Novel approach to detect hate speech and profanity on online platforms

Abstract

Hate speech is becoming more prominent and dominant in the virtual world, with the popularity of social media increasing day by day. People nowadays have various online platforms where they can express their hatred and write offensive speech in the safety of their home. They could even spread false rumors and incite hatred out of nothing. Cyberbullies often verbally attack the sentiments of people with different race, nationality, gender, beliefs and political views. They could also target young children and teenagers. It is also important to note that profane language or some sensitive topic may be bothersome when reached in front of young children and teenagers. It has become necessary for modern technology to detect all those profane and hate speeches so that they can be filtered or removed automatically before they can appear in front of young children or hurt the sentiments of targeted people. However, even though it is easy to detect profanities, it could be difficult to detect all the hate speeches which do not have any offensive or sensitive keywords. It is possible to spot all sorts of hate speeches on social media through the application of machine learning, neural networks and natural language processing. In our study, to identify and recognize hate speeches we will use various models and algorithms. Then we will design and implement an algorithm which will be able to detect hate speech and profane language more efficiently.