Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks
Stance detection, the task of identifying the stance expressed in a text toward a specific target, is essential for analyzing public opinion across diverse domains. The existing approaches primarily focus on modeling the semantic relationship between the text and target, but they often struggle when...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/5809 |
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| author | Xinyi Chen Bo Liu Huaping Hu Yiqing Cai Mengmeng Guo Xingkong Ma |
| author_facet | Xinyi Chen Bo Liu Huaping Hu Yiqing Cai Mengmeng Guo Xingkong Ma |
| author_sort | Xinyi Chen |
| collection | DOAJ |
| description | Stance detection, the task of identifying the stance expressed in a text toward a specific target, is essential for analyzing public opinion across diverse domains. The existing approaches primarily focus on modeling the semantic relationship between the text and target, but they often struggle when the target is implicit or indirectly referenced. In real-world scenarios, stance is frequently conveyed through references to related entities, events, or contextual implications, making stance detection particularly challenging. To tackle this challenge, we propose a novel framework that leverages large language models to construct a heterogeneous stance network from textual data. Based on this network, we develop two complementary methodologies tailored for distinct application scenarios: (1) In a supervised setting, we employ a graph neural network approach to learn stance representations from the heterogeneous stance network, enhancing stance prediction performance. (2) For zero-shot stance detection, we introduce an LLM-based method that leverages the heterogeneous stance network to infer stance without task-specific supervision. The experimental results on benchmark datasets demonstrate that our methods outperform the existing approaches, highlighting their effectiveness in both supervised and zero-shot scenarios. |
| format | Article |
| id | doaj-art-8abc8bc26b3b4ee3acc5992c6ef25683 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8abc8bc26b3b4ee3acc5992c6ef256832025-08-20T03:10:54ZengMDPI AGApplied Sciences2076-34172025-05-011511580910.3390/app15115809Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance NetworksXinyi Chen0Bo Liu1Huaping Hu2Yiqing Cai3Mengmeng Guo4Xingkong Ma5College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaStance detection, the task of identifying the stance expressed in a text toward a specific target, is essential for analyzing public opinion across diverse domains. The existing approaches primarily focus on modeling the semantic relationship between the text and target, but they often struggle when the target is implicit or indirectly referenced. In real-world scenarios, stance is frequently conveyed through references to related entities, events, or contextual implications, making stance detection particularly challenging. To tackle this challenge, we propose a novel framework that leverages large language models to construct a heterogeneous stance network from textual data. Based on this network, we develop two complementary methodologies tailored for distinct application scenarios: (1) In a supervised setting, we employ a graph neural network approach to learn stance representations from the heterogeneous stance network, enhancing stance prediction performance. (2) For zero-shot stance detection, we introduce an LLM-based method that leverages the heterogeneous stance network to infer stance without task-specific supervision. The experimental results on benchmark datasets demonstrate that our methods outperform the existing approaches, highlighting their effectiveness in both supervised and zero-shot scenarios.https://www.mdpi.com/2076-3417/15/11/5809stance detectiongraph neural networkslarge language modelsgraph learningheterogeneous networks |
| spellingShingle | Xinyi Chen Bo Liu Huaping Hu Yiqing Cai Mengmeng Guo Xingkong Ma Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks Applied Sciences stance detection graph neural networks large language models graph learning heterogeneous networks |
| title | Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks |
| title_full | Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks |
| title_fullStr | Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks |
| title_full_unstemmed | Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks |
| title_short | Integrating Graph Neural Networks and Large Language Models for Stance Detection via Heterogeneous Stance Networks |
| title_sort | integrating graph neural networks and large language models for stance detection via heterogeneous stance networks |
| topic | stance detection graph neural networks large language models graph learning heterogeneous networks |
| url | https://www.mdpi.com/2076-3417/15/11/5809 |
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