  The detection of offensive, hateful and profane language has become a
critical challenge since many users in social networks are exposed to
cyberbullying activities on a daily basis. In this paper, we present an
analysis of combining different textual features for the detection of hateful
or offensive posts on Twitter. We provide a detailed experimental evaluation to
understand the impact of each building block in a neural network architecture.
The proposed architecture is evaluated on the English Subtask 1A: Identifying
Hate, offensive and profane content from the post datasets of HASOC-2021
dataset under the team name TIB-VA. We compared different variants of the
contextual word embeddings combined with the character level embeddings and the
encoding of collected hate terms.
