Sensing Subjective Well-being from Social Media
This provides a low-cost, timely method for public policy-makers and researchers to assess SWB in large populations, though it is incremental as it applies existing machine learning methods to a new data source.
The paper tackles the problem of measuring subjective well-being (SWB) by proposing to sense it from social media data instead of traditional self-report questionnaires, achieving state-of-the-art prediction accuracy on a dataset of 1785 users.
Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.