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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 multimediadocuments that contain photos or illustrations together with phrases which,when combined, usually adopt a funny meaning. However, hate memes are also usedto spread hate through social networks, so their automatic detection would helpreduce their harmful societal impact. Our results indicate that the model canlearn to detect some of the memes, but that the task is far from being solvedwith this simple architecture. While previous work focuses on linguistic hatespeech, our experiments indicate how the visual modality can be much moreinformative for hate speech detection than the linguistic one in memes. In ourexperiments, we built a dataset of 5,020 memes to train and evaluate amulti-layer perceptron over the visual and language representations, whetherindependently or fused. The source code and mode and models are availablehttps://github.com/imatge-upc/hate-speech-detection .

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