@inproceedings{mu-etal-2024-examining-temporalities,
title = "Examining Temporalities on Stance Detection towards {COVID}-19 Vaccination",
author = "Mu, Yida and
Jin, Mali and
Bontcheva, Kalina and
Song, Xingyi",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.594",
pages = "6732--6738",
abstract = "Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public{'}s stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved over time on social media. Thus, it is necessary to account for possible temporal shifts when analysing these stances. This study aims to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination on Twitter. To this end, we evaluate a range of transformer-based models using chronological (splitting the training, validation, and test sets in order of time) and random splits (randomly splitting these three sets) of social media data. Our findings reveal significant discrepancies in model performance between random and chronological splits in several existing COVID-19-related datasets; specifically, chronological splits significantly reduce the accuracy of stance classification. Therefore, real-world stance detection approaches need to be further refined to incorporate temporal factors as a key consideration.",
}
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<abstract>Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public’s stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved over time on social media. Thus, it is necessary to account for possible temporal shifts when analysing these stances. This study aims to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination on Twitter. To this end, we evaluate a range of transformer-based models using chronological (splitting the training, validation, and test sets in order of time) and random splits (randomly splitting these three sets) of social media data. Our findings reveal significant discrepancies in model performance between random and chronological splits in several existing COVID-19-related datasets; specifically, chronological splits significantly reduce the accuracy of stance classification. Therefore, real-world stance detection approaches need to be further refined to incorporate temporal factors as a key consideration.</abstract>
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%0 Conference Proceedings
%T Examining Temporalities on Stance Detection towards COVID-19 Vaccination
%A Mu, Yida
%A Jin, Mali
%A Bontcheva, Kalina
%A Song, Xingyi
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F mu-etal-2024-examining-temporalities
%X Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public’s stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved over time on social media. Thus, it is necessary to account for possible temporal shifts when analysing these stances. This study aims to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination on Twitter. To this end, we evaluate a range of transformer-based models using chronological (splitting the training, validation, and test sets in order of time) and random splits (randomly splitting these three sets) of social media data. Our findings reveal significant discrepancies in model performance between random and chronological splits in several existing COVID-19-related datasets; specifically, chronological splits significantly reduce the accuracy of stance classification. Therefore, real-world stance detection approaches need to be further refined to incorporate temporal factors as a key consideration.
%U https://aclanthology.org/2024.lrec-main.594
%P 6732-6738
Markdown (Informal)
[Examining Temporalities on Stance Detection towards COVID-19 Vaccination](https://aclanthology.org/2024.lrec-main.594) (Mu et al., LREC-COLING 2024)
ACL