NDLI: Do neighbor buddies make a difference in reblog likelihood? An analysis on SINA Weibo data

Content Provider	IEEE Xplore Digital Library
Author	Lumin Zhang Jian Pei Yan Jia Bin Zhou Xiang Wang
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada (Jian Pei) \|\| Sch. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China (Lumin Zhang; Yan Jia; Bin Zhou; Xiang Wang)
Abstract	Reblogging, also known as retweeting in Twitter parlance, is a major type of activities in many online social networks. Although there are many studies on reblogging behaviors and potential applications, whether neighbors who are well connected with each other (called “buddies” in our study) may make a difference in reblog likelihood has not been examined systematically. In this paper, we tackle the problem by conducting a systematic statistical study on a large SINA Weibo data set, which is a sample of 135, 859 users, 10, 129, 028 followers, and 2, 296, 290, 930 reblog messages in total. To the best of our knowledge, this data set has more reblog messages than any data sets reported in literature. We examine a series of hypotheses about how essential neighborhood structures may help to boost the likelihood of reblogging, including buddy neighbors versus buddyless neighbors, traffic between buddy neighbors, activeness (i.e., the total number of blog messages a user sends), and the number of buddy triangles a user participates in. Our empirical study discloses several interesting phenomena that are not reported in literature, which may imply interesting and valuable new applications.
Starting Page	208
Ending Page	215
File Size	272132
Page Count	8
File Format	PDF
ISBN	9781479958771
DOI	10.1109/ASONAM.2014.6921585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Conferences retweet Blogs Unsolicited electronic mail online social networks Twitter Educational institutions neighborhood Facebook Reblog
Content Type	Text
Resource Type	Article

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