MULTI-STAGE MACHINE LEARNING AND FUZZY APPROACH TO CYBER-HATE DETECTION

Abstract

Multi-Stage Machine Learning and Fuzzy
Approach to Cyber-Hate Detection aims to
address the growing concern of hate speech
and cyber bullying on social media platforms,
where millions of users post content daily.
Statistically, studies show that over 40% of
internet users have experienced some form of
online harassment, and nearly 70% of reported
cases on social media go unaddressed or are
delayed due to manual moderation.
Traditionally, the manual system for detecting
cyber hate involves human moderators
reviewing flagged content based on user
reports. This process is labor-intensive, timeconsuming, subjective, and inconsistent due to
the sheer volume of data and varying
interpretations of hate speech across cultures
and contexts. These limitations lead to delays
in response and failure to filter out subtle or
context-specific abusive content. Motivated by
the need for faster, scalable, and more accurate
solutions, this research proposes an intelligent
system that combines a multi-stage machine
learning pipeline with a fuzzy logic approach.
The objective is to enhance detection accuracy
and reduce ambiguity by capturing not only
direct hate expressions but also context-based
and indirect abusive content. In the proposed
system, textual data undergoes preprocessing
followed by multiple stages of classification
using traditional machine learning algorithms
like Naive Bayes, SVM, and Decision Tree,
and then evaluated against a logistic regression
model to determine the most reliable classifier.
Furthermore, a fuzzy inference system is
integrated to handle linguistic uncertainty and
context sensitivity, allowing the system to
make better decisions in edge cases where text
may not be explicitly hateful but potentially
harmful. This hybrid model leverages the
strength of both deterministic and fuzzy
learning methods to create a reliable and
efficient cyber hate detection system,
addressing the inefficiencies of manual
approaches and contributing toward safer
online communication environments