Year: 2024
Type: article
Abstract: Introduction:Cyberbullying is a significant concern in today's digital age, affecting individuals across various demographics. Objective: This study aims to analyze and classify instances of cyberbullying using a dataset sourced from Kaggle, containing text data labeled for different types of bullying behaviors. Method: Our approach to tackling these challenges involves several key steps, starting with data preprocessing and feature extraction to identify patterns and improve detection methods, enhancing our understanding of how cyberbullying manifests in online communications.Result: The dataset provides a valuable resource for developing and evaluating machine learning models aimed at detecting sexist and racist content in tweets.Conclusion: This study advances the current understanding of the complexities involved in detecting cyberbullying and paves the way for future breakthroughs in this domain. The binary classification enabled by the 'oh_label' column streamlines the analysis process, making it particularly compatible with binary classification models
Source: LatIA
Authors: Alok Kumar Anand, Rajesh Kumar Mahto, Awadesh Prasad
Institution: Veer Kunwar Singh University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4402702380