  Social media is a potential source of information that infers latent mental
states through Natural Language Processing (NLP). While narrating real-life
experiences, social media users convey their feeling of loneliness or isolated
lifestyle, impacting their mental well-being. Existing literature on
psychological theories points to loneliness as the major consequence of
interpersonal risk factors, propounding the need to investigate loneliness as a
major aspect of mental disturbance. We formulate lonesomeness detection in
social media posts as an explainable binary classification problem, discovering
the users at-risk, suggesting the need of resilience for early control. To the
best of our knowledge, there is no existing explainable dataset, i.e., one with
human-readable, annotated text spans, to facilitate further research and
development in loneliness detection causing mental disturbance. In this work,
three experts: a senior clinical psychologist, a rehabilitation counselor, and
a social NLP researcher define annotation schemes and perplexity guidelines to
mark the presence or absence of lonesomeness, along with the marking of
text-spans in original posts as explanation, in 3,521 Reddit posts. We expect
the public release of our dataset, LonXplain, and traditional classifiers as
baselines via GitHub.
