@inproceedings{ranasinghe-etal-2023-publish,
title = "Publish or Hold? Automatic Comment Moderation in {L}uxembourgish News Articles",
author = "Ranasinghe, Tharindu and
Plum, Alistair and
Purschke, Christoph and
Zampieri, Marcos",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.104",
pages = "968--978",
abstract = "Recently, the internet has emerged as the primary platform for accessing news. In the majority of these news platforms, the users now have the ability to post comments on news articles and engage in discussions on various social media. While these features promote healthy conversations among users, they also serve as a breeding ground for spreading fake news, toxic discussions and hate speech. Moderating or removing such content is paramount to avoid unwanted consequences for the readers. How- ever, apart from a few notable exceptions, most research on automatic moderation of news article comments has dealt with English and other high resource languages. This leaves under-represented or low-resource languages at a loss. Addressing this gap, we perform the first large-scale qualitative analysis of more than one million Luxembourgish comments posted over the course of 14 years. We evaluate the performance of state-of-the-art transformer models in Luxembourgish news article comment moderation. Furthermore, we analyse how the language of Luxembourgish news article comments has changed over time. We observe that machine learning models trained on old comments do not perform well on recent data. The findings in this work will be beneficial in building news comment moderation systems for many low-resource languages",
}
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<abstract>Recently, the internet has emerged as the primary platform for accessing news. In the majority of these news platforms, the users now have the ability to post comments on news articles and engage in discussions on various social media. While these features promote healthy conversations among users, they also serve as a breeding ground for spreading fake news, toxic discussions and hate speech. Moderating or removing such content is paramount to avoid unwanted consequences for the readers. How- ever, apart from a few notable exceptions, most research on automatic moderation of news article comments has dealt with English and other high resource languages. This leaves under-represented or low-resource languages at a loss. Addressing this gap, we perform the first large-scale qualitative analysis of more than one million Luxembourgish comments posted over the course of 14 years. We evaluate the performance of state-of-the-art transformer models in Luxembourgish news article comment moderation. Furthermore, we analyse how the language of Luxembourgish news article comments has changed over time. We observe that machine learning models trained on old comments do not perform well on recent data. The findings in this work will be beneficial in building news comment moderation systems for many low-resource languages</abstract>
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%0 Conference Proceedings
%T Publish or Hold? Automatic Comment Moderation in Luxembourgish News Articles
%A Ranasinghe, Tharindu
%A Plum, Alistair
%A Purschke, Christoph
%A Zampieri, Marcos
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F ranasinghe-etal-2023-publish
%X Recently, the internet has emerged as the primary platform for accessing news. In the majority of these news platforms, the users now have the ability to post comments on news articles and engage in discussions on various social media. While these features promote healthy conversations among users, they also serve as a breeding ground for spreading fake news, toxic discussions and hate speech. Moderating or removing such content is paramount to avoid unwanted consequences for the readers. How- ever, apart from a few notable exceptions, most research on automatic moderation of news article comments has dealt with English and other high resource languages. This leaves under-represented or low-resource languages at a loss. Addressing this gap, we perform the first large-scale qualitative analysis of more than one million Luxembourgish comments posted over the course of 14 years. We evaluate the performance of state-of-the-art transformer models in Luxembourgish news article comment moderation. Furthermore, we analyse how the language of Luxembourgish news article comments has changed over time. We observe that machine learning models trained on old comments do not perform well on recent data. The findings in this work will be beneficial in building news comment moderation systems for many low-resource languages
%U https://aclanthology.org/2023.ranlp-1.104
%P 968-978
Markdown (Informal)
[Publish or Hold? Automatic Comment Moderation in Luxembourgish News Articles](https://aclanthology.org/2023.ranlp-1.104) (Ranasinghe et al., RANLP 2023)
ACL