Dynamic Seed Topic Construction and LLM-Driven Multi-Topic Identification for Social Q&A Platforms

Social Q&A platforms produce short, noisy, and highly diverse user questions, making coarse topic labels insufficient for accurate information organization. This study proposes a dynamic seed topic construction and multi-demand topic identification framework for health science popularization questions on the Zhihu platform. Using 3529 cleaned questions derived from 2011 to 2024, the framework combines BERTopic clustering, LLM-based topic naming, and a TOP-K cross-filtering update strategy that integrates semantic similarity and frequency. The LLM then performs topic identification, optimization, and assignment, and a co-occurrence network analyzes topic associations. The method generates 26 initial seed topics, which expand to 51 topic instances and are subsequently optimized to 36 topics. Compared with LDA, BERTopic, and LLM-based baselines, our framework achieved the best topic coherence and the lowest topic similarity while maintaining good topic diversity. Human evaluation by 17 volunteers on 30 questions showed that the model performed at least as well as user-assigned tags across relevance, comprehensiveness, clarity, accuracy, and readability, with clearer advantages in relevance and accuracy. These results suggest that the proposed framework can identify fine-grained, interpretable, and strongly associated information-demand topics for social Q&A platforms.