Racism is an unequal treatment based on race, color, origin, ethnicity or religion. It is often associated with rejection, inequality, and value judgment. A racist act, whether conscious or unconscious, goes beyond insult and aggression and leaves a devastating psychological effect on the victim. Although almost all laws around the world punish racist acts and speech, racist messages are on the rise on social networks. As a result, there is a strong need for reliable and accurate detectors of racist comments to identify the offenders and take appropriate punitive action against them. In this paper, we propose a model for the detection of racist statements in text messages by Bidirectional Gated Recurrent Units. For the word representation, we use different word embedding techniques, namely Word2Vec and GloVe. We show that this combination works well and provides a good level of detection. At the end of our study, we will suggest new horizons to improve the quality of our model.