Exploring Hate Speech Detection in Multimodal Publications
This addresses hate speech detection for social media platforms, but it is incremental as it shows no improvement over existing text-based methods.
The paper tackled hate speech detection in multimodal (text+image) publications by creating the MMHS150K dataset and comparing multimodal models to text-only ones, finding that current multimodal models do not outperform text-only models.
In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.