• DocumentCode
    1791782
  • Title

    The exceptional and the everyday: 144 Hours in Kiev

  • Author

    Manovich, Lev ; Tifentale, Alise ; Yazdani, Mojtaba ; Chow, Joe

  • Author_Institution
    Grad. Center, City Univ. of New York, New York, NY, USA
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    72
  • Lastpage
    79
  • Abstract
    How can we use computational analysis and visualization of content and interactions on social media network to write histories? Traditionally, historical timelines of social and political upheavals give us only distant views of the events, and singular interpretation of a person constructing the timeline. However, using social media as our source, we can potentially present many thousands of individual views of the events. We can also include representation of the everyday life next to the accounts of the exceptional events. This paper explores these ideas using a particular case study - images shared by people in Kiev on Instagram during 2014 Ukranian Revolution. Using Instagram public API we collected 13208 geo-coded images shared by 6165 Instagram users in the central part of Kiev during February 17-22, 2014. We used open source and our own custom software tools to analyze the images along with upload dates and times, geo locations, and tags, and visualize them in different ways.
  • Keywords
    application program interfaces; data analysis; data visualisation; public domain software; social networking (online); social sciences computing; 2014 Ukranian Revolution; Instagram public API; Kiev; computational analysis; content visualization; geo-coded images; histories; image analysis; open source software tools; social media network; Cities and towns; Data visualization; Facebook; Government; Media; Twitter; Visualization; Instagram; digital humanities; photography; social media; social movements;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004456
  • Filename
    7004456