A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis

Ying Lian, Xiaofeng Lin, Xuefan Dong and Shengjie Hou
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Ying Lian: School of Journalism, Communication University of China, Beijing 100024, China
Xiaofeng Lin: Center for Innovation-Driven Development (Center for Digital Economy Research and Development), National Development and Reform Commission, Beijing 100045, China
Xuefan Dong: College of Economics and Management, Beijing University of Technology, Beijing 100124, China
Shengjie Hou: National Innovation Institute of Defense Technology, Beijing 100071, China

Sustainability, 2022, vol. 14, issue 13, 1-19

Abstract: In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.

Keywords: keyword selection; social media; co-occurrence relationship; rich-club (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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