ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos

TL;DR

This paper introduces ToxVidLM, a multimodal framework for detecting toxic content in code-mixed videos, along with a new dataset, demonstrating high accuracy in toxicity detection through multimodal analysis.

Contribution

It presents the first benchmark dataset of code-mixed Hindi-English videos with toxicity annotations and develops a multimodal multitask model for toxicity, sentiment, and severity analysis.

Findings

Multimodal analysis improves toxicity detection accuracy.

The model achieves over 94% accuracy and F1 score.

The dataset includes 931 videos with 4021 annotated utterances.

Abstract

In an era of rapidly evolving internet technology, the surge in multimodal content, including videos, has expanded the horizons of online communication. However, the detection of toxic content in this diverse landscape, particularly in low-resource code-mixed languages, remains a critical challenge. While substantial research has addressed toxic content detection in textual data, the realm of video content, especially in non-English languages, has been relatively underexplored. This paper addresses this research gap by introducing a benchmark dataset, the first of its kind, consisting of 931 videos with 4021 code-mixed Hindi-English utterances collected from YouTube. Each utterance within this dataset has been meticulously annotated for toxicity, severity, and sentiment labels. We have developed an advanced Multimodal Multitask framework built for Toxicity detection in Video Content by leveraging Language Models (LMs), crafted for the primary objective along with the additional tasks of conducting sentiment and severity analysis. ToxVidLM incorporates three key modules - the Encoder module, Cross-Modal Synchronization module, and Multitask module - crafting a generic multimodal LM customized for intricate video classification tasks. Our experiments reveal that incorporating multiple modalities from the videos substantially enhances the performance of toxic content detection by achieving an Accuracy and Weighted F1 score of 94.29% and 94.35%, respectively.