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Sign-Aware Graph Learning Framework for Stance Detection
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DOI：10.1109/LSP.2025.3588084.png)
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Abstract
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Stance detection aims to identify individuals’ attitudes toward a specific target. The signs of links are beneficial in various graph-related tasks. However, existing stance detection methods neglect this information. This is because the signs of links are not explicitly provided in many real-world social networks, which makes current signed network representation learning methods inapplicable to this scenario. In this work, we propose a Sign-Aware Graph Learning framework (SAGL) for stance detection by innovating a Sign-Inferred Graph Auto-Encoder (SIGAE) and a Balance-Infused Signed Graph Attention Network (BSGAN). SIGAE infers and generates signed links from unsigned networks by exploiting stance labels and underlying structure information. BSGAN introduces balance theory into signed graph attention networks and refines node representations by capturing and dynamically adjusting the influence of signed links. Extensive experiments on real-world datasets demonstrate that SAGL significantly outperforms competitive baselines.
Keywords:
Stance detection
graph neural networks
signed networks
balance theory
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