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Title: :  A Comprehensive Approach to Machine Learning-Based Spam Message Classification Using Random Forest
PaperId: :  24981
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 5 2024
DUI:    16.0415/IJARIIE-24981
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Praveena BNCMR University
Syeeda MujeebunnisaCMR University

Abstract

Computer Engineering
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.
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.

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IJARIIE 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 Volume 10 Issue 5 2024 Page 569-573
MLA 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 10.5(2024) : 569-573.
APA Praveena BN, & Syeeda Mujeebunnisa. (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.
Chicago 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 10, no. 5 (2024) : 569-573.
Oxford 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, 2024, p. 569-573. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/A_Comprehensive_Approach_to_Machine_Learning_Based_Spam_Message_Classification_Using_Random_Forest_ijariie24981.pdf (Accessed : ).
Harvard Praveena BN, and Syeeda Mujeebunnisa. (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. 569-573IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/A_Comprehensive_Approach_to_Machine_Learning_Based_Spam_Message_Classification_Using_Random_Forest_ijariie24981.pdf (Accessed : )
IEEE 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, Sep-Oct 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/A_Comprehensive_Approach_to_Machine_Learning_Based_Spam_Message_Classification_Using_Random_Forest_ijariie24981.pdf [Accessed : ].
Turabian 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 [Online]. volume 10 number 5 ().
Vancouver 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 [Internet]. 2024 [Cited : ]; 10(5) : 569-573. Available from: https://ijariie.com/AdminUploadPdf/A_Comprehensive_Approach_to_Machine_Learning_Based_Spam_Message_Classification_Using_Random_Forest_ijariie24981.pdf
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