Evaluating Individual Subjective Well-being via Social Media

DOI

10.2991/ameii-16.2016.167How to use a DOI?

Keywords

subjective well-being, social media, semantic lexicon, feature extraction

Abstract

Subjective well-being (SWB) is now attracting more and more attention from policy makers. Traditional way of measuring SWB by self-reporting based on questionnaire has the limitation of small sample size and low survey frequency. With the rapid spread and use of social media, the online posts of users provide a new way to study SWB. But the existing work haven't gone far from word counting and ignore the components of SWB. In this paper we propose a new framework to evaluate individual SWB from social media. We leverage the language cues and introduce useful features to evaluate SWB. Moreover, we introduce a way to expand the SWB feature. To check the effectiveness of the framework, we crowdsource user tweets and their subjective well-being. Experiments on the dataset show that this way of evaluating SWB achieves a high accuracy in evaluating individual SWB.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - YaZhou Wang
PY  - 2016/04
DA  - 2016/04
TI  - Evaluating Individual Subjective Well-being via Social Media
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 859
EP  - 864
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.167
DO  - 10.2991/ameii-16.2016.167
ID  - Wang2016/04
ER  -