MIMIC: Multimodal Islamophobic Meme Identification and Classification

S M Jishanul Islam, Sahid Hossain Mustakim, Sadia Ahmmed, Md. Faiyaz Abdullah Sayeedi, Swapnil Khandoker, Syed Tasdid Azam Dhrubo, Nahid Hossain

Keywords: Islamophobia, Hate speech, Meme, Dataset, Multimodal Deep Learning, Computer Vision, Natural Language Processing, Classification

TL;DR: First work to combat Islamophobic hate shown through memes by creating a dataset out of it and using multimodal deep learning.

Abstract: Anti-Muslim hate speech has emerged within memes, characterized by context-dependent and rhetorical messages using text and images that seemingly mimic humor but convey Islamophobic sentiments. This work presents a novel dataset and proposes a classifier based on the Vision-and-Language Transformer (ViLT) specifically tailored to identify anti-Muslim hate within memes by integrating both visual and textual representations. Our model leverages joint modal embeddings between meme images and incorporated text to capture nuanced Islamophobic narratives that are unique to meme culture, providing both high detection accuracy and interoperability.

Submission Number: 12