• DocumentCode
    116419
  • Title

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

  • Author

    Lumin Zhang ; Jian Pei ; Yan Jia ; Bin Zhou ; Xiang Wang

  • Author_Institution
    Sch. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2014
  • fDate
    17-20 Aug. 2014
  • Firstpage
    208
  • Lastpage
    215
  • 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.
  • Keywords
    data analysis; social networking (online); SINA Weibo data; buddy triangles; buddyless neighbors; reblog likelihood; reblog messages; Blogs; Conferences; Educational institutions; Facebook; Twitter; Unsolicited electronic mail; Reblog; neighborhood; online social networks; retweet;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ASONAM.2014.6921585
  • Filename
    6921585