User portrait analysis on Chinese diabetes online health platforms: an information processing perspective DOI
Yang Zhang, Yunyun Gao,

Tingting Wu

и другие.

Online Information Review, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Purpose The objective of this research is to investigate the characteristics information interaction among users largest online health platform for diabetes in China from an processing viewpoint, determine stages and reveal variations requirements behavioral patterns across different user groups at various levels, ultimately creating a segmentation labeling system enhance portrait. Design/methodology/approach This study adopts deep learning BILSTM-CNN classification model identify characteristics, then classify into three groups. LDA topic employed analyze needs these Findings utilizes combined model, showcasing enhanced effectiveness classifying degree comments. Our also increases accuracy stability compared conventional models, achieving F1 score 95.0% (F1 Score: CNN 92%, LSTM 94%, BILSTM 94%). Based on results, were grouped showed differences needs, behavior natural attributes. Originality/value Taking as entry point, deeply mined data China’s platform, identifying present comments categorizing reflecting varying depths processing. multi-dimensional analysis, we innovatively constructed refined system, finally depicted complete not only enriches theoretical framework cognitive portrait but contributes personalized recommendations platforms diabetes. Peer review peer history article available at: https://publons.com/publon/10.1108/OIR-11-2024-0728 .

Язык: Английский

User portrait analysis on Chinese diabetes online health platforms: an information processing perspective DOI
Yang Zhang, Yunyun Gao,

Tingting Wu

и другие.

Online Information Review, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Purpose The objective of this research is to investigate the characteristics information interaction among users largest online health platform for diabetes in China from an processing viewpoint, determine stages and reveal variations requirements behavioral patterns across different user groups at various levels, ultimately creating a segmentation labeling system enhance portrait. Design/methodology/approach This study adopts deep learning BILSTM-CNN classification model identify characteristics, then classify into three groups. LDA topic employed analyze needs these Findings utilizes combined model, showcasing enhanced effectiveness classifying degree comments. Our also increases accuracy stability compared conventional models, achieving F1 score 95.0% (F1 Score: CNN 92%, LSTM 94%, BILSTM 94%). Based on results, were grouped showed differences needs, behavior natural attributes. Originality/value Taking as entry point, deeply mined data China’s platform, identifying present comments categorizing reflecting varying depths processing. multi-dimensional analysis, we innovatively constructed refined system, finally depicted complete not only enriches theoretical framework cognitive portrait but contributes personalized recommendations platforms diabetes. Peer review peer history article available at: https://publons.com/publon/10.1108/OIR-11-2024-0728 .

Язык: Английский

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