  The struggle of social media platforms to moderate content in a timely
manner, encourages users to abuse such platforms to spread vulgar or abusive
language, which, when performed repeatedly becomes cyberbullying a social
problem taking place in virtual environments, yet with real-world consequences,
such as depression, withdrawal, or even suicide attempts of its victims.
Systems for the automatic detection and mitigation of cyberbullying have been
developed but, unfortunately, the vast majority of them are for the English
language, with only a handful available for low-resource languages. To estimate
the present state of research and recognize the needs for further development,
in this paper we present a comprehensive systematic survey of studies done so
far for automatic cyberbullying detection in low-resource languages. We
analyzed all studies on this topic that were available. We investigated more
than seventy published studies on automatic detection of cyberbullying or
related language in low-resource languages and dialects that were published
between around 2017 and January 2023. There are 23 low-resource languages and
dialects covered by this paper, including Bangla, Hindi, Dravidian languages
and others. In the survey, we identify some of the research gaps of previous
studies, which include the lack of reliable definitions of cyberbullying and
its relevant subcategories, biases in the acquisition, and annotation of data.
Based on recognizing those research gaps, we provide some suggestions for
improving the general research conduct in cyberbullying detection, with a
primary focus on low-resource languages. Based on those proposed suggestions,
we collect and release a cyberbullying dataset in the Chittagonian dialect of
Bangla and propose a number of initial ML solutions trained on that dataset. In
addition, pre-trained transformer-based the BanglaBERT model was also
attempted.
