Main Article Content
Hate speech is becoming a very imperative problem in social as well political context in the present era. It ultimately reflects an intolerance to difference (on the basis of ethnicity, caste and creed, religious views, race, political views, etc.). As a matter of fact, a data generator (user), who uses hate speech wants to emphasize their viewpoints and identity among others and a consequence of such activities somehow leads to hate deeds and conduct. Social media platforms with their wide approach have now become very powerful and influential to affect the psychology of people. The Internet, especially social media, acts as a “turbo accelerator” of hate speech in any context. It is a communication channel that plays a significant role both in opposing hate speech and amplifying it at the same time as well.According to standards for the Facebook community, “Hate Speech” is classified as the text or speech that hurts emotions and attacks someone on the basis of their ethnicity, caste, nation of origin, religion, disability or some type of disease. Twitter, also provides a policy which applies to promoted tweets and prohibits the promotion of sensitive content. This work proposes the mining of web content for available political speeches and then classifying them as hate speech or benign speech. This paper also presents background on hate speech and its detection approaches.