Interactive Event Sifting using Bayesian Graph Neural Networks

Autor(es)

Nascimento, Jose ; Jacobs, Nathan ; Rocha, Anderson

• Tipo de documento: Artigo Científico
• Fonte: 2024 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY, WIFS 2024; v. N/A, p. 5-pg., 2024-01-01.

Resumo

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. (AU)

• Processo FAPESP: 23/12865-8 - Horus: técnicas de inteligência artificial para detecção e análise de realidades sintéticas
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Auxílio à Pesquisa - Temático