Abstract: 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.

Key words: social media, Sina Weibo, information narrowing, information cocoon room, word embedding