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        <title>PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection</title>
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        <namePart type="given">Xintong</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl.a.612</identifier>
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%0 Journal Article
%T PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection
%A Wang, Bingbing
%A Lin, Jingjie
%A Bai, Zhixin
%A Song, Xintong
%A Wang, Qianlong
%A Yang, Min
%A Zeng, Xi
%A Li, Jing
%A Xu, Ruifeng
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F wang-etal-2026-pikgl
%X 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.
%R 10.1162/tacl.a.612
%U https://aclanthology.org/2026.tacl-1.11/
%U https://doi.org/10.1162/tacl.a.612
%P 217-232

Markdown (Informal)

[PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection](https://aclanthology.org/2026.tacl-1.11/) (Wang et al., TACL 2026)

• PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (Wang et al., TACL 2026)

ACL

• Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, and Ruifeng Xu. 2026. PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection. Transactions of the Association for Computational Linguistics, 14:217–232.