Yang, Zaoli, et al. "Measuring topic network centrality for identifying technology and technological development in online communities." Technological Forecasting and Social Change 167 (2021): 120673.

This paper introduces a novel method for analyzing knowledge and social sentiment within online communities to identify and track emerging technological trends. Utilizing the Weibull distribution and Shannon entropy, the approach links social sentiment with technological topics, creating an undirected weighting network from data collected from Zhihu, a prominent Q&A online community. An empirical study focusing on artificial intelligence technology trends, validated by expert knowledge, demonstrates the method's effectiveness in identifying technological advancements. The findings reveal a long-tailed distribution of social sentiment towards hot technological topics, indicating a high correlation between topic popularity and the development of emerging technologies. The research offers significant insights for professionals in various fields aiming to discover and monitor trending technologies.