Garg Muskan, Gaur Manas, Goswami Raxit, Sohn Sunghwan
Mayo Clinic, Rochester, MN, USA.
University of Maryland, Baltimore County, MD, USA.
Conf Proc IEEE Int Conf Syst Man Cybern. 2023 Oct;2023:3854-3859. doi: 10.1109/smc53992.2023.10394671. Epub 2024 Jan 29.
Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burden-someness (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 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.
低自尊和人际需求（即归属感受挫（TB）和感知到的负担感（PB））对抑郁和自杀未遂有重大影响。个体在社交媒体上寻求社会联系以增强和减轻孤独感。社交媒体平台允许人们表达自己的想法、经历、信念和情感。先前关于社交媒体心理健康的研究集中在症状、原因和障碍方面。而对社交媒体内容进行人际风险因素和低自尊的初步筛查可能会发出早期警报，并为有精神障碍风险的用户分配治疗师。标准化量表通过使用心理学理论创建的问题来衡量自尊和人际需求。在当前的研究中，我们引入了一个基于心理学且经过专家注释的数据集LoST：低自尊，用于在Reddit上进行研究和检测。通过一种涉及连贯性、正确性、一致性和可靠性检查的注释方法，我们确保了监督学习的黄金标准。我们展示了使用两种数据增强技术测试的不同深度语言模型的结果。我们的研究结果表明，要开发一类融入心理学和临床知识的语言模型。
