• DocumentCode
    43459
  • Title

    Forecasti ng Virality [Dataflow]

  • Author

    Anderson, Matthew

  • Volume
    51
  • Issue
    4
  • fYear
    2014
  • fDate
    Apr-14
  • Firstpage
    76
  • Lastpage
    76
  • Abstract
    Memes, like Rickrolling or LOLcats, are the invasive species of social network ecosystems such as Facebook and Twitter. "Viral hashtags are so interesting that even at first sight, you just start to use them," says Yong-Yeol Ahn, assistant professor at Indiana University\´s School of Informatics and Computing. Ahn and his coauthors have isolated the network properties of memes and turned them into a forecasting tool, enabling the prediction of which Twitter hashtags will go viral nearly two out of three times based on how the hashtag is shared in its early stages. Ahn says later this spring they\´ll be publishing follow-up research that looks at predicting just how big a splash a viral meme will make.
  • fLanguage
    English
  • Journal_Title
    Spectrum, IEEE
  • Publisher
    ieee
  • ISSN
    0018-9235
  • Type

    jour

  • DOI
    10.1109/MSPEC.2014.6776315
  • Filename
    6776315