  As a result of social network popularity, in recent years, hate speech
phenomenon has significantly increased. Due to its harmful effect on minority
groups as well as on large communities, there is a pressing need for hate
speech detection and filtering. However, automatic approaches shall not
jeopardize free speech, so they shall accompany their decisions with
explanations and assessment of uncertainty. Thus, there is a need for
predictive machine learning models that not only detect hate speech but also
help users understand when texts cross the line and become unacceptable. The
reliability of predictions is usually not addressed in text classification. We
fill this gap by proposing the adaptation of deep neural networks that can
efficiently estimate prediction uncertainty. To reliably detect hate speech, we
use Monte Carlo dropout regularization, which mimics Bayesian inference within
neural networks. We evaluate our approach using different text embedding
methods. We visualize the reliability of results with a novel technique that
aids in understanding the classification reliability and errors.
