基于Word2vec的信息窄化测度及影响因素研究

Research on Measurement and Influencing Factors of Information Narrowing Based on Word2vec

为清晰而明确地掌握社交媒体使用与用户信息窄化的关系及其作用程度,选取典型的社交媒体之一新浪微博(N=7 825),分析微博使用度、活跃度、影响度的现实指标所伴随的用户信息窄化。从两方面实证考量用户内容在多种使用指标中的信息窄化。结合配对样本t检验的结果显示,微博媒介的使用程度越高的用户层级,其语义上的自我相似度越高,内容类型的分布均衡程度和丰富程度越低。

In order to understand the relationship between social media usage and information cocoon,this research takes Sina Weibo as an example,to analyze information cocoon accompanied by Weibo usage,activity,and impact. We use Word2vec,one of accessible NLP( Natural Language Processing) technology of word embedding,and k-means,a kind of clustering method,to explore the information cocoon and narrowing scope.The result of statistical paired T test shows that,as the development of users’ level in social media,there is a remarkable trend of rising in semantic similarity of UGC( User Generated Content). The distribution and richness of content categories will also decrease accordingly. The result inspires us to rethink the relation between social media usage and information cocoon. The classification of users does not bring more flexible discourse space.Rather,deeper,and higher users suffer more from similar content.