  With increasing popularity of social media platforms hate speech is emerging
as a major concern, where it expresses abusive speech that targets specific
group characteristics, such as gender, religion or ethnicity to spread
violence. Earlier people use to verbally deliver hate speeches but now with the
expansion of technology, some people are deliberately using social media
platforms to spread hate by posting, sharing, commenting, etc. Whether it is
Christchurch mosque shootings or hate crimes against Asians in west, it has
been observed that the convicts are very much influenced from hate text present
online. Even though AI systems are in place to flag such text but one of the
key challenges is to reduce the false positive rate (marking non hate as hate),
so that these systems can detect hate speech without undermining the freedom of
expression. In this paper, we use ETHOS hate speech detection dataset and
analyze the performance of hate speech detection classifier by replacing or
integrating the word embeddings (fastText (FT), GloVe (GV) or FT + GV) with
static BERT embeddings (BE). With the extensive experimental trails it is
observed that the neural network performed better with static BE compared to
using FT, GV or FT + GV as word embeddings. In comparison to fine-tuned BERT,
one metric that significantly improved is specificity.
