Automatic Hate Speech Detection using Machine Learning: A Comparative Study

The increasing use of social media and information sharing has given major benefits to humanity. However, this has also given rise to a variety of challenges including the spreading and sharing of hate speech messages. Thus, to solve this emerging issue in social media sites, recent studies employed...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2020, Vol.11 (8)
Main Authors: Abro, Sindhu, Shaikh, Sarang, Hussain, Zahid, Ali, Zafar, Khan, Sajid, Mujtaba, Ghulam
Format: Article
Language:English
Subjects:
Algorithms
Comparative studies
Datasets
Digital media
Engineering
Hate speech
Machine learning
Messages
Performance evaluation
Social networks
Support vector machines
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Summary:The increasing use of social media and information sharing has given major benefits to humanity. However, this has also given rise to a variety of challenges including the spreading and sharing of hate speech messages. Thus, to solve this emerging issue in social media sites, recent studies employed a variety of feature engineering techniques and machine learning algorithms to automatically detect the hate speech messages on different datasets. However, to the best of our knowledge, there is no study to compare the variety of feature engineering techniques and machine learning algorithms to evaluate which feature engineering technique and machine learning algorithm outperform on a standard publicly available dataset. Hence, the aim of this paper is to compare the performance of three feature engineering techniques and eight machine learning algorithms to evaluate their performance on a publicly available dataset having three distinct classes. The experimental results showed that the bigram features when used with the support vector machine algorithm best performed with 79% off overall accuracy. Our study holds practical implication and can be used as a baseline study in the area of detecting automatic hate speech messages. Moreover, the output of different comparisons will be used as state-of-art techniques to compare future researches for existing automated text classification techniques.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110861