@inproceedings{thapa-etal-2024-stance,
title = "Stance and Hate Event Detection in Tweets Related to Climate Activism - Shared Task at {CASE} 2024",
author = {Thapa, Surendrabikram and
Rauniyar, Kritesh and
Jafri, Farhan and
Shiwakoti, Shuvam and
Veeramani, Hariram and
Jain, Raghav and
Kohli, Guneet Singh and
H{\"u}rriyeto{\u{g}}lu, Ali and
Naseem, Usman},
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Thapa, Surendrabikram and
Uludo{\u{g}}an, G{\"o}k{\c{c}}e},
booktitle = "Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.case-1.33",
pages = "234--247",
abstract = "Social media plays a pivotal role in global discussions, including on climate change. The variety of opinions expressed range from supportive to oppositional, with some instances of hate speech. Recognizing the importance of understanding these varied perspectives, the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) at EACL 2024 hosted a shared task focused on detecting stances and hate speech in climate activism-related tweets. This task was divided into three subtasks: subtasks A and B concentrated on identifying hate speech and its targets, while subtask C focused on stance detection. Participants{'} performance was evaluated using the macro F1-score. With over 100 teams participating, the highest F1 scores achieved were 91.44{\%} in subtask C, 78.58{\%} in subtask B, and 74.83{\%} in subtask A. This paper details the methodologies of 24 teams that submitted their results to the competition{'}s leaderboard.",
}
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<abstract>Social media plays a pivotal role in global discussions, including on climate change. The variety of opinions expressed range from supportive to oppositional, with some instances of hate speech. Recognizing the importance of understanding these varied perspectives, the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) at EACL 2024 hosted a shared task focused on detecting stances and hate speech in climate activism-related tweets. This task was divided into three subtasks: subtasks A and B concentrated on identifying hate speech and its targets, while subtask C focused on stance detection. Participants’ performance was evaluated using the macro F1-score. With over 100 teams participating, the highest F1 scores achieved were 91.44% in subtask C, 78.58% in subtask B, and 74.83% in subtask A. This paper details the methodologies of 24 teams that submitted their results to the competition’s leaderboard.</abstract>
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%0 Conference Proceedings
%T Stance and Hate Event Detection in Tweets Related to Climate Activism - Shared Task at CASE 2024
%A Thapa, Surendrabikram
%A Rauniyar, Kritesh
%A Jafri, Farhan
%A Shiwakoti, Shuvam
%A Veeramani, Hariram
%A Jain, Raghav
%A Kohli, Guneet Singh
%A Hürriyetoğlu, Ali
%A Naseem, Usman
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F thapa-etal-2024-stance
%X Social media plays a pivotal role in global discussions, including on climate change. The variety of opinions expressed range from supportive to oppositional, with some instances of hate speech. Recognizing the importance of understanding these varied perspectives, the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) at EACL 2024 hosted a shared task focused on detecting stances and hate speech in climate activism-related tweets. This task was divided into three subtasks: subtasks A and B concentrated on identifying hate speech and its targets, while subtask C focused on stance detection. Participants’ performance was evaluated using the macro F1-score. With over 100 teams participating, the highest F1 scores achieved were 91.44% in subtask C, 78.58% in subtask B, and 74.83% in subtask A. This paper details the methodologies of 24 teams that submitted their results to the competition’s leaderboard.
%U https://aclanthology.org/2024.case-1.33
%P 234-247
Markdown (Informal)
[Stance and Hate Event Detection in Tweets Related to Climate Activism - Shared Task at CASE 2024](https://aclanthology.org/2024.case-1.33) (Thapa et al., CASE-WS 2024)
ACL
- Surendrabikram Thapa, Kritesh Rauniyar, Farhan Jafri, Shuvam Shiwakoti, Hariram Veeramani, Raghav Jain, Guneet Singh Kohli, Ali Hürriyetoğlu, and Usman Naseem. 2024. Stance and Hate Event Detection in Tweets Related to Climate Activism - Shared Task at CASE 2024. In Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024), pages 234–247, St. Julians, Malta. Association for Computational Linguistics.