Year: 2019
Type: conference-paper
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.
DOI: 10.18653/v1/d19-6213
Authors: Elsbeth Turcan, Kathy McKeown
Institution: Columbia University
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W2987392802