Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
Elsbeth Turcan, Kathleen McKeown
TL;DR
Dreaddit introduces a multi-domain Reddit corpus aimed at stress detection in long-form text, with 187K–190K posts and 3.5K MTurk-labeled segments across five domains. The authors establish robust baselines using traditional ML with domain-specific embeddings and high-correlation lexical/sociolinguistic features, showing competitive performance to BERT bases while highlighting interpretability advantages. Analyses reveal domain-specific linguistic patterns and the importance of lexical cues, with annotator agreement impacting lexical diversity and model performance. The work demonstrates the feasibility of stress detection in social media beyond microblogs and sets the stage for interpretable, context-aware stress analysis and distant labeling approaches.
Abstract
Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.