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Social media is a potential source of information that infers latent mentalstates through Natural Language Processing (NLP). While narrating real-lifeexperiences, social media users convey their feeling of loneliness or isolatedlifestyle, impacting their mental well-being. Existing literature onpsychological theories points to loneliness as the major consequence ofinterpersonal risk factors, propounding the need to investigate loneliness as amajor aspect of mental disturbance. We formulate lonesomeness detection insocial media posts as an explainable binary classification problem, discoveringthe users at-risk, suggesting the need of resilience for early control. To thebest of our knowledge, there is no existing explainable dataset, i.e., one withhuman-readable, annotated text spans, to facilitate further research anddevelopment in loneliness detection causing mental disturbance. In this work,three experts: a senior clinical psychologist, a rehabilitation counselor, anda social NLP researcher define annotation schemes and perplexity guidelines tomark the presence or absence of lonesomeness, along with the marking oftext-spans in original posts as explanation, in 3,521 Reddit posts. We expectthe public release of our dataset, LonXplain, and traditional classifiers asbaselines via GitHub.

LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts