LLM-Augmented Contrastive Learning for Misinformation Detection in Social Networks

Misinformation detection in social networks faces challenges due to complex semantics, scarcity of labeled data, and rapidly evolving false narratives. To address these issues, we present large language model (LLM)-augmented contrastive learning (LACL), a novel framework that integrates LLMs with contrastive learning (CL) for robust and accurate misinformation detection. We begin with an LLM-driven social media data augmentation strategy, utilizing prompt orchestration to generate diverse yet semantically consistent misinformation samples. These augmented samples are integrated into a CL-based detector, where the semantic richness and diversity introduced by the LLM enhance the CL’s discriminative feature extraction and predictive capability, thus improving generalization beyond the original training data. To align with CL’s discriminative goal, we develop a contrastive loss-aware joint training and fine-tuning approach where CL’s discriminative feature learning actively constrains the LLM’s hallucinations and guides the quality of augmentation. Through this closed-loop optimization, the CL-based detector progressively absorbs latent semantic knowledge from the LLM, effectively overcoming semantic complexity and reducing erroneous generations. Experimental results on four benchmark datasets (Twitter15, Twitter16, Weibo, and PHEME) demonstrate that LACL outperforms mainstream deep learning methods and surpasses approaches that apply commercial LLMs for detection without task-specific adaptation. These results hold consistently across different backbone LLMs (qwen and llama), highlighting LACL’s enhanced robustness, adaptability to varying language contexts, and superior generalization capability.