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Source note3 min read

Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach

Source

arXiv

arxiv.org/abs/2607.14957v1

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Plain-Language Brief

This research paper proposes an LLM (Language Model) based system to detect online firestorms, which are rapid escalations of negative user-generated content, on Reddit. The system operates in two modes: retrospective classification and early warning. In the early warning mode, the language model estimates three indicators: negativity share, escalation level, and contributor count, which can help detect escalating threads after a small number of comments and contributors.

What the Document Says

  • Online firestorms are rapid collective escalations of negative user-generated content that can cause substantial damage.
  • Existing detection systems use volume signals, sentiment scores, or predefined linguistic features, but they don't capture contextual meaning shifts effectively.
  • The paper proposes an LLM-based detection system with two modes: retrospective classification and early warning.
  • In the retrospective mode, the system classifies complete Reddit threads and combines local assessments into a thread-level judgment.
  • In the early warning mode, the system processes threads sequentially, issuing alerts when a sliding window exceeds calibrated thresholds.
  • The language model estimates three firestorm indicators: negativity share, escalation level, and contributor count in the early warning mode.
  • On a balanced Reddit dataset, the global mode achieves strong classification performance. The early warning mode has high recall and detects escalating threads after a small number of comments and contributors.
  • The results suggest that LLMs can be used not only for static judgment tasks but also for context-aware monitoring of social media discourse.

Key Concepts Mentioned

  • online firestorms
  • LLM-based detection system
  • Reddit threads
  • retrospective classification
  • early warning mode
  • negativity share
  • escalation level
  • contributor count

Claims Made by the Document

  • Online firestorms can cause substantial damage.
  • Existing detection systems are not effective in capturing contextual meaning shifts.
  • The proposed LLM-based system improves classification performance and early warning detection of online firestorms.
  • LLMs can be used for dynamic monitoring of social media discourse.

Evidence Used

  • Online firestorms may cause substantial reputational and economic damage
  • Such signals capture contextual meaning shifts indirectly
  • The global mode achieves strong classification performance
  • The early warning mode reaches high recall and detects escalating threads quickly

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.