Abstract
In the era of big data, group division in online social network analysis is a basic task. It can be divided into the group division based on static relationship and the group division based on dynamic relationship. Compared with the static group division, users express their semantic information in all kinds of social network behaviors, and they tend to interact with other users who have the same idea and attitude; this is how different groups are formed. In this paper, aimed at the issue that some Tibetan users use Chinese to publish microblogs on social platforms, a group division method based on semantic information of Tibetan users under the big data environment is proposed. When dividing a large number of Tibetan user groups in a social network, a large amount of semantic information of Tibetan users in the social network is first analyzed. Then, based on the semantic similarity between users, we aggregate the Tibetan users with high similarities into one group, thus achieving the final group division. The experimental results illustrate the effectiveness of the method of analyzing Tibetan user semantic information in the context of big data for group partitioning.
Highlights
With the rapid development of the Internet and the arrival of the era of big data, digital social network websites have attracted more and more people to participate in online social networking, and people’s communication is no longer blocked by time and space barriers
This paper differs from other semantic partitioning algorithms in that we fully consider the various behaviors of users in online social networks who serve to express their views, attitudes, and emotions; this paper proposes a Tibetan microblog user group division algorithm based on user semantic information in the age of big data
Build the network structure based on Weibo user semantic information, and use the proposed group division algorithm based on similarity degree to divide the group
Summary
With the rapid development of the Internet and the arrival of the era of big data, digital social network websites have attracted more and more people to participate in online social networking, and people’s communication is no longer blocked by time and space barriers. The enormous user scale, the large amount of data, and the complex network structure have posed great challenges to the research of social networks Under such a big data environment, it is very valuable to study an effective method of group division social network users. The above algorithms give the realization methods of semantic community division in complex networks It ignores the use of the probability topic model to divide the network without considering the overall network topology; it cannot be reasonably explained by the corresponding real groups after getting the network division structure. This paper differs from other semantic partitioning algorithms in that we fully consider the various behaviors of users in online social networks who serve to express their views, attitudes, and emotions; this paper proposes a Tibetan microblog user group division algorithm based on user semantic information in the age of big data
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