A Comparative Study of Naive Bayes and Enhanced Random Forest Algorithms in Spam Detection
0A Comparative Study of Naive Bayes and Enhanced Random Forest Algorithms in Spam Detection
Layman Abstract: Social media platforms like Facebook, Instagram, and X (formerly Twitter) have become a big part of our daily lives. People use them to chat, make friends, share opinions, and stay informed. As these platforms grow, they also become targets for cybercriminals who spread spam and harmful links, which can ruin the experience for users and even pose security threats.
While there have been efforts to stop spam, many current solutions still struggle to catch all the unwanted messages accurately. In this study, the authors explore two smart computer-based methods—Naive Bayes and an improved version of Random Forest—to detect and block spam on social media more effectively.
Naive Bayes is a method that uses probability to figure out if a message is spam, while Enhanced Random Forest is a smarter version of a well-known system that looks at patterns in the data. The study compares both methods using different performance measures like accuracy and reliability.
The results show that Enhanced Random Forest does a better job overall, especially when it comes to balancing performance and speed. This means it's a promising tool for helping social media platforms fight spam and keep users safer online.
View Book: https://doi.org/10.9734/bpi/stda/v3/3442
#SocialMedia #OnlineSocialNetworks #SpamDetection #CyberSecurity #NaiveBayes #RandomForest #EnhancedRandomForest #AIinCybersecurity #MachineLearning #SocialMediaAnalysis #DigitalSecurity #SocialMediaSpam #PhishingPrevention #DataScience #InformationSecurity #CyberThreats #AIForGood #TechForSafety #SecureSocialMedia
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