Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation

Abstract

This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused. The source code and mode and models are available https://github.com/imatge-upc/hate-speech-detection .

AI for Social Good Workshop at NeurIPS 2019 (short paper)

References in corpus (1)

• Detecting Hate Speech in Social Media

Cited by in corpus (5)

• HateProof: Are Hateful Meme Detection Systems really Robust?
• Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes
• Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning
• Detecting Harmful Memes and Their Targets
• AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media