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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Besim Shala, Peter Mandl, Andreas Humpe, Martin Häusl
• Venue: arXiv: cs.AI
• Published: 2026-07-16
• Collected: 2026-07-17
• URL: http://arxiv.org/abs/2607.14957v1
• PDF: https://arxiv.org/pdf/2607.14957v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces an LLM-based system to detect online firestorms (rapid escalations of negative user-generated content) on Reddit. The system operates in two modes: retrospective thread classification and early warning mode that issues alerts when a sliding window exceeds specific thresholds. In the early warning mode, the language model evaluates three indicators: negativity share, escalation level, and contributor count.

What the Document Says

• Online firestorms are rapid collective escalations of negative user-generated content.
• Existing detectors work with volume signals, sentiment scores, or predefined linguistic features.
• The paper proposes an LLM-based detection system in two operating modes: retrospective and early warning mode.
• In the retrospective mode, the system classifies complete Reddit threads retrospectively by combining local chunk-level assessments into a thread-level judgment.
• In the early warning mode, the language model estimates three firestorm indicators: negativity share, escalation level, and contributor count.
• The system achieves strong classification performance in global mode on a balanced Reddit dataset.
• The early warning mode reaches high recall and detects escalating threads after only a small number of comments and distinct contributors.
• The results suggest that LLMs can be used for context-aware monitoring of social media discourse.

Key Concepts Mentioned

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

Claims Made by the Document

• The proposed LLM-based system can effectively detect online firestorms.
• The early warning mode of the system has high recall and quickly identifies escalating threads.

Evidence Used

• results on a balanced Reddit dataset

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.