Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events

TL;DR

This paper introduces PsychoAgent, a psychology-inspired generative framework that improves the prediction and interpretability of public panic emotions on social media during disasters by combining fine-grained datasets, psychological modeling, and role-playing LLMs.

Contribution

It presents a novel psychology-driven approach with a new dataset and interpretability methods for panic prediction during emergencies.

Findings

PsychoAgent improves panic emotion prediction accuracy by up to 21.7%.

The approach enhances explainability and generalization in panic prediction.

It demonstrates a paradigm shift towards mechanistic, role-based simulation for emotion prediction.

Abstract

During sudden disaster events, accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained open panic emotion dataset (namely COPE) via human-large language models (LLMs) collaboration to mitigate semantic bias. Then, we develop a framework integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 12.6% to 21.7% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque "data-driven fitting" to transparent "role-based simulation with mechanistic interpretation" for panic emotion prediction during emergencies. Our implementation is publicly available at: https://anonymous.4open.science/r/PsychoAgent-19DD.