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A Feature Selection Approach to Detect Spam in the Facebook Social Network

  • Research Article - Computer Engineering and Computer Science
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Abstract

The widespread adoption of social networks and their enormous facilities and growing opportunities has attracted many users and audience. But along with attractive and interesting messages and topics, inappropriate and sometimes criminal contents, such as spam, are also released on these networks. Malicious spammers intend to send inaccurate or irrelevant contents to distribute malformed information on online social networks. This paper is about the spam comments detection on the Facebook social network. By reviewing the posts and comments, and studying their features, an online spam filtering system has been designed in this paper. The proposed filtering system is able to exploit various exploration methods and optimization algorithms such as simulated annealing, particle swarm optimization, ant colony optimization, and differential evolution to detect and filter malicious contents and to prevent publishing spam comments to provide a secure environment for users of this popular social network. Furthermore, supervised machine learning methods, clustering techniques, and decision trees have been exploited to provide an accurate performance and appropriate speed for the proposed filtering system.

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Authors and Affiliations

  1. Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

    Mohammad Karim Sohrabi & Firoozeh Karimi

Authors
  1. Mohammad Karim Sohrabi
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  2. Firoozeh Karimi
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Correspondence to Mohammad Karim Sohrabi.

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Sohrabi, M.K., Karimi, F. A Feature Selection Approach to Detect Spam in the Facebook Social Network. Arab J Sci Eng 43, 949–958 (2018). https://doi.org/10.1007/s13369-017-2855-x

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  • DOI: https://doi.org/10.1007/s13369-017-2855-x

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