Interactive Event Sifting using Bayesian Graph Neural Networks
<|reference_start|>Interactive Event Sifting using Bayesian Graph Neural Networks: 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.<|reference_end|>
@article{nascimento2024interactive,
    title={Interactive Event Sifting using Bayesian Graph Neural Networks},
    author={Jos'e Nascimento, Nathan Jacobs and Anderson Rocha},
    journal={arXiv preprint arXiv:2410.05359},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05359},
    primaryClass={cs.LG cs.SI}
}