Forecasting Popularity of Videos Using Social Media

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

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-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 short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct 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 view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.

Original languageEnglish (US)
Article number6955832
Pages (from-to)330-343
Number of pages14
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number2
StatePublished - Mar 2015
Externally publishedYes


  • Forecasting algorithm
  • online learning
  • online social networks
  • popularity prediction
  • situational and contextual awareness
  • social media

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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