Why Antiwork: A RoBERTa-Based System for Work-Related Stress Identification and Leading Factor Analysis
Tao Lu, Muzhe Wu, Xinyi Lu, Siyuan Xu, Shuyu Zhan, Anuj Tambwekar,, Emily Mower Provost

TL;DR
This paper introduces a RoBERTa-based system that detects work-related stress and antiwork sentiments from social media data, providing explainable insights into workplace dissatisfaction and its root causes.
Contribution
It creates a novel dataset from r/antiwork, trains a model for sentiment detection, and analyzes key factors leading to work-related stress and antiwork sentiments.
Findings
Work environments lacking authority and responsibility increase antiwork sentiment.
Frustrating recruiting experiences contribute to employee dissatisfaction.
Unfair compensation is a major cause of antiwork feelings.
Abstract
Harsh working environments and work-related stress have been known to contribute to mental health problems such as anxiety, depression, and suicidal ideation. As such, it is paramount to create solutions that can both detect employee unhappiness and find the root cause of the problem. While prior works have examined causes of mental health using machine learning, they typically focus on general mental health analysis, with few of them focusing on explainable solutions or looking at the workplace-specific setting. r/antiwork is a subreddit for the antiwork movement, which is the desire to stop working altogether. Using this subreddit as a proxy for work environment dissatisfaction, we create a new dataset for antiwork sentiment detection and subsequently train a model that highlights the words with antiwork sentiments. Following this, we performed a qualitative and quantitative analysis…
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Taxonomy
TopicsOccupational Health and Safety Research
MethodsFocus
