Mental disorders can severely affect quality of life, constitute a major predictive factor of suicide, and are usually underdiagnosed and undertreated. Early detection of signs of mental health problems is particularly important, since unattended, they can be life-threatening. This is why a deep understanding of the complex manifestations of mental disorder development is important. We present a study of mental disorders in social media, from different perspectives. We are interested in understanding whether monitoring language in social media could help with early detection of mental disorders, using computational methods. We developed deep learning models to learn linguistic markers of disorders, at different levels of the language (content, style, emotions), and further try to interpret the behavior of our models for a deeper understanding of mental disorder signs. We complement our prediction models with computational analyses grounded in theories from psychology related to cognitive styles and emotions, in order to understand to what extent it is possible to connect cognitive styles with the communication of emotions over time. The final goal is to distinguish between users diagnosed with a mental disorder and healthy users, in order to assist clinicians in diagnosing patients. We consider three different mental disorders, which we analyze separately and comparatively: depression, anorexia, and self-harm tendencies.

精神障碍会严重影响生活质量，构成自杀的主要预测因素，并且通常未被充分诊断和治疗。及早发现心理健康问题的迹象尤为重要，因为如果无人看管，它们可能会危及生命。这就是为什么深入了解精神障碍发展的复杂表现很重要。我们从不同的角度对社交媒体中的精神障碍进行了研究。我们有兴趣了解监控社交媒体中的语言是否有助于使用计算方法及早发现精神障碍。我们开发了深度学习模型来学习语言的不同层次（内容、风格、情感）的障碍的语言标记，并进一步尝试解释我们模型的行为，以更深入地了解精神障碍迹象。我们通过基于与认知风格和情绪相关的心理学理论的计算分析来补充我们的预测模型，以了解随着时间的推移将认知风格与情绪交流联系起来的可能性有多大。最终目标是区分诊断出患有精神障碍的用户和健康用户，以帮助临床医生诊断患者。我们考虑三种不同的精神障碍，分别进行比较分析：抑郁症、厌食症和自残倾向。为了了解随着时间的推移，将认知风格与情绪交流联系起来的可能性有多大。最终目标是区分诊断出患有精神障碍的用户和健康用户，以帮助临床医生诊断患者。我们考虑三种不同的精神障碍，分别进行比较分析：抑郁症、厌食症和自残倾向。为了了解随着时间的推移，将认知风格与情绪交流联系起来的可能性有多大。最终目标是区分诊断出患有精神障碍的用户和健康用户，以帮助临床医生诊断患者。我们考虑三种不同的精神障碍，分别进行比较分析：抑郁症、厌食症和自残倾向。