Multimodal Islamophobic Content Detection on Social Media using Deep Learning

Islamophobia or anti-Muslim antagonism is one prepotent yet dilapidated form of racism in our today’s world. The last couple of years have witnessed an immense surge in Islamophobic hate speech on social media nurturing and progressing violence and prejudice against Muslims and Islam. A growingly frequent expression of online hate speech is multimodal (text + image) in nature and known as a meme. Despite ample literature on hate speech detection on social media, there are only a few papers on Islamophobic hate speech detection. Our target is to automatically detect and classify the content of those memes that are hostile to Islam and transfer extremist thoughts against Muslims. As detecting memes is a multimodal (relying on both textual and visual cues) problem thus requiring a holistic understanding of photos, words in photos, and the context around the two as they convey a message using both images and text and, hence, require multimodal reasoning and joint visual and language understanding. The detection of multimodal Islamophobic content is an innately difficult and open problem: When viewing a meme, for example, we don’t think about the words and photos independently of each other; we understand the combined meaning together. This is extremely challenging for machines, however, because it means they can’t just analyze the text and the image separately. They must combine these different modalities and understand how the meaning changes when they are presented together. In this work, we seek to
advance this line of research and develop a multimodal framework for the detection of Islamophobic memes. The Data shall be collected from Facebook and Instagram and shall be manually annotated to train the system to automatically classify the Islamophobic cyber hate instances into the given categories. Specific keywords that refer to Islamophobic content shall be considered as a search criterion, considering different manifestations of hatred against Muslims, such as terrorists, extremists, stereotyping, objectification, destruction, and violence.