Lyrics play a crucial role in affecting and reinforcing emotional states by providing meaning and emotional connotations that interact with the acoustic properties of the music. Specific lyrical themes and emotions may intensify existing negative states in listeners and may lead to undesirable outcomes, especially in listeners with mood disorders such as depression. Hence, it is important for such individuals to be mindful of their listening strategies. In this study, we examine online music consumption of individuals at risk of depression in light of lyrical themes and emotions. Lyrics obtained from the listening histories of 541 Last.fm users, divided into At-Risk and No-Risk based on their mental well-being scores, were analyzed using natural language processing techniques. Statistical analyses of the results revealed that individuals at risk for depression prefer songs with lyrics associated with low valence and low arousal. Additionally, lyrics associated with themes of denial, self-reference, and ambivalence were preferred. In contrast, themes such as liberation, familiarity, and activity are not as favored. This study opens up the possibility of an approach to assessing depression risk from the digital footprint of individuals and potentially developing personalized recommendation systems.

翻译：歌词通过提供与音乐声学特性相互作用的意义和情感内涵，在影响和强化情绪状态方面发挥着关键作用。特定的歌词主题和情感可能会加剧听众现有的负面状态，并可能导致不良后果，尤其对于患有抑郁症等情绪障碍的听众而言。因此，这类个体有必要注意其聆听策略。本研究基于歌词主题和情感，考察了具有抑郁风险的个体的在线音乐消费行为。我们从541名Last.fm用户的聆听历史中获取歌词，根据其心理健康得分将其分为"风险组"和"无风险组"，并采用自然语言处理技术进行分析。结果的统计分析表明，抑郁风险个体更偏好歌词情感效价和唤醒度均较低的歌曲。此外，他们更倾向于选择涉及否认、自我指涉和矛盾情感主题的歌词。相比之下，解放、熟悉感和活动性等主题则不太受青睐。这项研究为通过个体数字足迹评估抑郁风险，并可能开发个性化推荐系统提供了一种新的可能性。