Exploratory Data Analysis on Code-mixed Misogynistic Comments
This work addresses the lack of studies on misogyny detection in under-resourced languages, which is an incremental step for improving online safety for women.
The authors tackled the problem of detecting misogynistic content in under-resourced languages by creating a novel dataset of Hinglish YouTube comments, applying pre-processing and exploratory data analysis to gain insights into its characteristics.
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.