  With the freedom of communication provided in online social media, hate
speech has increasingly generated. This leads to cyber conflicts affecting
social life at the individual and national levels. As a result, hateful content
classification is becoming increasingly demanded for filtering hate content
before being sent to the social networks. This paper focuses on classifying
hate speech in social media using multiple deep models that are implemented by
integrating recent transformer-based language models such as BERT, and neural
networks. To improve the classification performances, we evaluated with several
ensemble techniques, including soft voting, maximum value, hard voting and
stacking. We used three publicly available Twitter datasets (Davidson,
HatEval2019, OLID) that are generated to identify offensive languages. We fused
all these datasets to generate a single dataset (DHO dataset), which is more
balanced across different labels, to perform multi-label classification. Our
experiments have been held on Davidson dataset and the DHO corpora. The later
gave the best overall results, especially F1 macro score, even it required more
resources (time execution and memory). The experiments have shown good results
especially the ensemble models, where stacking gave F1 score of 97% on Davidson
dataset and aggregating ensembles 77% on the DHO dataset.
