  The exponential increase in the use of the Internet and social media over the
last two decades has changed human interaction. This has led to many positive
outcomes, but at the same time it has brought risks and harms. While the volume
of harmful content online, such as hate speech, is not manageable by humans,
interest in the academic community to investigate automated means for hate
speech detection has increased. In this study, we analyse six publicly
available datasets by combining them into a single homogeneous dataset and
classify them into three classes, abusive, hateful or neither. We create a
baseline model and we improve model performance scores using various
optimisation techniques. After attaining a competitive performance score, we
create a tool which identifies and scores a page with effective metric in
near-real time and uses the same as feedback to re-train our model. We prove
the competitive performance of our multilingual model on two langauges, English
and Hindi, leading to comparable or superior performance to most monolingual
models.
