論文

査読有り 本文へのリンクあり
2021年12月

Analysis of Leading Communities Contributing to arXiv Information Distribution on Twitter

The 20th IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '21)
  • Kyosuke Shimada
  • ,
  • Kazuhiro Kazama
  • ,
  • Mitsuo Yoshida
  • ,
  • Ikki Ohmukai
  • ,
  • Sho Sato

記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1145/3486622.3493947

To analyze the impact that arXiv is having on the world, in this paper we propose an arXiv information distribution model on Twitter, which has a three-layer structure: arXiv papers, information spreaders, and information collectors. First, we use the HITS algorithm to analyze the arXiv information diffusion network with users as nodes, which is created from three types of behavior on Twitter regarding arXiv papers: tweeting, retweeting, and liking. Next, we extract communities from the network of information spreaders with positive authority and hub degrees using the Louvain method, and analyze the relationship and roles of information spreaders in communities using research field, linguistic, and temporal characteristics. From our analysis using the tweet and arXiv datasets, we found that information about arXiv papers circulates on Twitter from information spreaders to information collectors, and that multiple communities of information spreaders are formed according to their research fields. It was also found that different communities were formed in the same research field, depending on the research or cultural background of the information spreaders. We were able to identify two types of key persons: information spreaders who lead the relevant field in the international community and information spreaders who bridge the regional and international communities using English and their native language. In addition, we found that it takes some time to gain trust as an information spreader.

リンク情報
DOI
https://doi.org/10.1145/3486622.3493947
共同研究・競争的資金等の研究課題
利用者の研究練度に応じた多様な観点を統合する学術情報システム
URL
https://arxiv.org/abs/2112.08073 本文へのリンクあり
ID情報
  • DOI : 10.1145/3486622.3493947

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