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

TL;DR

This paper introduces LonXplain, an explainable dataset and classification method for detecting loneliness in Reddit posts, aiming to identify at-risk users and facilitate early mental health interventions.

Contribution

It presents the first annotated, explainable dataset for loneliness detection in social media, along with baseline classifiers and a framework for interpretability in mental health NLP tasks.

Findings

Created and annotated the LonXplain dataset with expert input

Developed baseline classifiers for loneliness detection

Provided interpretability through human-readable explanations

Abstract

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.