Tripartite Intelligence: Synergizing Deep Neural Network, Large Language Model, and Human Intelligence for Public Health Misinformation Detection (Archival Full Paper)
Authors: 
Yang Zhang, Ruohan Zong, Lanyu Shang, Zhenrui Yue, Huimin Zeng, Yifan Liu, Dong WangAuthors Info & Claims
Yang Zhang, Ruohan Zong, Lanyu Shang, Zhenrui Yue, Huimin Zeng, Yifan Liu, Dong WangAuthors Info & ClaimsPages 63 - 75
Abstract
The threat of rapidly spreading health misinformation through social media during crises like COVID-19 emphasizes the importance of addressing both clear falsehoods and complex misinformation, including conspiracy theories and subtle distortions. This paper designs a novel tripartite collective intelligence approach that integrates deep neural networks (DNNs), large language models (LLMs), and crowdsourced human intelligence (HI) to collaboratively detect complex forms of public health misinformation on social media. Our design is inspired by the collaborative strengths of DNNs, LLMs, and HI, which complement each other. We observe that DNNs efficiently handle large datasets for initial misinformation screening but struggle with complex content and rely on high-quality training data. LLMs enhance misinformation detection with improved language understanding but may sometimes provide eloquent yet factually incorrect explanations, risking misinformation mislabeling. HI provides critical thinking and ethical judgment superior to DNNs and LLMs but is slower and more costly in misinformation detection. In particular, we develop TriIntel, a tripartite collaborative intelligence framework that leverages the collective intelligence of DNNs, LLMs, and HI to tackle the public health information detection problem under a novel few-shot and uncertainty-aware maximum likelihood estimation framework. Evaluation results on a real-world public health misinformation detection application related to COVID-19 show that TriIntel outperforms representative DNNs, LLMs, and human-AI collaboration baselines in accurately detecting public health misinformation under a diverse set of evaluation scenarios.
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