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

• URL: http://arxiv.org/abs/2305.18736v1
• Date: Tue, 30 May 2023 04:21:24 GMT
• Title: LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts
• Authors: Muskan Garg, Chandni Saxena, Debabrata Samanta, Bonnie J. Dorr
• Abstract summary: Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP) Existing literature on psychological theories points to loneliness as the major consequence of interpersonal risk factors. We formulate lonesomeness detection in social media posts as an explainable binary classification problem.
• Score: 0.41998444721319217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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