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International Conference on Network and System Security

NSS 2017: Network and System Security pp 427–438Cite as

A Systematic Analysis of Random Forest Based Social Media Spam Classification

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10394))

Abstract

Recently random forest classification became a popular choice machine learning applications aimed to detect spam content in online social networks. In this paper, we report a systematic analysis of random forest classification for this purpose. We assessed the impact of key parameters, such as number of trees, depth of trees and minimum size of leaf nodes on classification performance. Our results show that controlling the complexity of random forest classifiers applied to social media spam is important in order to avoid overfitting and optimize performance We also conclude that in order to support reproducibility of experimental results it is important to report key parameters of random forest classifiers.

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Authors and Affiliations

  1. School of Computing and Mathematics, Keele University, Newcastle-Under-Lyme, UK

    Mohammed Al-Janabi & Peter Andras

Authors
  1. Mohammed Al-Janabi
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  2. Peter Andras
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Corresponding author

Correspondence to Mohammed Al-Janabi .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Zheng Yan

  2. Eurecom, Sophia Antipolos, Valbonne, France

    Refik Molva

  3. Warsaw University of Technology, Warsaw, Poland

    Wojciech Mazurczyk

  4. Aalto University, Espoo, Finland

    Raimo Kantola

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Al-Janabi, M., Andras, P. (2017). A Systematic Analysis of Random Forest Based Social Media Spam Classification. In: Yan, Z., Molva, R., Mazurczyk, W., Kantola, R. (eds) Network and System Security. NSS 2017. Lecture Notes in Computer Science(), vol 10394. Springer, Cham. https://doi.org/10.1007/978-3-319-64701-2_31

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  • DOI: https://doi.org/10.1007/978-3-319-64701-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64700-5

  • Online ISBN: 978-3-319-64701-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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