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Stance Detection on Vietnamese Social Media

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 417)


This paper studies a new emerging NLP topic, stance detection on social media. The goal is to automatically elicit stance information towards a specific claim made by a primary author which is explicitly or implicitly stated in texts. While most studies have been done for high-resource languages, this work is dedicated to a low-resource language. To stimulate this research direction, we introduce the first Vietnamese corpus (Contact the first author) annotated with stance information on social issues. This corpus consists of 11,253 pairs of claims-comments labeled with one of four stances (i.e. agree, disagree, discuss, and unrelated). Based on this corpus, a wide variety of advanced classification methods have been carefully investigated. Experimental results show that the deep learning model - attentive biLSTM outperforms the traditional methods by a large margin. This model is further enhanced when it is enriched with character embeddings, and stance knowledge. Using this best model, we achieve an \(F_1\)-macro score of 66.71%, an accuracy score of 66.32% in detecting stances. To our knowledge, this is the first work that provides a baseline result for future research on this interesting yet unexplored problem in Vietnamese.

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This work was funded by the International School, Vietnam National University Hanoi under the project CS.NNC/2020-04.

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Correspondence to Oanh Thi Tran .

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Tran, O.T., Dao, T.T., Dang, Y.N. (2022). Stance Detection on Vietnamese Social Media. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham.

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