Most adolescents with depression remain undiagnosed and untreated-missed opportunities that are costly from both personal and public health perspectives. A promising approach to detecting adolescent depression in real-time and at a large scale is through their social communication on the smartphone (e.g., text messages, social media posts). Past research has shown that language from online social communication reliably indicates interindividual differences in depression. To move toward detecting the emergence of depression symptoms intraindividually, the present study tested whether sentiment (i.e., words connoting positive and negative affect) from smartphone social communication prospectively predicted daily mood fluctuations in 83 adolescents (Mage = 16.49, 73.5% female) with a wide range of depression severity. Participants completed daily mood ratings across a 90-day period, during which 354,278 messages were passively collected from social communication apps. Greater positive sentiment (i.e., more positive weighted composite valence score and a greater proportion of words expressing positive sentiment) predicted more positive next-day mood, controlling for previous-day mood. Moreover, greater proportions of positive and negative sentiment were, respectively, associated with lower anhedonia and greater dysphoria symptoms measured at baseline. Exploratory analyses of nonaffective linguistic features showed that greater use of social engagement words (e.g., friends and affiliation) and emojis (primarily consisting of hearts) predicted more positive changes in mood. Collectively, findings suggest that language from smartphone social communication can detect mood fluctuations in adolescents, laying the foundation for language-based tools to identify periods of heightened depression risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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通过青少年智能手机社交交流捕捉情绪动态。


大多数患有抑郁症的青少年仍未得到诊断和治疗，错过了从个人和公共健康角度来看代价高昂的机会。实时大规模检测青少年抑郁症的一种有前途的方法是通过智能手机上的社交交流（例如短信、社交媒体帖子）。过去的研究表明，在线社交交流中的语言可靠地表明了抑郁症的个体差异。为了进一步检测个体抑郁症状的出现，本研究测试了 83 名青少年（Mage = 16.49，73.5% 女性）的智能手机社交交流中的情绪（即表示积极和消极情绪的词语）是否能够前瞻性预测日常情绪波动。抑郁症严重程度范围广泛。参与者在 90 天内完成了每日情绪评级，在此期间从社交通讯应用程序被动收集了 354,278 条消息。在控制前一天情绪的情况下，更大的积极情绪（即更积极的加权综合价分数和更大比例的表达积极情绪的单词）可以预测更积极的第二天情绪。此外，较高比例的积极情绪和消极情绪分别与基线时较低的快感缺失和较高的烦躁症状相关。对非情感语言特征的探索性分析表明，更多地使用社交词汇（例如朋友和关系）和表情符号（主要由心形组成）预示着情绪会发生更积极的变化。 总的来说，研究结果表明，智能手机社交交流中的语言可以检测青少年的情绪波动，为基于语言的工具识别抑郁风险升高的时期奠定了基础。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。