  Social media platforms and online streaming services have spawned a new breed
of Hate Speech (HS). Due to the massive amount of user-generated content on
these sites, modern machine learning techniques are found to be feasible and
cost-effective to tackle this problem. However, linguistically diverse datasets
covering different social contexts in which offensive language is typically
used are required to train generalizable models. In this paper, we identify the
shortcomings of existing Bangla HS datasets and introduce a large manually
labeled dataset BD-SHS that includes HS in different social contexts. The
labeling criteria were prepared following a hierarchical annotation process,
which is the first of its kind in Bangla HS to the best of our knowledge. The
dataset includes more than 50,200 offensive comments crawled from online social
networking sites and is at least 60% larger than any existing Bangla HS
datasets. We present the benchmark result of our dataset by training different
NLP models resulting in the best one achieving an F1-score of 91.0%. In our
experiments, we found that a word embedding trained exclusively using 1.47
million comments from social media and streaming sites consistently resulted in
better modeling of HS detection in comparison to other pre-trained embeddings.
Our dataset and all accompanying codes is publicly available at
github.com/naurosromim/hate-speech-dataset-for-Bengali-social-media
