Advancements and characteristics of scholar profiling research in China

Abstract

This study analyzes the development and characteristics of scholar profiling research in China from 2016 to 2022, utilizing content analysis of 38 core journal articles. Scholar profiling, adapted from user profiling in big data, involves extracting and analyzing academic data to build models that describe scholars' characteristics and behaviors. Our findings highlight the use of diverse data sources, including commercial databases and social platforms, and emphasize the importance of data preprocessing and modeling techniques. While applied research dominates, the field faces challenges such as the lack of standardized data collection and depth in model construction. The study underscores the need for robust profiling tools and broader application scenarios to enhance the effectiveness of scholar profiling systems.