@inproceedings{steel-ruths-2025-stancemining,
title = "{S}tance{M}ining: An open-source stance detection library supporting time-series and visualization",
author = "Steel, Benjamin and
Ruths, Derek",
editor = "Liu, Xuebo and
Purwarianti, Ayu",
booktitle = "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 = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-demo.8/",
pages = "67--76",
ISBN = "979-8-89176-301-2",
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."
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%0 Conference Proceedings
%T StanceMining: An open-source stance detection library supporting time-series and visualization
%A Steel, Benjamin
%A Ruths, Derek
%Y Liu, Xuebo
%Y Purwarianti, Ayu
%S 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
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-301-2
%F steel-ruths-2025-stancemining
%X 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.
%U https://aclanthology.org/2025.ijcnlp-demo.8/
%P 67-76
Markdown (Informal)
[StanceMining: An open-source stance detection library supporting time-series and visualization](https://aclanthology.org/2025.ijcnlp-demo.8/) (Steel & Ruths, IJCNLP 2025)
ACL