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Bursty Event Detection Model for Twitter

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Distributed Computing and Intelligent Technology (ICDCIT 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14501))

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Abstract

Huge amount of diversified information in the form of multimedia data gets uploaded to Online Social Network platform every second. This eventually gets a sudden burst during high impact events. Twitter platform plays a very important role during these events in the process of diffusion of this information across the entire social network of users. The real challenge is in the analysis of tweet during these bursty events when data gets generated in large volume with high arrival rate. Under this circumstances, near real-time detection of bursty event should be implemented to match up the speed of the information diffusion which demands efficient algorithms. In this paper a bursty event detection algorithm is proposed which considers a dynamic set of tweets in every time window and generates optimal k topics per window of a bursty event. This research has also studied the goodness of the topics produced across the different time windows. Our proposed model is successful in creating better semantically coherent and contextual topics for bursty event as compared to the other state of the art techniques such as Latent Dirichlet Allocation Model, Gibbs Sampling Dirichlet Mixture Model and Gamma-Poisson Mixture Topic Model.

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Author information

Authors and Affiliations

  1. Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India

    Anuradha Goswami

  2. Symbiosis Centre for Information Technology, Symbiosis International (Deemed University), Pune, India

    Ajey Kumar & Dhanya Pramod

Authors
  1. Anuradha Goswami
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  2. Ajey Kumar
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  3. Dhanya Pramod
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Corresponding author

Correspondence to Anuradha Goswami .

Editor information

Editors and Affiliations

  1. University of Picardie Jules Verne, Amiens, France

    Stéphane Devismes

  2. Indian Institute of Technology Guwahati, Guwahati, India

    Partha Sarathi Mandal

  3. Indian Institute of Technology Guwahati, Guwahati, India

    V. Vijaya Saradhi

  4. Florida Agricultural and Mechanical University, Tallahassee, FL, USA

    Bhanu Prasad

  5. Indian Statistical Institute, Kolkata, India

    Anisur Rahaman Molla

  6. Kent State University, Kent, OH, USA

    Gokarna Sharma

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Goswami, A., Kumar, A., Pramod, D. (2024). Bursty Event Detection Model for Twitter. In: Devismes, S., Mandal, P.S., Saradhi, V.V., Prasad, B., Molla, A.R., Sharma, G. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2024. Lecture Notes in Computer Science, vol 14501. Springer, Cham. https://doi.org/10.1007/978-3-031-50583-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-50583-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50582-9

  • Online ISBN: 978-3-031-50583-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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