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The social NLP research community witness a recent surge in the computationaladvancements of mental health analysis to build responsible AI models for acomplex interplay between language use and self-perception. Such responsible AImodels aid in quantifying the psychological concepts from user-penned texts onsocial media. On thinking beyond the low-level (classification) task, weadvance the existing binary classification dataset, towards a higher-level taskof reliability analysis through the lens of explanations, posing it as one ofthe safety measures. We annotate the LoST dataset to capture nuanced textualcues 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 oflow self-esteem, focus more on three types of textual cues: (i) Trigger: wordsthat triggers mental disturbance, (ii) LoST indicators: text indicatorsemphasizing low self-esteem, and (iii) Consequences: words describing theconsequences of mental disturbance. We implement existing classifiers toexamine the attention mechanism in pre-trained language models (PLMs) for adomain-specific psychology-grounded task. Our findings suggest the need ofshifting the focus of PLMs from Trigger and Consequences to a morecomprehensive explanation, emphasizing LoST indicators while determining lowself-esteem in Reddit posts.

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