{
  "id": "1403.5603",
  "version": "v1",
  "published": "2014-03-22T02:15:39.000Z",
  "updated": "2014-03-22T02:15:39.000Z",
  "title": "Forecasting Popularity of Videos using Social Media",
  "authors": [
    "Jie Xu",
    "Mihaela van der Schaar",
    "Jiangchuan Liu",
    "Haitao Li"
  ],
  "categories": [
    "cs.LG",
    "cs.SI"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-03-22T02:15:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "social media",
      "forecasting popularity",
      "social-forecast",
      "systematic online prediction method",
      "largest online social networks"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "doi": "10.1109/JSTSP.2014.2370942",
      "journal": "IEEE Journal of Selected Topics in Signal Processing",
      "year": 2015,
      "month": "Mar",
      "volume": 9,
      "number": 2,
      "pages": 330
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2015ISTSP...9..330X"
    }
  }
}