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Identifying the Influential User Based on User Interaction Model for Twitter Data

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1209))

Abstract

In the social network, the user Interaction model is used to measure the interest depending on the interaction behavior between different entities. An entity or node can be people, groups or organizations and the links or edges are shown to represent the relationship between them. Edges are used to identify whether there is communication or social interaction between different users. Based on the user’s interest or activities made in a social group, it is used to identify whether the interaction in the network is active or inactive. Social media is one of the fast-growing, dynamic and unpredictable in detecting future influencers which becomes harder in the social group. The features identified from the user interaction model and thus the potential influencers and current influencers are identified. Incidence matrix is used to identify the interaction behavior between different users in a social group. Thus, the incidence edges play an essential role in the vertex-edge incidence matrix interaction to identify the active and inactive user interactions in a social network. The Fraction of Strongly Influential (FSI) users score is used to evaluate the user interaction model by analyzing the influential user’s interest and thus the top trending user interests are achieved.

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Acknowledgement

This publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya Ph.D. Scheme of Ministry of electronics of Info. Technology, Government of India, being implemented by Digital India Corporation (formerly media Lab Asia).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai, Tamil Nadu, India

    C. Suganthini & R. Baskaran

Authors
  1. C. Suganthini
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  2. R. Baskaran
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Corresponding author

Correspondence to R. Baskaran .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Trivandrum, India

    Sabu M. Thampi

  2. Indian Institute of Technology Kanpur, Kanpur, India

    Rajesh M. Hegde

  3. Ryerson University, Toronto, ON, Canada

    Sri Krishnan

  4. Indian Institute of Technology Kharagpur, Kharagpur, India

    Jayanta Mukhopadhyay

  5. State University of New York at Buffalo, Buffalo, NY, USA

    Vipin Chaudhary

  6. Florida Atlantic University, Boca Raton, FL, USA

    Oge Marques

  7. University of Florida, Gainesville, FL, USA

    Selwyn Piramuthu

  8. University of Salamanca, Salamanca, Spain

    Juan M. Corchado

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Suganthini, C., Baskaran, R. (2020). Identifying the Influential User Based on User Interaction Model for Twitter Data. In: Thampi, S., et al. Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2019. Communications in Computer and Information Science, vol 1209. Springer, Singapore. https://doi.org/10.1007/978-981-15-4828-4_5

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  • DOI: https://doi.org/10.1007/978-981-15-4828-4_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4827-7

  • Online ISBN: 978-981-15-4828-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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