The First International Conference on IoT in Urban Space

Research Article

Classifying Urban Events' Popularity by Analyzing Friends Information in Location-based Social Network

  • @INPROCEEDINGS{10.4108/icst.urb-iot.2014.257295,
        author={Makoto Kawano and Takuro Yonezawa and Jin Nakazawa and Satoshi Kawasaki and Ken Ohta and Hiroshi Inamura and Hideyuki Tokuda},
        title={Classifying Urban Events' Popularity by Analyzing Friends Information in Location-based Social Network},
        proceedings={The First International Conference on IoT in Urban Space},
        publisher={ACM},
        proceedings_a={URB-IOT},
        year={2014},
        month={11},
        keywords={urban event classification location-based social network},
        doi={10.4108/icst.urb-iot.2014.257295}
    }
    
  • Makoto Kawano
    Takuro Yonezawa
    Jin Nakazawa
    Satoshi Kawasaki
    Ken Ohta
    Hiroshi Inamura
    Hideyuki Tokuda
    Year: 2014
    Classifying Urban Events' Popularity by Analyzing Friends Information in Location-based Social Network
    URB-IOT
    ICST
    DOI: 10.4108/icst.urb-iot.2014.257295
Makoto Kawano1,*, Takuro Yonezawa2, Jin Nakazawa2, Satoshi Kawasaki3, Ken Ohta3, Hiroshi Inamura3, Hideyuki Tokuda2
  • 1: The University of Tokyo, Keio University
  • 2: Keio University
  • 3: NTT DOCOMO, INC.
*Contact email: makora@ht.sfc.keio.ac.jp

Abstract

Recent progress and spread of smartphones and social network services have enabled us to transmit text messages with GPS location data anywhere and anytime. Since these location-based SNS messages often refer to urban events, many researchers have tried to recognize urban events by analyzing of the messages. To construct the various applications based on the urban events information, we propose a new indicator of event, called Popularity which represents how popular the urban event is. Popularity is estimated by analyzing friends on social network of events' participants. To evaluate our new indicator, we designed and implemented intuitive and interactive web-based tool for analyzing Popularity of events. Through comparative experiments, we confirmed that our proposed method could provide a certain amount of accuracy for estimating Popularity of events.