[论文解读] Predicting Depressive Symptoms through Emotion Pairs within Asian American Families

Sangpil Youm, Nari Yoo+1 more|arXiv (Cornell University)|Feb 3, 2026

Mental Health via Writing被引用 0

一句话总结

该论文从关于亚裔父母的Reddit帖子中提取情感，构建情感网络，并使用逻辑回归将情感对与抑郁症状联系起来。

ABSTRACT

Studies on intergenerational relationships between parents and children in Asian American families highlight their impact on mental health and well-being. This study investigates the role of ambivalent emotions in online narratives shared by Asian and Asian American children on the subreddit, r/Asianparentstories. By employing a BERT-based model to detect emotion at the sentence level and depressive symptoms at the post level, we analyze mixed feelings to better understand how they predict depressive symptoms. First, among 28 detectable, eight (realization, approval, sadness, anger, curiosity, annoyance, disappointment, disapproval) comprise over 50%, exhibiting significant co-occurrence among themselves and with other emotions. Second, we find the co-occurrence of multiple emotions, indicating that emotions in a single post are not limited to consistently positive or negative feelings. Finally, our findings indicate that while negative emotion pairs (e.g., confusion-grief, anger-grief) are associated with depressive symptoms, positive emotion pairs (e.g., admiration-realization, amusement-joy) negatively correlate with depressive symptoms, and combinations of ambivalent emotions indicate varied results in predicting depressive symptoms. These findings highlight the importance of automated emotion classification and the need to consider emotional ambivalence, which holds practical and clinical implications for understanding the dynamics of parent-child relationships.

研究动机与目标

• 理解亚裔美国家庭代际矛盾及其与心理健康之间关系的必要性。
• 使用Reddit叙述捕捉超越调查与访谈的真实情感语境。
• 识别能显著预测抑郁症状的情感对。
• 描述帖子中的主导情感及其互动模式，以指导具有文化敏感性的干预。

提出的方法

• 用 EmoRoBERTa 在句子层面检测情感。
• 构建每篇帖子的情感网络，其中节点为情感，边的粗细反映共现频率。
• 使用 DepRoBERTa 识别帖子中的抑郁症状。
• 将情感对作为预测变量，抑郁症状作为结果，在逻辑回归模型中分析。
• 进行内容分析以解释显著情感对。
• 报告显著情感对及其比值比（例如 逗趣-悲痛 OR=2.62；悲伤-乐观 OR=0.79）。

Figure 1: Emotion relationship network. Note. Nodes represent emotions. Link thickness corresponds to size of degree, measured by frequency of co-occurrence

实验结果

研究问题

• RQ1 哪些情感对在关于亚裔父母的帖子中共现，这些对与抑郁症状有何关系？
• RQ2 哪些具体情感对在数据集中显著预测抑郁症状？
• RQ3 在Reddit关于亚裔父母-子女经历的帖子中，主导情感和共现模式是什么？
• RQ4 混合/矛盾情感是否比单一情感与抑郁症状的关系更强？
• RQ5 如何将发现用于为亚裔美国家庭设计具有文化敏感性的家庭干预？

主要发现

• 八种情感（领悟、认同/认可、悲伤、愤怒、反对、恼怒、好奇、失望）约占检测到情感的50%左右。
• 帖子常包含多种情感，情感对与情感三联体较为常见。
• 逗趣-悲痛是最具影响力的情感对，与抑郁症状呈正相关（OR=2.62）。
• 关爱-好奇是与抑郁症状最强负相关的情感对。
• 悲伤与乐观的结合显示抑郁症状的负相关（OR=0.79）。
• 在 p<0.05 时，十个情感对显著贡献于抑郁症状。

Figure 2: Cumulative Distribution Function (CDF) and Complementary Cumulative Distribution Function (CCDF).