The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate speech can be detrimental to maintaining the peace and harmony in society. Particularly when hate speech is spread with the intention to defame people, or spoil the image of a person, a community, or a nation. A major ground for spreading hate speech is that of social media. This significantly contributes to the difficultyof the task, as social media posts not only include paralinguistic tools (e.g. emoticons, and hashtags), their linguistic content contains plenty of poorly written text that does not adhere to grammar rules. With the recent development in Natural Language Processing (NLP), particularly with deep architecture, it is now possible to anlayze unstructured composite natural language text. For this reason, we propose a deep NLP model for the detection of automatic hate speech in social media data. We have applied our model on the HASOC2019 hate speech corpus, and attained a macro F1 score of 0.63 in the detection of hate speech.