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.

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.

People who think an almighty deity created the world are more likely to believe in conspiracy theories, scientists have discovered. Experts found the religion or God a person believes in is irrelevant, believers are still more likely to think the moon landing was a hoax or JFK was killed by the CIA. Scientists claim the'previously unidentified' link between the two is down to a brain bias that connects unrelated events. Teleological thinkers are willing to accept statements such as'the sun rises in order to give us light' and'the purpose of bees is to ensure pollination' as true. They hope that drawing the link between the two errors in rational thinking can highlight the'major flaws of conspiracy theories'.

They might be tall, dark and handsome but men on the dating app Tinder suffer from low self-esteem, a study found. A survey of more than 1,300 men and women revealed that those who use the highly popular smartphone app tend to be less happy with their looks. Psychologists warn the app could be bad for your health, with users drawn into a downward spiral of physical comparisons. Researchers looked at more than 1,300 undergraduate students and asked them to complete questionnaires based on psychological state. They found that one in ten of them used the Tinder app.