International Journal of Advanced Networking and Applications

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Hate Speech Detection in Social Media Using the Ensemble Learning Technique


Affiliations
1 Department of Computer Science, New Mexico Tech, Socorro, New Mexico, United States
 

Our lives have become intertwined with social media platforms such as Twitter, Facebook, LinkedIn, etc. They provide us with a platform to express our opinions and share our thoughts with the world. However, some individuals abuse the freedom of expression afforded to them by these platforms and utilize them to disseminate content that is derogatory and promotes hate speech. This has become a significant problem today, and detecting such content is a challenging task. In this research paper, we propose a solution for hate speech detection in social media using natural language processing techniques. We use a publicly available dataset provided by CrowdFlower and perform text pre-processing to clean the dataset. We then conduct feature engineering to extract key features that can be used in machine learning classification algorithms. We compare the performance of various algorithms about each feature set and conduct an in-depth analysis of the results obtained.

Keywords

Hate Speech Detection, Social Media, Natural Language Processing, Machine Learning, Artificial Intelligence, Sentiment Analysis.
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  • Hate Speech Detection in Social Media Using the Ensemble Learning Technique

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Authors

Ahshanul Haque
Department of Computer Science, New Mexico Tech, Socorro, New Mexico, United States
Md Naseef-Ur-Rahman Chowdhury
Department of Computer Science, New Mexico Tech, Socorro, New Mexico, United States
Hamdy Soliman
Department of Computer Science, New Mexico Tech, Socorro, New Mexico, United States

Abstract


Our lives have become intertwined with social media platforms such as Twitter, Facebook, LinkedIn, etc. They provide us with a platform to express our opinions and share our thoughts with the world. However, some individuals abuse the freedom of expression afforded to them by these platforms and utilize them to disseminate content that is derogatory and promotes hate speech. This has become a significant problem today, and detecting such content is a challenging task. In this research paper, we propose a solution for hate speech detection in social media using natural language processing techniques. We use a publicly available dataset provided by CrowdFlower and perform text pre-processing to clean the dataset. We then conduct feature engineering to extract key features that can be used in machine learning classification algorithms. We compare the performance of various algorithms about each feature set and conduct an in-depth analysis of the results obtained.

Keywords


Hate Speech Detection, Social Media, Natural Language Processing, Machine Learning, Artificial Intelligence, Sentiment Analysis.

References