• DocumentCode
    1824226
  • Title

    TopicFlow: Visualizing topic alignment of Twitter data over time

  • Author

    Malik, S. ; Smith, A. ; Hawes, Timothy ; Papadatos, Panagis ; Jianyu Li ; Dunne, Cody ; Shneiderman, Ben

  • Author_Institution
    Univ. of Maryland, College Park, MD, USA
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    720
  • Lastpage
    726
  • Abstract
    Social media, particularly Twitter, provides an abundance of real-time data. To account for this volume, researchers often use automated analysis and visualization techniques to produce a high-level overview of a Twitter stream. Existing techniques for understanding Twitter data make use of hashtags or word-pairs and may ignore the complex trends in discussions over time. To remedy this, we present an application of statistical topic modeling and alignment (binned topic models) to group related tweets into automatically generated topics and TopicFlow, an interactive tool to visualize the evolution of these topics. The effectiveness of this visualization for reasoning about large data sets is demonstrated by a usability study with 18 participants.
  • Keywords
    data visualisation; social networking (online); statistical analysis; TopicFlow; Twitter data; Twitter stream; automated analysis; data visualization; hashtags; interactive tool; real-time data; social media; statistical topic modeling; topic alignment; visualization techniques; word-pairs; Analytical models; Conferences; Data models; Data visualization; Market research; Measurement; Twitter;
  • 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
    6785782