  A body of literature has demonstrated that users' mental health conditions,
such as depression and anxiety, can be predicted from their social media
language. There is still a gap in the scientific understanding of how
psychological stress is expressed on social media. Stress is one of the primary
underlying causes and correlates of chronic physical illnesses and mental
health conditions. In this paper, we explore the language of psychological
stress with a dataset of 601 social media users, who answered the Perceived
Stress Scale questionnaire and also consented to share their Facebook and
Twitter data. Firstly, we find that stressed users post about exhaustion,
losing control, increased self-focus and physical pain as compared to posts
about breakfast, family-time, and travel by users who are not stressed.
Secondly, we find that Facebook language is more predictive of stress than
Twitter language. Thirdly, we demonstrate how the language based models thus
developed can be adapted and be scaled to measure county-level trends. Since
county-level language is easily available on Twitter using the Streaming API,
we explore multiple domain adaptation algorithms to adapt user-level Facebook
models to Twitter language. We find that domain-adapted and scaled social
media-based measurements of stress outperform sociodemographic variables (age,
gender, race, education, and income), against ground-truth survey-based stress
measurements, both at the user- and the county-level in the U.S. Twitter
language that scores higher in stress is also predictive of poorer health, less
access to facilities and lower socioeconomic status in counties. We conclude
with a discussion of the implications of using social media as a new tool for
monitoring stress levels of both individuals and counties.
