Exploratory Data Analysis on Code-mixed Misogynistic Comments
Sargam Yadav Abhishek Kaushik Kevin McDaid
The problems of online hate speech and cyberbullying have significantly
worsened since the increase in popularity of social media platforms such as
YouTube and Twitter (X). Natural Language Processing (NLP) techniques have
proven to provide a great advantage in automatic filtering such toxic content.
Women are disproportionately more likely to be victims of online abuse.
However, there appears to be a lack of studies that tackle misogyny detection
in under-resourced languages. In this short paper, we present a novel dataset
of YouTube comments in mix-code Hinglish collected from YouTube videos which
have been weak labelled as `Misogynistic' and `Non-misogynistic'.
Pre-processing and Exploratory Data Analysis (EDA) techniques have been applied
on the dataset to gain insights on its characteristics. The process has
provided a better understanding of the dataset through sentiment scores, word
clouds, etc.