@article{wang-etal-2026-pikgl,
    title = "{P}i{KGL}: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection",
    author = "Wang, Bingbing  and
      Lin, Jingjie  and
      Bai, Zhixin  and
      Song, Xintong  and
      Wang, Qianlong  and
      Yang, Min  and
      Zeng, Xi  and
      Li, Jing  and
      Xu, Ruifeng",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "14",
    year = "2026",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-latest-mitpress-cl-tacl/2026.tacl-1.11/",
    doi = "10.1162/tacl.a.612",
    pages = "217--232",
    abstract = "Stance detection on social media plays a vital role in understanding public opinion on contentious topics. While prior work leverages external knowledge sources like Wikipedia to enrich limited target information, it primarily introduces conceptual content, neglecting the interpretability potential of knowledge and often leading to the incorporation of irrelevant or redundant information that hinders stance prediction performance. To address this, we introduce PiKGL, a Pruned interpretable Knowledge Graph Learning framework for explainable stance detection. Specifically, we first extract event triplets and topics to obtain real-world knowledge, which is then used to construct an interpretable knowledge graph. To ensure precision and minimize noise, we introduce a retrieval-guided pruning strategy that incorporates commonsense knowledge, filtering redundant information of the interpretable knowledge graph. Finally, the pruned knowledge graph is injected into a large language model to jointly model textual, target, and commonsense for improved stance comprehension. Experimental results conducted on three public datasets demonstrate our PiKGL achieves state-of-the-art performance on stance detection."
}