在线风暴的情境化早期检测：一种基于序列语言模型的方法
Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach
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中文总结 AI 辅助
研究在线风暴的早期检测，提出基于语言模型的检测系统，有回顾性分类和顺序处理两种模式，在Reddit数据集上实验，全局模式分类性能强，早期警告模式召回率高，证明语言模型可用于情境感知监测。
中文摘要 AI 辅助
在线风暴是高度负面的用户生成内容的快速集体升级，可能造成重大声誉和经济损害。现有探测器通常处理数量信号、情感分数或预定义语言特征，只能间接捕捉不断演变的讨论线程中的情境意义变化。本文提出一种基于语言模型的检测系统，有两种操作模式。第一种模式通过将局部块级评估组合成线程级判断来回顾性地分类完整的Reddit线程。第二种模式顺序处理线程，当滑动窗口超过校准阈值时发出早期警告。在平衡的Reddit数据集上，全局模式实现了强大的分类性能，而早期警告模式达到了高召回率，并且在只有少量评论和不同贡献者之后就能检测到升级的线程。结果表明，语言模型不仅可用于静态判断任务，还可作为社交媒体话语情境感知监测中的重复估计器。
英文摘要
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.
