A Framework for Hate Speech Detection using Different ML Algorithms

Social media is one among the most widely used platforms in the world. People use social media for communication and many other purposes. However, this has led to a number of issues, including the proliferation of hate messages. It is becoming increasingly difficult to identify hate speech. While th...

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Bibliographic Details
Published in:	2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) pp. 960 - 967
Main Authors:	Rao, Matta Chenna, Yelavarti, Kalyan Chakravarti, Kalyan, Nakka Pavan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 11-04-2023
Subjects:	Classification algorithms Feature extraction Hate speech Hateful speech Machine learning Natural language processing Social networking (online) Task analysis Text categorization Virtual networking platforms
Online Access:	Get full text
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Summary:	Social media is one among the most widely used platforms in the world. People use social media for communication and many other purposes. However, this has led to a number of issues, including the proliferation of hate messages. It is becoming increasingly difficult to identify hate speech. While there have been some completed works on this topic, research works still need to be improved in terms of accuracy. To address this problem on social media, this study proposes various ML methods to mark hateful contents on various datasets. Text classification tasks are commonly used to model hate speech concerns. This study makes a contribution by combining features extracted through feature engineering techniques and comparing the performance of ML algorithms on publicly available dataset with three different categories.
DOI:	10.1109/ICOEI56765.2023.10125942