Reply trees in Twitter: data analysis and branching process models
Reply trees in Twitter: data analysis and branching process models
复制标题
Twitter 中的回复树：数据分析和分支过程模型
DOI：
10.1007/s13278-016-0334-0
复制
发表时间：
2016
影响因子：
2.8
通讯作者：
Naoki Masuda
中科院分区：
文献类型：
--
作者：
Ryosuke Nishi;Taro Takaguchi;Keigo Oka;Takanori Maehara;Masashi Toyoda;Ken-ichi Kawarabayashi;Naoki Masuda
网络的结构，从提到网络媒体中的帖子之间的关系构建可能是有价值的了解如何在这些媒体中传播的信息和意见。我们抓取Twitter来收集推文和回复，以构建大量所谓的回复树，每个回复树都植根于一条推文，并由回复连接。与以往的文献一致，我们发现，经验树的特点是一些长的路径状回复树，大星状树，长不规则树，虽然他们的频率不高。我们测试了几个分支过程模型来解释这些类型回复树的经验频率以及更基本的量，例如回复树的大小和深度的分布。基于我们的建模结果，我们建议发起回复树的推文的入度（即，推文被其他回复帖子直接提及的次数）可以在形成回复树的全局形状中起重要作用。
Structure of networks constructed from mentioning relationships between posts in online media may be valuable for understanding how information and opinions spread in these media. We crawled Twitter to collect tweets and replies to construct a large number of so-called reply trees, each of which was rooted at a tweet and joined by replies. Consistent with the previous literature, we found that the empirical trees were characterized by some long path-like reply trees, large star-like trees, and long irregular trees, although their frequencies were not high. We tested several branching process models to explain the empirical frequency of these types of reply trees as well as more basic quantities such as the distributions of the size and depth of the reply tree. Based on our modeling results, we suggest that the in-degree of the tweet that initiates a reply tree (i.e., the number of times that the tweet is directly mentioned by other reply posts) may play an important role in forming the global shape of the reply tree.