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. Our model, which attains the state-of-the-art prediction accuracy, can then be applied to identify large amount of social media users’ SWB in time with low cost.
Most recent work of Schwartz, generalize their method to LS prediction @cite_21 . Their work used LS as a single indicator of SWB, and established model to predict the LS of each counties in the U.S.A through Twitter data. In their work, county is the unit to predict the LS, rather than individual. Their method, mainly analyze linguistic features on social media. Furthermore, their model introduced variables like median age'', median household income'' and educational attainment'', which can only be obtained via costly census.
@cite_21
6274171
The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association.