Conference Proceedings
PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA
H Andrew Schwartz, Maarten Sap, Margaret L Kern, Johannes C Eichstaedt, Adam Kapelner, Megha Agrawal, Eduardo Blanco, Lukasz Dziurzynski, Gregory Park, David Stillwell, Michal Kosinski, Martin EP Seligman, Lyle H Ungar, RB Altman (ed.), AK Dunker (ed.), L Hunter (ed.), MD Ritchie (ed.), T Murray (ed.), TE Klein (ed.)
PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016 | WORLD SCIENTIFIC PUBL CO PTE LTD | Published : 2016
Abstract
We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded mo..
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