Pruning Knowledge Graphs for Sharper Stance Detection
A new framework called PiKGL (Pruned interpretable Knowledge Graph Learning) advances explainable stance detection on social media. The method addresses a key limitation in prior work, which often introduced irrelevant noise from external knowledge sources like Wikipedia. PiKGL constructs an interpretable knowledge graph from extracted event triplets and topics, then applies a retrieval-guided pruning strategy that incorporates commonsense knowledge to filter out redundant information. This pruned, focused knowledge graph is then injected into a large language model, enabling a joint understanding of text, target, and commonsense for superior stance comprehension. The approach has demonstrated state-of-the-art performance across three public datasets, offering a more precise and interpretable model for analyzing public opinion on contentious topics.
Study Significance: For NLP practitioners focused on text classification and information extraction, this research provides a concrete methodology for enhancing large language models with structured, pruned knowledge. It directly tackles the practical challenge of noise in knowledge-augmented models, a critical issue for real-world deployment in sentiment analysis and content moderation. The framework’s emphasis on interpretability through knowledge graphs also offers a strategic path toward more transparent and auditable AI systems in social media analysis.
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