Modular networks of word correlations on Twitter
Modular networks of word correlations on Twitter
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DOI：
10.1038/srep00814
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发表时间：
2012-11-08
影响因子：
4.6
通讯作者：
Jensen, Mogens H.
中科院分区：
文献类型：
--
作者：
Mathiesen, Joachim;Yde, Pernille;Jensen, Mogens H.
复杂网络是分析自然和人类社会许多方面信息流的重要工具。利用来自微博服务 Twitter 的数据，我们研究了国际品牌、名词和美国主要城市这三个不同类别的单词出现的相关性网络。我们创建的网络中，链接的强度是通过基于单词共现率的相似性度量来确定的。与零模型相比，假设单词不相关，配对相关性的重尾分布被证明是表示相似实体的单词组的结果。
Complex networks are important tools for analyzing the information flow in many aspects of nature and human society. Using data from the microblogging service Twitter, we study networks of correlations in the occurrence of words from three different categories, international brands, nouns and US major cities. We create networks where the strength of links is determined by a similarity measure based on the rate of co-occurrences of words. In comparison with the null model, where words are assumed to be uncorrelated, the heavy-tailed distribution of pair correlations is shown to be a consequence of groups of words representing similar entities.