Combating Online Malicious Behavior: Integrating Machine Learning and Deep Learning Methods for Harmful News and Toxic Comments

Abstract

The surge in online media has inundated the public with information, prompting the use of sensational and provocative language to capture attention, worsening the prevalence of online malicious behavior. This study delves into machine learning (ML) and deep learning (DL) techniques to identify and recognize harmful news and toxic comments, aiming to counteract the detrimental impact on public perception. Effective methods for detecting and categorizing malicious content are proposed and discussed, highlighting the differences between ML and DL approaches in combating malicious behavior. The study employs feature selection methods to scrutinize the distinctive feature set and keywords linked to harmful news and toxic comments. The proposed approach yields promising outcomes, achieving a 94% accuracy rate in recognizing toxic comments, a 68% recognition accuracy for harmful news, and an 81% accuracy in classifying malicious behavior content (combining harmful news and toxic comments). By harnessing the capabilities of ML and DL, this research enriches our comprehension of and ability to mitigate malicious behavior in online media. It provides valuable insights into the practical identification and categorization of harmful news and toxic comments, highlighting the unique facets of these advanced computational strategies as they address the pressing challenges of our digital society.