Abstract:
Communication in society had developed within cultural and
geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed
the conventional constraints for communication with regards to time and
location. These new platforms have ushered in a new age of user-generated
content, online chats, social network and comprehensive data on individual
behavior. However, the abuse of communication software such as social
media websites, online communities, and chats has resulted in a new kind of
online hostility and aggressive actions. Due to widespread use of the social
networking platforms and technological gadgets, conventional bullying has
migrated from physical form to online, where it is termed as Cyberbullying.
However, recently the digital technologies as machine learning and deep
learning have been showing their efficiency in identifying linguistic patterns
used by cyberbullies and cyberbullying detection problem. In this research
paper, we aimed to evaluate shallow machine learning and deep learning
methods in cyberbullying detection problem. We deployed three deep and six
shallow learning algorithms for cyberbullying detection problems. The results
show that bidirectional long-short-term memory is the most efficient method
for cyberbullying detection, in terms of accuracy and recall.
