Timely video popularity forecasting based on social networks

Jie Xu, Mihaela Van Der Schaar, Jiangchuan Liu, Haitao Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


This paper presents Pop-Forecast, a systematic method for accurately forecasting the popularity of videos promoted through social networks. Pop-Forecast aims to optimize the forecasting accuracy and the timeliness with which forecasts are issued, by explicitly taking into account the dynamic propagation of videos in social networks. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the performance loss of Pop-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its fast rate of convergence as well as its asymptotic convergence to the optimal performance. We validate the performance of Pop-Forecast through extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing approaches for popularity prediction (which do not take into account the propagation in social network) by more than 30% in terms of prediction rewards.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE INFOCOM
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Print)9781479983810
StatePublished - Aug 21 2015
Externally publishedYes
Event34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong
Duration: Apr 26 2015May 1 2015


Other34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Country/TerritoryHong Kong
CityHong Kong

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


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