Why Antiwork: A RoBERTa-Based System for Work-Related Stress Identification and Leading Factor Analysis

TL;DR

This paper tackles work-related stress and antiwork sentiment by leveraging Reddit data to identify underlying causes. It proposes a RoBERTa-based feature extractor with an RNN to detect antiwork propensity and highlight sentiment-driving words, complemented by LIWC-based linguistic analysis and LDA topic modeling for interpretation. The approach achieves about 80% accuracy and a 0.79 F1 score, identifying leading antiwork factors as harsh environments, frustrating recruiting experiences, and unfair compensation, while linking antiwork to reduced self-confidence. The work offers a data-driven, explainable view of workplace dissatisfaction with implications for worker rights and organizational practices, and discusses ethical safeguards around data sharing and misuse risk, acknowledging limitations in generalizability and labeling noise.

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 to uncover some of the key insights into the mindset of individuals who identify with the antiwork movement and how their working environments influenced them. We find that working environments that do not give employees authority or responsibility, frustrating recruiting experiences, and unfair compensation, are some of the leading causes of the antiwork sentiment, resulting in a lack of self-confidence and motivation among their employees.

Figures (9)

• Figure 1: Data structure of Antiwork Reddit Dataset.
• Figure 2: Architecture of our model
• Figure 3: Overview of the application of integrated gradient. Red for words that positively contribute to antiwork prediction and green for words that negatively contribute to antiwork prediction.
• Figure 4: Visualization of word attributions
• Figure 5: Top-10 most salient terms from Topic Modeling. Each row shows the saliency of a certain word to the dataset. The longer the bar in the row is, the more salient the word is.