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Sensing Subjective Well-being from Social Media
Bibo Hao1; Lin Li2; Rui Gao1; Ang Li1; Tingshao Zhu1
第一作者Bibo Hao
2014
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通讯作者邮箱tszhu@psych.ac.cn
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摘要

Subjective Well-being(SWB), which refers to how people ex-
perience the quality of their lives, is of great use to public policy-makers
as well as economic, sociological research, etc. Traditionally, the mea-
surement of SWB relies on time-consuming and costly self-report ques-
tionnaires. 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.

关键词Subjective Well-being Social Media Machine Learning
DOI10.1007/978-3-319-09912-5_27 · Source: arXiv
语种英语
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文献类型会议论文
条目标识符http://ir.psych.ac.cn/handle/311026/26581
专题社会与工程心理学研究室
作者单位1.fInstitute of Psychology, University of Chinese Academy of Sciencesg, CAS
2.School of Humanities and Social Sciences, Nanyang Technological University
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Bibo Hao,Lin Li,Rui Gao,et al. Sensing Subjective Well-being from Social Media[C],2014.
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