Work

MIMIC: Multimodal Islamophobic Meme Identification and Classification

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2412.00681

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

Institutions: United International University, Johannes Kepler University of Linz

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4405033444

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