An Optimization Approach for Sub-event Detection and Summarization in Twitter

  • Polykarpos MeladianosEmail author
  • Christos Xypolopoulos
  • Giannis Nikolentzos
  • Michalis Vazirgiannis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)


In this paper, we present a system that generates real-time summaries of events using only posts collected from Twitter. The system both identifies important moments within the event and generates a corresponding textual description. First, the set of tweets posted in a short time interval is represented as a weighted graph-of-words. To identify important moments within an event, the system detects rapid changes in the graphs’ edge weights using a convex optimization formulation. The system then extracts a few tweets that best describe the chain of interesting occurrences in the event using a greedy algorithm that maximizes a nondecreasing submodular function. Through extensive experiments on real-world sporting events, we show that the proposed system can effectively capture the sub-events, and that it clearly outperforms the dominant sub-event detection method.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Polykarpos Meladianos
    • 1
    • 2
    Email author
  • Christos Xypolopoulos
    • 1
  • Giannis Nikolentzos
    • 1
    • 2
  • Michalis Vazirgiannis
    • 1
    • 2
  1. 1.Lix, École PolytechniquePalaiseauFrance
  2. 2.Athens University of Economics and BusinessAthensGreece

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