Construction and Validation of a Hotspot Coding System for Public Library Short Video Titles Based on Human-Intelligence Collaborative Pathways

Wang Yisheng, Xiao Bing, Kang Qi

Knowledge Management Forum ›› 2026, Vol. 11 ›› Issue (5) : 0.

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Knowledge Management Forum ›› 2026, Vol. 11 ›› Issue (5) : 0. DOI: 10.13266/j.issn.2095-5472.2026.041  CSTR: 32306.14.j.issn.2095-5472.2026.041

Construction and Validation of a Hotspot Coding System for Public Library Short Video Titles Based on Human-Intelligence Collaborative Pathways

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Abstract

Purpose/Significance In response to the issues in public library short video research, where the identification of title hotspots largely relies on empirical judgement and hotspot resources are difficult to consistently identify and verify, this study utilises large language models to perform temporal backtracking of sample data, constructing and validating a human-AI collaborative coding system for hotspot identification in short-video titles. The study further examines the relationship between different types of hotspot resources and user interaction performance, providing a verifiable analytical tool for public library short video content diagnosis and dissemination optimisation. Method/Process Using 2 621 popular short video titles published by public Douyin accounts from 2021 to 2025 as samples, the study employedcontent analysis and developeda human-AI collaborative coding process comprising theoretical derivation, formative-sample pre-coding, large language model-assisted preliminary screening, manual review, and rule revision. Title hotspot characteristics were operationalised into three dimensions: hotspot status, hotspot resource type, and spatial orientation, forming the A-H-S(Attention-Hotspot-Space) coding system. The reliability and applicability of the coding system were evaluated through content validity testing, intercoder reliability and model stability testing, full-sample applicability testing, and criterion-related validity analysis based on interaction indicators. Result/Conclusion The study develops an A-H-S coding system comprising A-class hotspot status, H-class hotspot resource type, and S-class spatial orientation, which can consistently identify different hotspot resources in public library short video titles. Different hotspot resources exhibit distinct interaction patterns, providing a basis for matching title resources with different communication objectives. This research expands the analytical toolkit for public library short video communication research and provides a verifiable human-AI collaborative pathway for large language model-assisted library text coding.

Key words

human-AI collaboration / short video in public / identification of trends / text encoding / temporal retrospective

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Wang Yisheng , Xiao Bing , Kang Qi. Construction and Validation of a Hotspot Coding System for Public Library Short Video Titles Based on Human-Intelligence Collaborative Pathways[J]. Knowledge Management Forum. 2026, 11(5): 0 https://doi.org/10.13266/j.issn.2095-5472.2026.041

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