Mental Distress in English Posts from r/AmITheAsshole Subreddit Community with Language Models

Mental distress emerged as a significant area of concern. However, previous research predominantly concentrated on the detection and classification of mental issues, with limited attention given to comprehensive investigations of the interrelationships and contextual events associated with these issues. This research delves into the exploration of mental distress derived from user-generated content on the r/AmItheAsshole subreddit. The experimental data contains 1,888,423 words from 5051 English posts. Through the utilization of NLP techniques, including emotion classification, topic modeling, and semantic role labeling, the study revealed a significant prevalence of negative emotions (94 %) such as sadness, anger, and fear in the analyzed content. Furthermore, a combined BERTopic and narrative analysis shed light on the trivial origins of these conflicts. A potentialcorrelation between heightened intimacy and an increased occurrence of disagreements was discerned. As online communities become increasingly instrumental in people’s daily lives, this paper emphasizes their potential in providing invaluable insights to psychologists and sociologists, thereby enabling the formulation of effective strategies and interventions. We recommend educational endeavors to nurture empathy and promote effective communication. Our dataset and codes have been uploaded to GitHub.

Authors

Publication Details

Journal
Universität Zürich, ZORA
Published
2025-04-23
DOI
https://doi.org/10.5167/uzh-291401
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mental Distress in English Posts from r/AmITheAsshole Subreddit Community with Language Models

Qinghao Guan
Universität Zürich, ZORA
Mental Health via Writing
article

Mental Distress in English Posts from r/AmITheAsshole Subreddit Community with Language Models

Qinghao Guan
article en

Abstract

Mental distress emerged as a significant area of concern. However, previous research predominantly concentrated on the detection and classification of mental issues, with limited attention given to comprehensive investigations of the interrelationships and contextual events associated with these issues. This research delves into the exploration of mental distress derived from user-generated content on the r/AmItheAsshole subreddit. The experimental data contains 1,888,423 words from 5051 English posts. Through the utilization of NLP techniques, including emotion classification, topic modeling, and semantic role labeling, the study revealed a significant prevalence of negative emotions (94 %) such as sadness, anger, and fear in the analyzed content. Furthermore, a combined BERTopic and narrative analysis shed light on the trivial origins of these conflicts. A potentialcorrelation between heightened intimacy and an increased occurrence of disagreements was discerned. As online communities become increasingly instrumental in people’s daily lives, this paper emphasizes their potential in providing invaluable insights to psychologists and sociologists, thereby enabling the formulation of effective strategies and interventions. We recommend educational endeavors to nurture empathy and promote effective communication. Our dataset and codes have been uploaded to GitHub.

Universität Zürich, ZORA
Reduced inequalities
Openalex Percentile: Top 20%
Mental Health via Writing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.