A method of identifying domain-specific academic user information needs based on academic Q&A communities

Purpose - To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs. Design/methodology/approach - This study's method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science. Findings - Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories "methods," "experimental phenomena" and "experimental materials" are relatively high in the materials science field. Originality/value - This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users' information needs, which in turn facilitates the platform's academic resource organization and services.