• DocumentCode
    2921098
  • Title

    Predicting short interval tracking polls with online social media

  • Author

    Li Ho Leung ; Ng, Vincent T. Y. ; Shiu, Simon C. K.

  • Author_Institution
    Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
  • fYear
    2013
  • fDate
    27-29 June 2013
  • Firstpage
    587
  • Lastpage
    592
  • Abstract
    The of behavioral patterns in online social media are often reflecting the happenings in our society. These patterns, which can be considered as opinions, are often correlated with public opinion polling. However, many correlation analyses done previously were for subsequent discoveries and not being able to handle short interval polling opinions. For opinions obtained from tracking polling with short opinion collection interval, like rolling polling, it cannot perform well in tracing the latest trends. This paper describes an extended correlation model for such kind of polling in examining the correlation between opinion in online social media and the public opinion from tracking poll. It has been tested with a recent rolling polling and it outperformed the previous correlation models.
  • Keywords
    correlation methods; social networking (online); behavioral patterns; extended correlation model; online social media; public opinion polling; rolling polling; short interval tracking polls prediction; short opinion collection interval; Blogs; Correlation; Data collection; Data models; Joining processes; Media; PSNR; online social media; opinion tracking; prediction; public polling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Supported Cooperative Work in Design (CSCWD), 2013 IEEE 17th International Conference on
  • Conference_Location
    Whistler, BC
  • Print_ISBN
    978-1-4673-6084-5
  • Type

    conf

  • DOI
    10.1109/CSCWD.2013.6581027
  • Filename
    6581027