Infer the probability of read in microblogs

Abstract

In microblogs contexts like Twitter, a large number of users follow others. In case the author is not protecting his tweets, they appear in the so-called public timeline and his followers will receive all the messages from him. However, if followers of the author do not browse the personal page of the author, or they do not browse the timeline of themselves, they will not read messages of the author. So, followers of the author could not read all messages of the author. In this paper, we will infer the probability of read in microblogs according to the daily time-series model of posting and the similarity of personal interest. Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 2.7 million tweets. Experimental results indicate that out method is effective to infer the probability of read in microblogs.