Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages
This addresses the problem of detecting hateful content in social media memes for users in multilingual regions like India, representing an incremental advancement.
The paper tackles hate speech detection in multimodal memes using code-switched languages, presenting a novel dataset and a CNN-LSTM model that achieves state-of-the-art results.
The rise in the number of social media users has led to an increase in the hateful content posted online. In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched languages. This hate speech is depicted with the help of images to form "Memes" which create a long-lasting impact on the human mind. In this paper, we take up the task of hate and offense detection from multimodal data, i.e. images (Memes) that contain text in code-switched languages. We firstly present a novel triply annotated Indian political Memes (IPM) dataset, which comprises memes from various Indian political events that have taken place post-independence and are classified into three distinct categories. We also propose a binary-channelled CNN cum LSTM based model to process the images using the CNN model and text using the LSTM model to get state-of-the-art results for this task.