A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest

Praveena BN; Syeeda Mujeebunnisa

A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest

September 2024

Vol-10, Issue-5

Paper ID: 24981

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Spam detection Fake user identification Social networks Random Forest classifier Machine learning Spam message classification Social media security Feature extraction Data preprocessing User behavior analysis Predictive modelling.

Abstract

Social media platforms, with billions of global users, have unfortunately become prime targets for spammers and fraudulent entities distributing harmful and irrelevant content. Twitter, a leading social network, is particularly vulnerable to the spread of spam. Malicious users post unsolicited tweets to advertise products, services, or dubious websites, which can negatively impact legitimate users. This paper introduces a machine learning-based approach for identifying spam messages and fake users on Twitter. Using a Random Forest classifier, our system differentiates between authentic and spam tweets, achieving an accuracy rate of 92%. The focus of this research is on classifying spam tweets by analyzing their content, URLs, trending topics, and user profiles. By utilizing behavioral patterns and textual analysis, this study offers an effective method for spam detection on social media platforms.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Praveena BN	CMR University
2	Syeeda Mujeebunnisa	CMR University

How to Cite

Use the following formats to cite this article in your research.

APA Style

BN, Praveena & Mujeebunnisa, Syeeda (2024). A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest. International Journal of Advance Research and Innovative Ideas In Education, 10(5), 569-573.

MLA Style

BN, Praveena, and Syeeda Mujeebunnisa. "A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, 2024, pp. 569-573.

IEEE Style

Praveena BN and Syeeda Mujeebunnisa, "A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, pp. 569-573, 2024.

Vancouver Style

BN Praveena, Mujeebunnisa Syeeda. A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(5):569-573.

Harvard Style

BN, Praveena & Mujeebunnisa, Syeeda (2024) 'A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest', International Journal of Advance Research and Innovative Ideas In Education, 10(5), pp. 569-573.

Chicago Style

BN, Praveena and Syeeda Mujeebunnisa. "A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 569-573.

Turabian Style

BN, Praveena and Syeeda Mujeebunnisa. "A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 569-573.