Quartet@LT-EDI 2024: A Support Vector Machine Approach For Caste and Migration Hate Speech Detection

AbstractHate speech refers to the offensive remarks against a community or individual based on inherent characteristics. Hate speech against a community based on their caste and native are unfortunately prevalent in the society. Especially with social media platforms being a very popular tool for communication and sharing ideas, people post hate speech against caste or migrants on social medias. The Shared Task LT–EDI 2024: Caste and Migration Hate Speech Detection was created with the objective to create an automatic classification system that detects and classifies hate speech posted on social media targeting a community belonging to a particular caste and migrants. Datasets in Tamil language were provided along with the shared task. We experimented with several traditional models such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier and Decision Tree Classifier out of which Support Vector Machine yielded the best results placing us 8th in the rank list released by the organizers.