Visual-affect-guided spatiotemporal graph modeling of disaster-image diffusion for embodied decision support.

Overview

1. College of Information Technology and Media, Hexi University, Zhangye, Gansu, China
2. College of Artificial Intelligence, Shenzhen Technology University, Shenzhen, Guangdong, China

Abstract

Introduction: Disaster-response robots and other embodied agents can use social-media imagery as contextual evidence, but existing approaches typically analyze affect at the image level or model diffusion without isolating affect-specific information.

Methods: We encoded disaster images into 128-dimensional continuous visual-affect embeddings representing visual intensity, fear, sadness, and negative valence. These embeddings initialized node states in a time-aware graph neural network whose edges were constrained by observed interaction links, temporal precedence, and spatial reachability. Multi-task heads predicted propagation scale, propagation speed, and spatial-ranking consistency, and a parameter-decomposition module separated affect-associated contributions from structure-associated residuals. Experiments used 4,450 reconstructed image-centered CrisisMMD samples, a common 70/15/15 split, five random seeds, matched baselines, and ablation tests.

Results: The full model achieved an AUC of 0.842 ± 0.011, an RMSE of 0.091 ± 0.007 under the current task definition, and a Kendall tau of 0.730 ± 0.018. Relative to matched ablations, visual affect increased AUC by 0.041, time-aware recurrence reduced RMSE by 33.6%, and spatial constraints increased Kendall tau by 0.089. The RMSE unit must be standardized after the propagation-speed versus propagation-time definition is resolved, as marked in the proof.

Discussion: Visual affect provides an interpretable complementary signal for modeling disaster-image diffusion. The findings do not establish causal effects, cross-platform generalizability, real-time device performance, or closed-loop robot benefit; the framework should therefore be regarded as an upstream social-sensing prior for embodied decision support.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. The source CrisisMMD dataset is available under the conditions established by its original providers. The reconstructed metadata structure, preprocessing description, and implementation code supporting this study can be made available by the corresponding author on reasonable request, subject to the source-data terms of use.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

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This paper

Zhang, X., & Zhang, W. (2026). Visual-affect-guided spatiotemporal graph modeling of disaster-image diffusion for embodied decision support. Frontiers in neurorobotics, 20, 1889303. https://doi.org/10.3389/fnbot.2026.1889303

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