On popularity prediction of videos shared in online social networks

  • Authors:

  • Haitao Li;Xiaoqiang Ma;Feng Wang;Jiangchuan Liu;Ke Xu

  • Affiliations:

  • Simon Fraser University, Burnaby, BC, Canada;Simon Fraser University, Burnaby, BC, Canada;The University of Mississippi, University, MS, USA;Simon Fraser University, Burnaby, BC, Canada;Tsinghua University, Beijing, China

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Popularity prediction, with both technological and economic importance, has been extensively studied for conventional video sharing sites (VSSes), where the videos are mainly found via searching, browsing, or related links. Recent statistics however suggest that online social network (OSN) users regularly share video contents from VSSes, which has contributed to a significant portion of the accesses; yet the popularity prediction in this new context remains largely unexplored. In this paper, we present an initial study on the popularity prediction of videos propagated in OSNs along friendship links. We conduct a large-scale measurement and analysis of viewing patterns of videos shared in one of largest OSNs in China, and examine the performance of typical views-based prediction models. We find that they are generally ineffective, if not totally fail, especially when predicting the early peaks and later bursts of accesses, which are common during video propagations in OSNs. To overcome these limits, we track the propagation process of videos shared in a Facebook-like OSN in China, and analyze the user viewing and sharing behaviors. We accordingly develop a novel propagation-based video popularity prediction solution, namely SoVP. Instead of relying solely on the early views for prediction, SoVP considers both the intrinsic attractiveness of a video and the influence from the underlying propagation structure. The effectiveness of SoVP, particularly for predicting the peaks and bursts, have been validated through our trace-driven experiments.