Abstract
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.
Operating Modes
- Retrospective Mode: This mode classifies complete Reddit threads retrospectively by combining local chunk-level assessments into a thread-level judgment.
- Early Warning Mode: This 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.
Results
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.
Blogger's Review: The proposed LLM-based detection system showcases an innovative approach to effectively identify potential crises in dynamic social networks. By integrating contextual information and real-time data analysis, this method offers a fresh perspective on public sentiment monitoring, warranting further attention and research.