Analysis of Leading Communities Contributing to arXiv Information Distribution on Twitter
Kyosuke Shimada, Kazuhiro Kazama, Mitsuo Yoshida, Ikki Ohmukai, Sho, Sato

TL;DR
This paper models and analyzes how arXiv-related information spreads on Twitter, revealing community structures, key influencers, and the influence of cultural backgrounds on information dissemination.
Contribution
It introduces a three-layer model of arXiv information diffusion on Twitter and applies network analysis to identify community roles and influential spreaders.
Findings
Information circulates from spreaders to collectors on Twitter.
Multiple communities form based on research fields and backgrounds.
Key persons include field leaders and regional bridges.
Abstract
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…
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