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Contextualized Early Detection of Online Firestorms... | AI Research

Key Takeaways

  • Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach Online firestorms—rapid, intense waves of negative social media content—...
  • Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage.
  • Existing detectors usually work with volume signals, sentiment scores, or predefined linguistic features.
  • Such signals are useful, but they capture contextual meaning shifts in evolving discussion threads only indirectly.
  • This paper proposes an LLM-based detection system with two operating modes.
Paper AbstractExpand

Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage. Existing detectors usually work with volume signals, sentiment scores, or predefined linguistic features. Such signals are useful, but they capture contextual meaning shifts in evolving discussion threads only indirectly. This paper proposes an LLM-based detection system with two operating modes. The first mode classifies complete Reddit threads retrospectively by combining local chunk-level assessments into a thread-level judgment. The second mode processes threads sequentially and issues early warnings when a sliding window exceeds calibrated thresholds. In this mode, the language model estimates three firestorm indicators: negativity share, escalation level, and contributor count. On a balanced Reddit dataset, the global mode achieves strong classification performance, while the early warning mode reaches high recall and detects escalating threads after only a small number of comments and distinct contributors. The results indicate that LLMs can be used not only for static judgment tasks, but also as repeated estimators in context-aware monitoring of social media discourse.

Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach
Online firestorms—rapid, intense waves of negative social media content—can cause significant reputational and financial damage to organizations. Traditional detection tools often rely on simple volume counts or basic sentiment scores, which frequently fail to capture the nuanced, evolving nature of online arguments. This paper introduces a new system that leverages Large Language Models (LLMs) to monitor discussion threads as they unfold, allowing for both retrospective analysis and, more importantly, early warnings before a situation escalates out of control.

A Two-Mode Detection System

The researchers developed a system with two distinct operating modes. The first, the "Global Recognition Mode," analyzes a completed Reddit thread to determine if it constitutes a firestorm. Because long threads can exceed the memory limits of AI models, the system breaks the conversation into smaller, manageable chunks. It assesses each chunk for conflict-relevant content and then synthesizes these local assessments into a final, thread-level judgment.
The second, the "Early Warning Mode," is designed for real-time monitoring. Instead of waiting for a thread to end, the system uses a sliding window to process comments as they arrive. It continuously evaluates the discourse to see if it meets specific, pre-calibrated thresholds for a firestorm.

Measuring Escalation

To make the early warning system effective, the researchers focused on three specific indicators grounded in communication science:

  • Negativity Share: The proportion of comments in the current window that are highly negative.

  • Escalation Level: The degree of "piling-on" and intensifying hostility within the conversation.

  • Contributor Count: The number of unique users actively participating in the conflict.
    By using the LLM as a judge to estimate these three metrics, the system can identify the specific moment a discussion shifts from a standard disagreement into a collective, aggressive firestorm.

Performance and Practical Application

The system was tested on a dataset of 200 Reddit threads, evenly split between firestorms and neutral discussions. The results show that the global mode provides high-accuracy retrospective classification. In the early warning mode, the system demonstrated high recall, successfully identifying escalating threads after only a small number of comments and contributors had participated.
This approach demonstrates that LLMs can function as dynamic, context-aware monitors rather than just static tools. By moving beyond simple keyword matching, the system provides a practical way for organizations to detect the early signs of a firestorm, potentially allowing them to intervene before minor criticisms evolve into widespread reputational harm.

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