Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan

• Published in: IEEE transactions on computational social systems Vol. 8; no. 4; pp. 1030 - 1041
• Main Authors: Harakawa, Ryosuke, Iwahashi, Masahiro
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blogs; Community detection; coronavirus; coronavirus disease (COVID-19); Coronaviruses; Correlation; COVID-19; Data mining; Information retrieval; network analysis; network centrality; Occupancy; Particle measurements; semantic understanding; Semantics; Social networking (online); Viral diseases; Japan; Community detection; coronavirus; network analysis; network centrality; coronavirus disease (COVID-19); semantic understanding
• ISSN: 2329-924X; 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2021.3063820

This article presents a method that detects tweet communities with similar topics and ranks the communities by importance measures . By identifying the tweet communities that have high importance measures, it is possible for users to easily find important information about the coronavirus disease (COVID-19). Specifically, we first construct a community network, whose nodes are tweet communities obtained by applying a community detection method to a tweet network. The community network is constructed based on textual similarities between tweet communities and sizes of tweet communities. Second, we apply algorithms for calculating centrality to the community network. Because the obtained centrality is based on tweet community sizes as well, we call it the importance measure in distinction to conventional centrality. The importance measure can simultaneously evaluate the importance of topics in the entire data set and occupancy (or dominance) of tweet communities in the network structure. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to May 15, 2020. The results show that the proposed method is able to extract keywords that have a high correlation with the number of people infected with COVID-19 in Japan. Because users can browse the keywords from a small number of central tweet communities, quick and easy understanding of important information becomes feasible.