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arXiv · 2410.05359

Interactive Event Sifting using Bayesian Graph Neural Networks

José Nascimento·Nathan Jacobs·Anderson Rocha
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Abstract

Forensic analysts often use social media imagery and texts to understand important events. A primary challenge is the initial sifting of irrelevant posts. This work introduces an interactive process for training an event-centric, learning-based multimodal classification model that automates sanitization. We propose a method based on Bayesian Graph Neural Networks (BGNNs) and evaluate active learning and pseudo-labeling formulations to reduce the number of posts the analyst must manually annotate. Our results indicate that BGNNs are useful for social-media data sifting for forensics investigations of events of interest, the value of active learning and pseudo-labeling varies based on the setting, and incorporating unlabelled data from other events improves performance.

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José Nascimento, Nathan Jacobs, Anderson Rocha. 2024-10-07. Interactive Event Sifting using Bayesian Graph Neural Networks. https://arxiv.org/abs/2410.05359

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