Infer the probability of read in microblogs

Author

Zhaoyun Ding ; Bingying Xu ; Lei Deng ; Hui Zhao ; Yan Jia ; Bin Zhou

Author_Institution

Sch. of Comput., Nat. Univ. of Defense Technol., Changsha, China

fYear

2012

fDate

18-20 Oct. 2012

Firstpage

274

Lastpage

277

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.

Keywords

probability; social networking (online); time series; Twitter; daily time series model; followers; microblogs; personal page browsing; public timeline; read probability; tweets;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-1743-6

Type

conf

DOI

10.1109/ICACI.2012.6463166

Filename

6463166