StanceMining: An open-source stance detection library supporting time-series and visualization

Benjamin Steel, Derek Ruths


Abstract
Despite the size of the field, stance detection has remained inaccessible to most researchers due to implementation barriers. Here we present a library that allows easy access to an end-to-end stance modelling solution. This library comes complete with everything needed to go from a corpus of documents, to exploring stance trends in a corpus through an interactive dashboard. To support this, we provide stance target extraction, stance detection, stance time-series trend inference, and an exploratory dashboard, all available in an easy-to-use library. We hope that this library can increase the accessibility of stance detection for the wider community of those who could benefit from this method.
Anthology ID:
2025.ijcnlp-demo.8
Volume:
Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations
Month:
December
Year:
2025
Address:
Mumbai, India
Editors:
Xuebo Liu, Ayu Purwarianti
Venue:
IJCNLP
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Publisher:
Association for Computational Linguistics
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Pages:
67–76
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URL:
https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-demo.8/
DOI:
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Cite (ACL):
Benjamin Steel and Derek Ruths. 2025. StanceMining: An open-source stance detection library supporting time-series and visualization. In Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations, pages 67–76, Mumbai, India. Association for Computational Linguistics.
Cite (Informal):
StanceMining: An open-source stance detection library supporting time-series and visualization (Steel & Ruths, IJCNLP 2025)
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https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-demo.8.pdf