This is the repository for the article "Dynamic Stance: Modeling Discussions by Labeling the Interactions".
Stance detection is an increasingly popular task that has been mainly modeled as a static task, by assigning the expressed attitude of a text toward a given topic. Such a framing presents limitations, with trained systems showing poor generalization capabilities and being strongly topic-dependent. In this work, we propose modeling stance as a dynamic task, by focusing on the interactions between a message and their replies. For this purpose, we present a new annotation scheme that enables the categorization of all kinds of textual interactions. As a result, we have created a new corpus, the Dynamic Stance Corpus (DySC), consisting of three datasets in two middle-resourced languages: Catalan and Dutch. Our data analysis further supports our modeling decisions, empirically showing differences between the annotation of stance in static and dynamic contexts. We fine-tuned a series of monolingual and multilingual models on DySC, showing portability across topics and languages.
The folders of analysis and create_dataset contain the scripts used to create and analyse the datasets of this article. If you want to reproduce the results follow the instructions in model_train.
The folder data contains the publicly distributable format of the Dutch component of DySC, the Dutch Stance Twitter (DuST). To access the full text format, interested must sign a Data Sharing agreement. The full text version of the corpus is available via DataverseNL.
The Catalan components of DySC are available via Hugging Face datasets:
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Catalan Stance and Emotions Twitter dataset (CaSET): https://huggingface.co/datasets/projecte-aina/CaSET-catalan-stance-emotions-twitter
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Catalan Stance and Emotion Racó dataset (CaSERa): https://huggingface.co/datasets/projecte-aina/CaSERa-catalan-stance-emotions-raco
Calvo Figueras, B., Baucells, I., and Caselli, T. 2023. Dynamic Stance: Modeling Discussions by Labeling the Interactions. In: Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore. Association for Computational Linguistics