Abstract
An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these "Quantified Selfs"many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social sur-roundings and general online behavior. Concretely, we look at which social media features are predictive of physical sta-Tus, such as body weight at the individual level, and activity patterns at the population level. We show that it is possi-ble to predict an individual's weight using their online social behaviors, such as their self-description and tweets. Weekly and monthly patterns of quantified-self behaviors are also discovered. These findings could contribute to building mod-els to monitor public health and to have more customized personal training interventions. While there are many studies using either quantified self or social media data in isolation, this is one of the few that combines the two data sources and, to the best of our knowl-edge, the only one that uses public data.
| Original language | English |
|---|---|
| Title of host publication | DH 2016 - Proceedings of the 2016 Digital Health Conference |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 93-97 |
| Number of pages | 5 |
| ISBN (Print) | 9781450342247 |
| DOIs | |
| Publication status | Published - 11 Apr 2016 |
| Event | 6th International Conference on Digital Health, DH 2016 - Montreal, Canada Duration: 11 Apr 2016 → 13 Apr 2016 |
Other
| Other | 6th International Conference on Digital Health, DH 2016 |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 11/4/16 → 13/4/16 |
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Keywords
- Quantified Self
- Smart Scales
ASJC Scopus subject areas
- Health Information Management
- Computer Science Applications
- Health Informatics
Cite this
Quantified self meets social media : Sharing of weight updates on twitter. / Wang, Yafei; Weber, Ingmar; Mitra, Prasenjit.
DH 2016 - Proceedings of the 2016 Digital Health Conference. Association for Computing Machinery, Inc, 2016. p. 93-97.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quantified self meets social media
T2 - Sharing of weight updates on twitter
AU - Wang, Yafei
AU - Weber, Ingmar
AU - Mitra, Prasenjit
PY - 2016/4/11
Y1 - 2016/4/11
N2 - An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these "Quantified Selfs"many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social sur-roundings and general online behavior. Concretely, we look at which social media features are predictive of physical sta-Tus, such as body weight at the individual level, and activity patterns at the population level. We show that it is possi-ble to predict an individual's weight using their online social behaviors, such as their self-description and tweets. Weekly and monthly patterns of quantified-self behaviors are also discovered. These findings could contribute to building mod-els to monitor public health and to have more customized personal training interventions. While there are many studies using either quantified self or social media data in isolation, this is one of the few that combines the two data sources and, to the best of our knowl-edge, the only one that uses public data.
AB - An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these "Quantified Selfs"many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social sur-roundings and general online behavior. Concretely, we look at which social media features are predictive of physical sta-Tus, such as body weight at the individual level, and activity patterns at the population level. We show that it is possi-ble to predict an individual's weight using their online social behaviors, such as their self-description and tweets. Weekly and monthly patterns of quantified-self behaviors are also discovered. These findings could contribute to building mod-els to monitor public health and to have more customized personal training interventions. While there are many studies using either quantified self or social media data in isolation, this is one of the few that combines the two data sources and, to the best of our knowl-edge, the only one that uses public data.
KW - Quantified Self
KW - Smart Scales
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84966565243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966565243&partnerID=8YFLogxK
U2 - 10.1145/2896338.2896363
DO - 10.1145/2896338.2896363
M3 - Conference contribution
AN - SCOPUS:84966565243
SN - 9781450342247
SP - 93
EP - 97
BT - DH 2016 - Proceedings of the 2016 Digital Health Conference
PB - Association for Computing Machinery, Inc
ER -