• DocumentCode
    3747048
  • Title

    Twitter knows: Understanding the emergence of topics in social networks

  • Author

    Lachlan Birdsey;Claudia Szabo;Yong Meng Teo

  • Author_Institution
    School of Computer Science, The University of Adelaide, Australia
  • fYear
    2015
  • Firstpage
    4009
  • Lastpage
    4020
  • Abstract
    Social networks such as Twitter and Facebook are important and widely used communication environments that exhibit scale, complexity, node interaction, and emergent behavior. In this paper, we analyze emergent behavior in Twitter and propose a definition of emergent behavior focused on the pervasiveness of a topic within a community. We extend an existing stochastic model for user behavior, focusing on advocate-follower relationships. The new user posting model includes retweets, replies, and mentions as user responses. To capture emergence, we propose a RPBS (Rising, Plateau, Burst and Stabilization) topic pervasiveness model with a new metric that captures how frequent and in what form the community is talking about a particular topic. Our initial validation compares our model with four Twitter datasets. Our extensive experimental analysis allows us to explore several “what-if” scenarios with respect to topic and knowledge sharing, showing how a pervasive topic evolves given various popularity scenarios.
  • Keywords
    "Twitter","Tagging","Market research","Feeds","Predictive models","Focusing"
  • Publisher
    ieee
  • Conference_Titel
    Winter Simulation Conference (WSC), 2015
  • Electronic_ISBN
    1558-4305
  • Type

    conf

  • DOI
    10.1109/WSC.2015.7408555
  • Filename
    7408555