Stance Detection in Twitter Conversations Using Reply Support Classification

Parul Khandelwal, Preety Singh, Rajbir Kaur, Roshni Chakraborty

2025

Abstract

During crisis, social media platforms like Twitter play a crucial role in disseminating information and offering emotional support. Understanding the conversations among people is essential for evaluating the overall impact of the crisis on the public. In this paper, we focus on classifying the replies to tweets during the “Fall of Kabul” event into three classes: supporting, unbiased, and opposing. To achieve this goal, we proposed two frameworks. We used LSTM layers for sentence/word-level feature extraction for classification. We also employed a BERT-based approach where the text of both the tweet and the reply is concatenated.Our evaluation on real-world crisis data showed that the BERT-based architecture outperformed the LSTM models. It produced an F1-score of 0.726 for the opposing class, 0.738 for the unbiased class, and 0.729 for the supportive class. These results highlight the robustness of contextualized embeddings in accurately identifying the stance of replies within Twitter conversations through tweet-reply pairs.

Paper Citation

in Harvard Style

Khandelwal P., Singh P., Kaur R. and Chakraborty R. (2025). Stance Detection in Twitter Conversations Using Reply Support Classification. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 235-242. DOI: 10.5220/0013129800003905

in Bibtex Style

@conference{icpram25,
author={Parul Khandelwal and Preety Singh and Rajbir Kaur and Roshni Chakraborty},
title={Stance Detection in Twitter Conversations Using Reply Support Classification},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={235-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013129800003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Stance Detection in Twitter Conversations Using Reply Support Classification
SN - 978-989-758-730-6
AU - Khandelwal P.
AU - Singh P.
AU - Kaur R.
AU - Chakraborty R.
PY - 2025
SP - 235
EP - 242
DO - 10.5220/0013129800003905
PB - SciTePress