Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts. Individuals seek social connectedness on social media to boost and alleviate their loneliness. Social media platforms allow people to express their thoughts, experiences, beliefs, and emotions. Prior studies on mental health from social media have focused on symptoms, causes, and disorders. Whereas an initial screening of social media content for interpersonal risk factors and low self-esteem may raise early alerts and assign therapists to at-risk users of mental disturbance. Standardized scales measure self-esteem and interpersonal needs from questions created using psychological theories. In the current research, we introduce a psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to study and detect low self-esteem on Reddit. Through an annotation approach involving checks on coherence, correctness, consistency, and reliability, we ensure gold-standard for supervised learning. We present results from different deep language models tested using two data augmentation techniques. Our findings suggest developing a class of language models that infuses psychological and clinical knowledge.

翻译：低自尊和人际需求（即归属感受挫（TB）与累赘感知（PB））对抑郁和自杀企图具有重大影响。个体通过社交媒体寻求社会联系以缓解孤独感。社交媒体平台允许人们表达思想、经历、信念和情感。先前基于社交媒体的心理健康研究主要关注症状、原因及障碍。然而，对社交媒体内容中人际风险因素和低自尊进行初步筛查，可能为心理健康风险用户提供早期预警并指派治疗师。标准化量表通过基于心理学理论设计的问题测量自尊和人际需求。在本研究中，我们引入了一个基于心理学且经专家标注的数据集LoST：低自尊（Low Self esTeem），用于研究和检测Reddit上的低自尊现象。通过包含一致性、正确性、连贯性和可靠性检查的标注方法，我们确保了监督学习的黄金标准。我们展示了采用两种数据增强技术测试的不同深度语言模型的结果。研究结果表明，开发融合心理学与临床知识的语言模型类别具有必要性。