Interactive Event Sifting using Bayesian Graph Neural Networks

• URL: http://arxiv.org/abs/2410.05359v1
• Date: Mon, 7 Oct 2024 16:28:47 GMT
• Title: Interactive Event Sifting using Bayesian Graph Neural Networks
• Authors: José Nascimento, Nathan Jacobs, Anderson Rocha,
• Abstract summary: 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.
• Score: 20.9835974435447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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