Volume 20 No 12 (2022)
Download PDF
YOUTUBE SPAM COMMENTS DETECTION
BACHU BHAGYA LAKSHMI , KAMBHAM SALIVAHANA REDDY
Abstract
With the raised quality of online social networks, spammers realize these platforms are simple to lure
users into malicious activities by posting spam messages in the comments section of the videos. In this
work, YouTube comments have been taken and spam detection is performed. To stop spammers,
Google Safe Browsing and YouTube Bookmaker tools detect and block spam YouTube. These tools will
block malicious links, however they cannot protect the user in real-time as early as possible. Thus,
industries and researchers have applied completely different approaches to form spam free social
network platform. The survey for the spam comments detection methodology has been carried out
using four Artificial Intelligence estimations – Logistic Regression, Ada Boost, Decision Tree and Random
Forest. With the use of Neural Network, we can achieve an exactness of 91.65% and beat the present
course of action by around 18%. The most notable AI procedures (Bayesian portrayal, k-NN, ANNs,
SVMs) and of their suitability to the issue of spam.
Keywords
KNN, ANN, SVM, AI, Ada Boost, spam
Copyright
Copyright © Neuroquantology
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.