• DocumentCode
    261018
  • Title

    Social data analysis for predicting next event

  • Author

    Deiva Ragavi, M. ; Usharani, S.

  • Author_Institution
    Dept. of CSE, Anna Univ., Chennai, India
  • fYear
    2014
  • fDate
    27-28 Feb. 2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Twitter is a user-friendly social network which deserves its real-time nature. With the help of an algorithm, the investigation can be made with regard to some of the real-time events such as earthquake. The target event is assumed and classified based on the keywords, number of words and their context. The probabilistic spatiotemporal model is provided which can find the Centre of the event location. The Twitter users are regarded as sensors and apply particle filter, mainly used for detecting the location. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability merely by monitoring tweets. Our system detects earthquakes promptly and notification much faster than JMA (Japan Meteorological Agency) broadcast announcements.
  • Keywords
    earthquakes; geophysics computing; human computer interaction; particle filtering (numerical methods); probability; real-time systems; social networking (online); JMA; Japan Meteorological Agency broadcast announcement; Twitter; earthquakes; event location; location detection; monitoring tweets; particle filter; probabilistic spatiotemporal model; probability; real-time events; real-time nature; social data analysis; user-friendly social network; Earthquakes; Educational institutions; Electronic mail; Event detection; Real-time systems; Sensors; Twitter; Data Mining; Social Data; Social Media; Text Mining; Tweets; Twitter; earthquake; event detection; social sensor;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Communication and Embedded Systems (ICICES), 2014 International Conference on
  • Conference_Location
    Chennai
  • Print_ISBN
    978-1-4799-3835-3
  • Type

    conf

  • DOI
    10.1109/ICICES.2014.7033935
  • Filename
    7033935