How do people cope when disaster strikes and can we detect it at scale, in real time, from what they write? This study addresses that question using over one million Turkish-language tweets posted in the aftermath of the February 6, 2023 earthquake in Turkiye, which unfolded in a deeply polarized political context just months before a national election. Drawing on Lazarus and Folkman's (1984) coping theory, we develop a multi-label BERTurk classifier to detect three coping styles (problem-focused, emotion-focused, and meaning-making) across four theoretically motivated crisis phases. BERTurk achieves a macro F1 of 0.693, substantially outperforming a zero-shot mDeBERTa baseline (macro F1 = 0.324). Applied to the full corpus, the classifier reveals a clear temporal trajectory: problem-focused coping dominates the urgency phase and declines sharply, emotion-focused coping rises and stabilizes, and meaning-making increases monotonically. Anger correlates most strongly with meaning-making (Spearman r = 0.387), suggesting it functions as a mobilizing force toward blame attribution rather than practical action. These findings demonstrate that coping theory can be reliably operationalized in real-world digital crisis data and that doing so can help humanitarian organizations tailor their responses to where a population actually is.

翻译：灾难来袭时人们如何应对？我们能否从他们的文字中大规模、实时地检测到这些应对方式？本研究基于2023年2月6日土耳其地震后在高度两极化的政治背景下（距离全国大选仅数月）发布的一百多万条土耳其语推文，对这一问题展开探究。借鉴拉扎勒斯和福克曼（1984）的应对理论，我们开发了一个多标签BERTurk分类器，用于检测四种理论驱动危机阶段中的三种应对风格（问题聚焦型、情绪聚焦型和意义建构型）。BERTurk的宏F1值达到0.693，显著优于零样本mDeBERTa基线模型（宏F1=0.324）。将该分类器应用于完整语料库后，揭示了清晰的时间轨迹：问题聚焦型应对在紧急阶段占据主导地位并急剧下降，情绪聚焦型应对先上升后趋于稳定，而意义建构型应对则单调递增。愤怒情绪与意义建构型应对的相关性最强（斯皮尔曼相关系数r=0.387），这表明愤怒在此情境下更趋向于推动责任归因而非实际行动。这些发现表明，应对理论可在现实数字危机数据中得到可靠操作化，且这种操作化有助于人道主义组织根据人群实际心理状态调整响应策略。