Topic Detection Based on Weak Tie Analysis： A Case Study of LIS Research

• Published in: Journal of data and information science (Warsaw, Poland) Vol. 1; no. 4; pp. 81 - 101
• Main Authors: Wei, Ling, Xu, Haiyun, Wang, Zhenmeng, Dong, Kun, Wang, Chao, Fang, Shu
• Format: Journal Article
• Language: English
• Published: De Gruyter Open 01.09.2017; Sciendo
• Subjects: Library and Information Science (LIS); Research topics; Weak tie network; Weak tie nodes; Weak tie theory; 情报科学; 数据
• ISSN: 2096-157X; 2543-683X; 2543-683X
• DOI: 10.20309/jdis.201626

Purpose： Based on the weak tie theory, this paper proposes a series of connection indicatorsof weak tie subnets and weak tie nodes to detect research topics, recognize their connections,and understand their evolution.Design/methodology/approach： First, keywords are extracted from article titles and pre-processed. Second, high-frequency keywords are selected to generate weak tie co-occurrencenetworks. By removing the internal lines of clustered sub-topic networks, we focus on theanalysis of weak tie subnets＇ composition and functions and the weak tie nodes＇ roles.