  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.
