HateMM: A Multi-Modal Dataset for Hate Video Classification
Mithun Das Rohit Raj Punyajoy Saha Binny Mathew Manish Gupta Animesh Mukherjee
Hate speech has become one of the most significant issues in modern society,
having implications in both the online and the offline world. Due to this, hate
speech research has recently gained a lot of traction. However, most of the
work has primarily focused on text media with relatively little work on images
and even lesser on videos. Thus, early stage automated video moderation
techniques are needed to handle the videos that are being uploaded to keep the
platform safe and healthy. With a view to detect and remove hateful content
from the video sharing platforms, our work focuses on hate video detection
using multi-modalities. To this end, we curate ~43 hours of videos from
BitChute and manually annotate them as hate or non-hate, along with the frame
spans which could explain the labelling decision. To collect the relevant
videos we harnessed search keywords from hate lexicons. We observe various cues
in images and audio of hateful videos. Further, we build deep learning
multi-modal models to classify the hate videos and observe that using all the
modalities of the videos improves the overall hate speech detection performance
(accuracy=0.798, macro F1-score=0.790) by ~5.7% compared to the best uni-modal
model in terms of macro F1 score. In summary, our work takes the first step
toward understanding and modeling hateful videos on video hosting platforms
such as BitChute.