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Predicting Online Firestorms with High-Fidelity Data from Large Language Models: An Evaluation of RNN, LSTM, and GRU

Author

Listed:
  • Gábor Nagy

    (INSEEC - Institut des hautes études économiques et commerciales | School of Business and Economics)

  • Jae-Yun Jun

    (LyRIDS - ECE Engineering School - OMNES Education - ECE Engineering School - OMNES Education, ECE Engineering School - OMNES Education)

Abstract

Although data obtained from social media platforms are relatively rich in terms of customer-customer and customer-firm interactions, there may be scenarios (e.g., alternative realizations of events and, consequently, effective firm-response strategies) that are rarely, if ever, observed on social media platforms (i.e., counterfactuals). This study synthesizes high-fidelity data—scenarios that are ‘closer' to empirical reality—using generative language models (e.g., ChatGPT 3.5 turbo) and Monte Carlo simulations. It then builds a multi-agent system that can better predict online firestorms. The study explores and empirically tests how three predictive architectures—RNN, LSTM, and GRU—built on high-fidelity synthetic data from multiple realizations of customer-customer interactions, can predict online firestorms in firm-mediated online brand communities. Among these three architectures, GRU demonstrates superior predictive accuracy.

Suggested Citation

  • Gábor Nagy & Jae-Yun Jun, 2025. "Predicting Online Firestorms with High-Fidelity Data from Large Language Models: An Evaluation of RNN, LSTM, and GRU," Post-Print hal-05729323, HAL.
    • Handle: RePEc:hal:journl:hal-05729323
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