Measuring topic network centrality for identifying technology and technological development in online communities

Abstract

Online communities are a rapidly growing knowledge repository that provides scholarly research, technical discussion, and social interactivity. This abundance of online information increases the difficulty of keeping up with new developments difficult for researchers and practitioners. Thus, we introduced a novel method that analyses both knowledge and social sentiment within the online community to discover the topical coverage of emerging technology and trace technological trends. The method utilizes the Weibull distribution and Shannon entropy to measure and link social sentiment with technological topics. Based on question-and-answer and social sentiment data from Zhihu, which is an online question and answer (Q&A) community with high-profile entrepreneurs and public intellectuals, we built an undirected weighting network and measured the centrality of nodes for technology identification. An empirical study on artificial intelligence technology trends supported by expert knowledge-based evaluation and cognition provides sufficient evidence of the method's ability to identify technology. We found that the social sentiment of hot technological topics presents a long-tailed distribution statistical pattern. High similarity between the topic popularity and emerging technology development trends appears in the online community. Finally, we discuss the findings in various professional fields that are widely applied to discover and track hot technological topics.