  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.
