Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos

Problem — The paper addresses the gap in existing literature on hateful video detection, which predominantly focuses on binary classification without providing contextual rationales for decisions. This lack of explainability undermines the understanding of model outputs, particularly in sensitive applications like hate speech detection. The authors introduce two new datasets, Ex-HateMM and Ex-ImpliHateVid, specifically designed for explainable detection, which include fine-grained annotations of multimodal harmful elements and contextual rationales. This work is presented as a preprint and has not yet undergone peer review.

Method — The authors propose the Information Augmentation and Reasoning Enhancement (IARE) framework for explainable hateful video detection. The IARE framework consists of two main phases:

1. Information Augmentation: This phase utilizes a multimodal chain-of-thought approach to integrate harmful elements from various modalities (e.g., visual, audio, and textual) to enrich the rationale evidence.
2. Reasoning Enhancement: This phase employs Direct Preference Optimization, which steers the model towards correct reasoning paths while avoiding incorrect ones, thereby enhancing the logical coherence of the generated justifications. The architecture details, including specific neural network configurations or training compute, are not disclosed in the abstract.

Results — The IARE framework was evaluated on the Ex-HateMM and Ex-ImpliHateVid datasets, achieving state-of-the-art performance compared to multiple baseline models. While specific performance metrics (e.g., accuracy, F1 score) are not detailed in the abstract, the authors claim that IARE not only surpasses existing methods in classification accuracy but also generates accurate and contextually relevant rationales for its decisions.

Limitations — The authors acknowledge that their approach may still struggle with edge cases where implicit meanings are particularly nuanced or context-dependent. Additionally, the reliance on multimodal inputs may limit the framework’s applicability in scenarios where such data is not available. The datasets introduced may also have inherent biases that could affect generalizability, although this is not explicitly discussed in the paper.

Why it matters — The development of explainable models for detecting hateful content is crucial for enhancing trust and accountability in AI systems, particularly in content moderation on social media platforms. By providing contextual rationales, the IARE framework not only improves model transparency but also aids in the understanding of complex social issues related to hate speech. This work contributes to the broader field of explainable AI, as published in arXiv, and sets a precedent for future research aimed at integrating explainability into multimodal classification tasks.