Social Media is a repository of digital literature including user-generated
content. The users of social media are expressing their opinion with diverse
mediums such as text, emojis, memes, and also through other visual and textual
mediums. A major portion of these media elements could be treated as harmful to
others and they are known by many words including Cyberbullying and Toxic
Language . The goal of this research paper is to analyze a curated and
value-added dataset of toxic language titled ToxLex_bn . It is an exhaustive
wordlist that can be used as classifier material to detect toxicity in social
media. The toxic language/script used by the Bengali community as
cyberbullying, hate speech and moral policing became major trends in social
media culture in Bangladesh and West Bengal. The toxicity became so high that
the victims has to post as a counter or release explanation video for the
haters. Most cases are pointed to women celebrity and their relation, dress,
lifestyle are became trolled and toxicity flooded in comments boxes. Not only
celebrity bashing but also hates occurred between Hindu Muslims,
India-Bangladesh, Two opponents of 1971 and these are very common for virtual
conflict in the comment thread. Even many times facebook comment causes sue and
legal matters in Bangladesh and thus it requires more study. In this study, a
Bangla toxic language dataset has been analyzed which was inputted by the user
in Bengali script & language. For this, about 1968 unique bigrams or phrases as
wordlists have been analyzed which are derived from 2207590 comments. It is
assumed that this analysis will reinforce the detection of Bangla's toxic
language used in social media and thus cure this virtual disease.

通过分析社交媒体上使用的孟加拉语有毒语言的数据集，本研究旨在检测和解决在社交媒体中存在的有毒言论问题。