LASTIST: LArge-Scale Target-Independent STance dataset

DongJae Kim, Yaejin Lee, Minsu Park, Eunil Park
10/28/2025
cs.CLcs.AI

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Abstract

Stance detection has emerged as an area of research in the field of artificial intelligence. However, most research is currently centered on the target-dependent stance detection task, which is based on a person's stance in favor of or against a specific target. Furthermore, most benchmark datasets are based on English, making it difficult to develop models in low-resource languages such as Korean, especially for an emerging field such as stance detection. This study proposes the LArge-Scale Target-Independent STance (LASTIST) dataset to fill this research gap. Collected from the press releases of both parties on Korean political parties, the LASTIST dataset uses 563,299 labeled Korean sentences. We provide a detailed description of how we collected and constructed the dataset and trained state-of-the-art deep learning and stance detection models. Our LASTIST dataset is designed for various tasks in stance detection, including target-independent stance detection and diachronic evolution stance detection. We deploy our dataset on https://anonymous.4open.science/r/LASTIST-3721/.

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Cite this paper

@article{kim2025lastist,
  title  = {LASTIST: LArge-Scale Target-Independent STance dataset},
  author = {DongJae Kim and Yaejin Lee and Minsu Park and Eunil Park},
  year   = {2025},
  eprint = {2510.25783},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.25783v1}
}

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