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

arXiv: https://arxiv.org/abs/2607.14957 · HTML: https://mlsanity.com/paper/2607.14957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs](https://mlsanity.com/paper/2404.10308/) (2024)
- [The NLP Task Effectiveness of Long-Range Transformers](https://mlsanity.com/paper/2202.07856/) (2022)
- [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022)
- [Context-Aware Hierarchical Merging for Long Document Summarization](https://mlsanity.com/paper/2502.00977/) (2025)
- [ChuLo: Chunk-Level Key Information Representation for Long Document Understanding](https://mlsanity.com/paper/2410.11119/) (2024)
- [Against the Others! Detecting Moral Outrage inSocial Media Networks](https://mlsanity.com/paper/2010.07237/) (2020)

## Abstract (arXiv)

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.