We have used the 3 benchmark data sets for abusive content detection on Twitter. At the time of the experiment, the [10] data set had a total of 15,844 tweets out of which 1,924 were labelled as belonging to racism, 3,058 as sexism and 10,862 as none. The <cite>[9]</cite> data set had a total of 25,112 tweets out of which 1498 were labelled as hate speech, 19,326 as offensive language and 4,288 as neither.