  The negative effects of online bullying and harassment are increasing with
Internet popularity, especially in social media. One solution is using natural
language processing (NLP) and machine learning (ML) methods for the automatic
detection of harmful remarks, but these methods are limited in low-resource
languages like the Chittagonian dialect of Bangla.This study focuses on
detecting vulgar remarks in social media using supervised ML and deep learning
algorithms.Logistic Regression achieved promising accuracy (0.91) while simple
RNN with Word2vec and fastTex had lower accuracy (0.84-0.90), highlighting the
issue that NN algorithms require more data.
