An Effective Twitter Spam Detection Model using Multiple Hidden Layers Extreme Learning Machine

Authors

  • K. Maithili Associate Professor,Department of CSE,KG REDDY COLLEGE OF ENGINEERING &TECHNOLOGY,Moinabad, Hyderabad, Telangana-501504
  • T. Prabhakara Rao Associate Professor,Department of Computer Science and Engineering,Aditya Engineering College (A),Surampalem, Andhra Pradesh,533437
  • C. Ambhika Assistant Professor,Department of Information Technology, Velammal Institute of Technology,Ponneri, Chennai
  • Y. Divya Sr. Assistant Professor,Department of EIE,CVR COLLEGE OF ENGINEERING, HYDERABAD
  • Bommisetti Yamini Supriya Assistant Professor, Department of CSE,Koneru Lakshmaiah Education Foundation,Vaddeswaram,AP,India
  • R. Sundar Assistant Professor,Computer science and Engineering,Madanapalle Institute of Technology & Science,Ap
  • Tabish Rao Assistant Professor, School of Computing, Graphic Era Hill University, Dehradun, R/S, Graphic Era Deemed To Be University, Dehradun, 248002
  • Balajee J. Associate professor,Department of Computer Science,Mother Theresa Institute of Engineering and Technology, Palamaner,Andhra Pradesh

Keywords:

Twitter, spam detection, machine learning, word2vector, extreme learning machine

Abstract

In contemporary times, social networking sites have gained widespread popularity as tools for interaction and communication. Among these platforms, Twitter holds a significant position, facilitating news consumption, idea sharing, social discourse, and interpersonal communication. However, due to its wide user base, Twitter has also become a breeding ground for spam activities. Numerous studies have been conducted to detect spam on Twitter, employing both traditional and machine learning models. Addressing this issue, this paper introduces an innovative approach to Twitter spam detection using a multi-layered extreme learning machine (MELM). Additionally, the Word2Vec model is employed to map words in the dataset into multi-dimensional vectors. By introducing multiple hidden layers and adaptively initializing weights connecting input, first hidden layer, and bias, the MELM model advances beyond the conventional ELM model. The application of the least squares technique aids in determining output weights for the network. To assess the efficacy of the MELM model in detecting spam, extensive experiments were conducted on three spam datasets. The results demonstrate the MELM model's proficiency, achieving an accuracy of 0.8817, precision of 0.9057, recall of 0.8650, and an F-Score of 0.8848.

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Wang Wei, Natural Language Processing Techniques for Sentiment Analysis in Social Media , Machine Learning Applications Conference Proceedings, Vol 1 2021.

Wang Wei, Natural Language Processing Techniques for Sentiment Analysis in Social Media , Machine Learning Applications Conference Proceedings, Vol 1 2021.

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Published

03.09.2023

How to Cite

Maithili, K. ., Rao, T. P. ., Ambhika, C. ., Divya, Y. ., Supriya, B. Y. ., Sundar, R. ., Rao, T. ., & J., B. . (2023). An Effective Twitter Spam Detection Model using Multiple Hidden Layers Extreme Learning Machine. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 01–09. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3389

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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