Twitter上对arXiv信息传播贡献的主要社区分析
Analysis of Leading Communities Contributing to arXiv Information Distribution on Twitter
摘要：
为了分析arXiv对世界的影响，本文提出了一个Twitter上的arXiv信息分发模型，该模型具有三层结构：arXiv论文，信息传播者和信息收集者。首先，我们使用HITS算法来分析以用户为节点的arXiv信息扩散网络，该网络是由Twitter上关于arXiv论文的三种行为创建的：tweeting，retweeting和liked。接下来，我们使用Louvain方法从具有正权威和枢纽度的信息传播者网络中提取社区，并使用研究领域，语言和时间特征分析社区中信息传播者的关系和角色。从我们使用tweet和arXiv数据集的分析中，我们发现关于arXiv论文的信息在Twitter上从信息传播者传播到信息收集者，并且根据他们的研究领域形成了多个信息传播者社区。研究还发现，在同一研究领域中，根据信息传播者的研究或文化背景，形成了不同的社区。我们能够确定两类关键人物：在国际社会相关领域处于领先地位的信息传播者和使用英语及其母语在区域和国际社会之间架起桥梁的信息传播者。此外，我们发现，作为信息传播者，获得信任需要一些时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
