The social NLP research community witness a recent surge in the computational
advancements of mental health analysis to build responsible AI models for a
complex interplay between language use and self-perception. Such responsible AI
models aid in quantifying the psychological concepts from user-penned texts on
social media. On thinking beyond the low-level (classification) task, we
advance the existing binary classification dataset, towards a higher-level task
of reliability analysis through the lens of explanations, posing it as one of
the safety measures. We annotate the LoST dataset to capture nuanced textual
cues that suggest the presence of low self-esteem in the posts of Reddit users.
We further state that the NLP models developed for determining the presence of
low self-esteem, focus more on three types of textual cues: (i) Trigger: words
that triggers mental disturbance, (ii) LoST indicators: text indicators
emphasizing low self-esteem, and (iii) Consequences: words describing the
consequences of mental disturbance. We implement existing classifiers to
examine the attention mechanism in pre-trained language models (PLMs) for a
domain-specific psychology-grounded task. Our findings suggest the need of
shifting the focus of PLMs from Trigger and Consequences to a more
comprehensive explanation, emphasizing LoST indicators while determining low
self-esteem in Reddit posts.

社交 NLP 研究社区近期在心理健康分析的计算进展中见证了构建负责任的 AI 模型，以应对语言使用和自我认知之间的复杂相互作用。我们通过注释 LoST 数据集捕捉到 Reddit 用户帖子中表明低自尊的微妙文本线索，并发现 NLP 模型在确定低自尊时更关注三种类型的文本线索：触发词、LoST 指标和后果词。我们的研究结果表明，在 Reddit 帖子中确定低自尊时，需要将 PLMs 的重点从触发词和后果词转移到更全面的解释上，强调 LoST 指标。

用户笔文中心理概念抽取与分类的可靠性分析

Reliability Analysis of Psychological Concept Extraction and  Classification in User-penned Text

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.

本研究引入一项心理学基础和专家注释的数据集，旨在研究和检测 Reddit 上的低自尊心，并通过标注方法确保监督学习的金标准；本研究的结果表明，可以开发一类注入心理和临床知识的语言模型。