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Regression Methods in the Authority Identification Within Web Discussions

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Computational Collective Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9329))

Abstract

The paper describes the problem of authority identification within web discussions solving using linear and nonlinear regression methods. The goal is to find an approximation of dependency of the authority value on variables representing parameters of the structure and particularly the content of selected web discussions. The approximation function can be used at first for computation of the authority value of a given discussant, at second, for discrimination of an authoritative discussant from non-authoritative contributors to the web discussion. This information is important for web users, who search for truthful and reliable information in the process of decision making about important things. The web users would like to be influenced by some credible professionals. The various regression methods were tested. The best solution was implemented in the Application for the Machine Authority Identification.

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Author information

Authors and Affiliations

  1. Department of Cybernetics and Artificial Intelligence, Technical University, Letná 9, 042 00, Košice, Slovakia

    Kristína Machová & Jaroslav Štefaník

Authors
  1. Kristína Machová
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  2. Jaroslav Štefaník
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Corresponding author

Correspondence to Kristína Machová .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wrocław University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wrocław University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Machová, K., Štefaník, J. (2015). Regression Methods in the Authority Identification Within Web Discussions. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-24069-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24068-8

  • Online ISBN: 978-3-319-24069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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