Suppr超能文献

社交媒体上的自组织:内外爆发与基线波动。

Self-organization on social media: endo-exo bursts and baseline fluctuations.

作者信息

Oka Mizuki, Hashimoto Yasuhiro, Ikegami Takashi

机构信息

Department of Computer Science, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan.

Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan.

出版信息

PLoS One. 2014 Oct 16;9(10):e109293. doi: 10.1371/journal.pone.0109293. eCollection 2014.

Abstract

A salient dynamic property of social media is bursting behavior. In this paper, we study bursting behavior in terms of the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter. We found that there is a fluctuation threshold up to which the burst size increases as the fluctuation increases and that above the threshold, there appears a variety of burst sizes. We call this threshold the critical threshold. Investigating this threshold in relation to endogenous bursts and exogenous bursts based on peak ratio and burst size reveals that the bursts below this threshold are endogenously caused and above this threshold, exogenous bursts emerge. Analysis of the 3,000 keywords shows that all the nouns have both endogenous and exogenous origins of bursts and that each keyword has a critical threshold in the baseline fluctuation value to distinguish between the two. Having a threshold for an input value for activating the system implies that Twitter is an excitable medium. These findings are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, to predict the response to particular information on social media.

摘要

社交媒体的一个显著动态特性是爆发行为。在本文中,我们使用推特上3000个关键词的频率时间序列,从先前基线波动与连续爆发响应之间的时间关系角度研究爆发行为。我们发现存在一个波动阈值,在该阈值以下,爆发规模随着波动增加而增大,而在该阈值以上,则会出现各种不同的爆发规模。我们将这个阈值称为临界阈值。基于峰值比和爆发规模,对与内源性爆发和外源性爆发相关的这个阈值进行研究发现,低于该阈值的爆发是内源性引起的,而高于该阈值时,外源性爆发出现。对这3000个关键词的分析表明,所有名词都有爆发的内源性和外源性来源,并且每个关键词在基线波动值上都有一个临界阈值来区分这两者。对于激活系统的输入值有一个阈值意味着推特是一个可激发介质。这些发现对于刻画推特上一个关键词的可激发程度很有用,并且例如可用于预测对社交媒体上特定信息的响应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6680/4199606/a428fc1bb778/pone.0109293.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验