PDF

eversvd,+5 (15)

Ramakrishna Hegde, Bharath G

2026enhate speechsocial mediacyberbullyingmachine learningdeep learningneural networkscontent detection

Abstract

Language:

This review paper discusses the critical issue of hate speech detection in social media, where the proliferation of hateful communications can have severe consequences for individuals and groups, including mental health challenges and even self-harm. The objective of this research is to highlight the necessity of automating the identification of hate speech through advanced machine learning and deep learning methodologies. It explores various studies and techniques employed in detecting hate speech across different text contexts. The methodology involves a comprehensive review of existing literature pertaining to detection techniques such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP), Multiple Kernel Clustering (MKC), and Lagrangian Support Vector Machines (LSVM). The findings illustrate that effective hate speech detection can significantly contribute to healthier social media environments by mitigating the risks associated with hate speech. This review emphasizes the importance of continuous research to refine detection methodologies and enhance the overall safety of all users on social platforms.

Download

Cite This Work

@article{9de9d7e9-f659-426d-b25e-c3570df84943,
  title={eversvd,+5 (15)},
  author={Ramakrishna Hegde and Bharath G},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - eversvd,+5 (15)
AU  - Ramakrishna Hegde
AU  - Bharath G
PY  - 2026
LA  - en
ER  -

Similar Items

Artificial intelligence-aided materials design: AI-algorithms and case studies on alloys and metallurgical processes

Rajesh Jha, Bimal Kumar Jha

This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t

2022enPDF

A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management

Julian Oelhaf, Georg Kordowich

The integration of renewable and distributed energy resources has fundamentally reshaped modern power systems, challenging conventional protection sch

2023EnglishPDF

DeepFEA: Deep Learning for Prediction of Transient Finite Element

Georgios Triantafyllou, Panagiotis G. Kalozoumis

Finite Element Analysis (FEA) enables the simulation of physical phenomena under various conditions. This is usually a computationally expensive and t

2021EnglishPDF

Challenges and opportunities in the recovery of gold from electronic waste

Mudila Dhanunjaya Rao, Kamalesh K. Singh

Rapid global technological development has resulted in increased production of electronic waste, which presents both challenges and opportunities in r

2023enPDF

640069

Roger Rumbu

2025enPPTX

Optimization of Integrated Steel Plant R

This paper addresses the challenge of assessing the feasibility of wind power plant projects at sites with insufficient or no local historic wind data

2025enPDF