An Approach to Detection of Sexism and Racism in Hate Text-Speech Using Machine Learning Algorithms

Registration ID: IJNRD_207252

Published ID: IJNRD2310258

DOI: Click Here to Get

Abubakar Adamu Usman , Muhammed Kabir Ahmed

Hate speech, Sentiment analysis, Social media, Machine learning

The rise of hate speech in online platforms poses a significant societal concern, necessitating effective detection and mitigation strategies. This research presents a robust approach to detect hate speech using machine learning algorithms. The study leverages the Glove Twitter dataset and extracts tweets directly from Twitter using the Twitter API. Three distinct machine learning algorithms - Random Forest, Logistic Regression, and Support Vector Machine (SVM) - are employed to train models for hate speech detection. The obtained accuracies for Random Forest, Logistic Regression, and SVM are 98.16%, 87.77%, and 98.54%, with F1 scores recorded at 98.1318%, 88.0759%, and 98.5313% respectively. The results demonstrated the effectiveness of the Support Vector Machine in hate speech detection and highlight the importance of leveraging machine learning for fostering a safer online environment.

Published Paper   E-Certificate

Abubakar Adamu Usman & Muhammed Kabir Ahmed (October-2023). An Approach to Detection of Sexism and Racism in Hate Text-Speech Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(10), c550-c557. https://ijnrd.org/papers/IJNRD2310258.pdf

Volume 8 Issue 10, October-2023

Pages : c550-c557

Paper Reg. ID: IJNRD_207252

Published Paper Id: IJNRD2310258

Downloads: 000121985

Research Area: Computer Science & Technology 

Country: Gombe/Gombe, GOMBE, Nigeria

Published Paper PDF: https://ijnrd.org/papers/IJNRD2310258.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2310258

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 10 | October 2025

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