Characterizing popularity dynamics of online videos

被引:15
|
作者
Ren, Zhuo-Ming [1 ]
Shi, Yu-Qiang [2 ]
Liao, Hao [1 ,3 ]
机构
[1] Univ Fribourg, Dept Phys, Chemin Musee 3, CH-1700 Fribourg, Switzerland
[2] Southwest Univ Sci & Technol, Sch Mfg Sci & Engn, Mianyang 621010, Peoples R China
[3] Shenzhen Univ, Coll Comp Sci & Software Engn, Guangdong Prov Key Lab Popular High Performance C, Shenzhen 518060, Peoples R China
基金
瑞士国家科学基金会;
关键词
Popularity dynamic; Online video; Burst behavior; MODEL;
D O I
10.1016/j.physa.2016.02.019
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Online popularity has a major impact on videos, music, news and other contexts in online systems. Characterizing online popularity dynamics is nature to explain the observed properties in terms of the already acquired popularity of each individual. In this paper, we provide a quantitative, large scale, temporal analysis of the popularity dynamics in two online video-provided websites, namely MovieLens and Netflix. The two collected data sets contain over 100 million records and even span a decade. We characterize that the popularity dynamics of online videos evolve over time, and find that the dynamics of the online video popularity can be characterized by the burst behaviors, typically occurring in the early life span of a video, and later restricting to the classic preferential popularity increase mechanism. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:236 / 241
页数:6
相关论文
共 50 条
  • [1] Characterizing and Predicting the Popularity of Online Videos
    Li, Chenyu
    Liu, Jun
    Ouyang, Shuxin
    IEEE ACCESS, 2016, 4 : 1630 - 1641
  • [2] Characterizing and Modeling the Dynamics of Online Popularity
    Ratkiewicz, Jacob
    Fortunato, Santo
    Flammini, Alessandro
    Menczer, Filippo
    Vespignani, Alessandro
    PHYSICAL REVIEW LETTERS, 2010, 105 (15)
  • [3] Lifetime Popularity Prediction for Online Videos
    Tan, Zhiyi
    Zhang, Ya
    Li, Chaofeng
    Liu, Ning
    2014 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB), 2014,
  • [4] Characterizing and modelling popularity of user-generated videos
    Borghol, Youmna
    Mitra, Siddharth
    Ardon, Sebastien
    Carlsson, Niklas
    Eager, Derek
    Mahanti, Anirban
    PERFORMANCE EVALUATION, 2011, 68 (11) : 1037 - 1055
  • [5] Reproducing Popularity Dynamics of YouTube Videos
    Kamiyama, Noriaki
    Murata, Masayuki
    2018 14TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), 2018, : 205 - 211
  • [6] A Peek Into the Future: Predicting the Popularity of Online Videos
    Ouyang, Shuxin
    Li, Chenyu
    Li, Xueming
    IEEE ACCESS, 2016, 4 : 3026 - 3033
  • [7] Characterizing and Modeling the Dynamics of Activity and Popularity
    Zhang, Peng
    Li, Menghui
    Gao, Liang
    Fan, Ying
    Di, Zengru
    PLOS ONE, 2014, 9 (02):
  • [8] Exploring Popularity Predictability of Online Videos With Fourier Transform
    Zhou, Yan
    Wu, Zhanpeng
    Zhou, Yipeng
    Hu, Miao
    Yang, Chunfeng
    Qin, Jing
    IEEE ACCESS, 2019, 7 : 41823 - 41834
  • [9] On Popularity Prediction of Videos Shared in Online Social Networks
    Li, Haitao
    Ma, Xiaoqiang
    Wang, Feng
    Liu, Jiangchuan
    Xu, Ke
    PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), 2013, : 169 - 178
  • [10] Predicting Popularity of Online Videos Using Support Vector Regression
    Trzcinski, Tomasz
    Rokita, Przemyslaw
    IEEE TRANSACTIONS ON MULTIMEDIA, 2017, 19 (11) : 2561 - 2570