LoST:Reddit 帖子中的低自尊心理健康数据集。

Muskan Garg, Manas Gaur, Raxit Goswami, Sunghwan Sohn
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引用次数: 0

摘要

低自尊和人际需求(即归属感受挫(TB)和感知到的负担感(PB))对抑郁症和自杀企图有重大影响。个人在社交媒体上寻求社会联系,以增强和缓解他们的孤独感。社交媒体平台允许人们表达自己的想法、经历、信仰和情感。之前有关社交媒体带来的心理健康的研究主要集中在症状、原因和失调方面。而对社交媒体内容中的人际交往风险因素和自卑感进行初步筛查,则可以提高早期预警,并为有精神障碍风险的用户指派治疗师。标准化量表通过使用心理学理论创建的问题来测量自尊和人际需求。在当前的研究中,我们引入了一个以心理学为基础并经过专家注释的数据集--LoST:Low Self esTeem,用于研究和检测 Reddit 上的低自尊。通过对一致性、正确性、一致性和可靠性进行检查的注释方法,我们确保了监督学习的黄金标准。我们介绍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,开发一类注入了心理学和临床知识的语言模型是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LoST: A Mental Health Dataset of Low Self-esteem in Reddit Posts.

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 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.

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