Improving Safety on the Internet: A New Multimodal Framework for Hateful Meme Classification

In the fast-changing social media world, a significant mode of communication carrying complex concepts and feelings has materialized: the manufacture of memes. It welcomes bad memes such as hatred, stereotypes, and misinformation, but many memes also have positive values for discussion. This paper directly addresses the challenge of detecting hateful memes. It leverages the “Facebook hateful meme dataset”, Kaggle, 2020 by Facebook AI, a hand-curated dataset, for conducting innovative model training to detect and mitigate the spread of hateful content. Our dataset analyzes multimodal representations of hate speech in memes, integrating both text and images for classification. The results are encouraging with the implementation of advanced neural network architectures, such as convolutional networks for image annotation and recurrent networks called text-data modelling. The implications of this paper extend beyond detection. This is a significant step in combating the spread of hateful content and ensuring a safer online space. Ultimately, this work benefits the larger natural language processing and computer vision community, making an argument necessary for interdisciplinary computational treatment of the intricacies of online hate.