• DocumentCode
    1821509
  • Title

    Some computational challenges in mining social media

  • Author

    Huan Liu

  • Author_Institution
    Data Min. & Machine Learning Lab., Arizona State Univ., Tempe, AZ, USA
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Abstract
    People of all walks of life use social media for communications and networking. Their active participation in numerous and diverse online activities continually generates massive amounts of social media data. This undoubtedly “big” data presents new challenges to data mining, including how to select salient features for social media data with varied relations, how to assess user vulnerability, and how to ensure that patterns discovered from social media data are valid when no ground truth is available. We will illustrate the intricacies of social media data, present original social-computing problems, deliberate approaches to mining social media data to gain insight from real-world applications and deepen our understanding, and exploit unique characteristics of social media data in developing novel algorithms and computational tools for social media mining.
  • Keywords
    data mining; social networking (online); data mining; salient feature; social media; social-computing problem; user vulnerability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
  • Conference_Location
    Niagara Falls, ON
  • Type

    conf

  • Filename
    6785672