Exploring Hate Speech Detection in Multimodal Publications
Computer Vision and Pattern Recognition
2019-10-10 v1 Computation and Language
Abstract
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.
Cite
@article{arxiv.1910.03814,
title = {Exploring Hate Speech Detection in Multimodal Publications},
author = {Raul Gomez and Jaume Gibert and Lluis Gomez and Dimosthenis Karatzas},
journal= {arXiv preprint arXiv:1910.03814},
year = {2019}
}