How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets

TL;DR

This study analyzes stress and relaxation expressions on Twitter using content analysis and machine learning, revealing geographic variations and potential for supplementing traditional stress surveys.

Contribution

It introduces a combined qualitative and machine learning approach to classify stress and relaxation tweets and correlates findings with public stress surveys.

Findings

Stress tweets often relate to education, work, and social relationships.

Relaxation tweets frequently mention rest, vacation, nature, and water.

Higher stress tweet proportions were found in New York and San Diego.

Abstract

Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Objective: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. Methods: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords "stress" and "relax", respectively. We then investigated the use of machine learning algorithms to automatically classify tweets as stress versus non stress and relaxation versus non relaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. Results: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest and vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. Conclusions: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data.