The Internet is currently the largest platform for global communication
including expressions of opinions, reviews, contents, images, videos and so
forth. Moreover, social media has now become a very broad and highly engaging
platform due to its immense popularity and swift adoption trend. Increased
social networking, however, also has detrimental impacts on the society leading
to a range of unwanted phenomena, such as online assault, intimidation, digital
bullying, criminality and trolling. Hence, cyberbullying has become a pervasive
and worrying problem that poses considerable psychological and emotional harm
to the people, particularly amongst the teens and the young adults. In order to
lessen its negative effects and provide victims with prompt support, a great
deal of research to identify cyberbullying instances at various online
platforms is emerging. In comparison to other languages, Bangla (also known as
Bengali) has fewer research studies in this domain. This study demonstrates a
deep learning strategy for identifying cyberbullying in Bengali, using a
dataset of 12282 versatile comments from multiple social media sites. In this
study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has
been built to identify cyberbullying, using a variety of optimisers as well as
5-fold cross validation. To evaluate the functionality and efficacy of the
proposed system, rigorous assessment and validation procedures have been
employed throughout the project. The results of this study reveals that the
proposed model's accuracy, using momentum-based stochastic gradient descent
(SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a
F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31%
in 5-fold cross validation.

使用深度学习策略，本研究构建了一个双层双向长短时记忆（Bi-LSTM）模型，通过多种优化器和 5 折交叉验证来鉴别孟加拉语中的网络欺凌，结果显示所提出的模型在动量加速随机梯度下降（SGD）优化器下的准确率为 94.46%，在 Adam 优化器下的准确率为 95.08%，F1 分数为 95.23%，并在 5 折交叉验证中具有 94.31% 的准确率。