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

arXiv: https://arxiv.org/abs/2305.18736 · HTML: https://mlsanity.com/paper/2305.18736/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 4 of 4)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes](https://mlsanity.com/paper/2206.06141/) (2022)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions](https://mlsanity.com/paper/2601.15353/) (2026)
- [Reliability Analysis of Psychological Concept Extraction and Classification in User-penned Text](https://mlsanity.com/paper/2401.06709/) (2024)

## Abstract (arXiv)

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.