• DocumentCode
    683953
  • Title

    Iterative algorithm for retweeting prediction in Twitter

  • Author

    Li, Yingle ; Wu, Zhen ; Yu, Hongtao ; Liu, Lixiong

  • Author_Institution
    National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, Henan, China
  • fYear
    2013
  • fDate
    23-25 March 2013
  • Firstpage
    541
  • Lastpage
    545
  • Abstract
    Twitter is emerging social media. There is an important practical significance to study its communication effect in many aspects of the Marketing Management, Public Sentiment, Hot Extraction and so on. The retweeting scale is an important indicator to reflect the communication effect. Based on analyzing the factors, a prediction algorithm based on SVM was proposed for retweeting with five features: publisher influence, acceptor activity, interest level, content importance and intimacy. Furthermore, the prediction algorithm for retweeting scale was proposed based above, the method to evaluate the prediction accuracy was given. At last the experiment with Twitter data showed a good result that the prediction accuracy of retweeting scale was up to 86.63%.
  • Keywords
    Accuracy; Classification algorithms; Feature extraction; Media; Prediction algorithms; Support vector machines; Twitter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Technology (ICIST), 2013 International Conference on
  • Conference_Location
    Yangzhou
  • Print_ISBN
    978-1-4673-5137-9
  • Type

    conf

  • DOI
    10.1109/ICIST.2013.6747607
  • Filename
    6747607