Abstract
Overweight is epidemic in the United States and elsewhere in the world, causing major health concerns. Based on self-disclosure theory, i.e., people have the tendency to disclose information concerning their feelings, intentions, and acts (e.g., food consumption) online, we aim to leverage social media platforms to develop an unobtrusive approach to predicting overweight. However, traditional statistical and machine learning-based approaches either deliver unsatisfactory performance or demand a large number of features. In this paper, we present a novel social media-based overweight prediction approach based on deep learning as applied in the context of Natural Language Processing (NLP). The input to this approach is food-related Twitter posts. Our computational results show the effectiveness of our method, with remarkable improvement in terms of accuracy over a set of benchmark methods.
| Original language | English (US) |
|---|---|
| Title of host publication | Americas Conference on Information Systems 2018 |
| Subtitle of host publication | Digital Disruption, AMCIS 2018 |
| Publisher | Association for Information Systems |
| ISBN (Print) | 9780996683166 |
| State | Published - Jan 1 2018 |
| Event | 24th Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018 - New Orleans, United States Duration: Aug 16 2018 → Aug 18 2018 |
Publication series
| Name | Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018 |
|---|
Conference
| Conference | 24th Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018 |
|---|---|
| Country | United States |
| City | New Orleans |
| Period | 8/16/18 → 8/18/18 |
Fingerprint
Keywords
- Deep learning
- Food
- Language
- Overweight
- Public health
ASJC Scopus subject areas
- Information Systems
Cite this
Social media-based overweight prediction using deep learning. / Huangfu, Luwen; Zeng, Dajun.
Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018. Association for Information Systems, 2018. (Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Social media-based overweight prediction using deep learning
AU - Huangfu, Luwen
AU - Zeng, Dajun
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Overweight is epidemic in the United States and elsewhere in the world, causing major health concerns. Based on self-disclosure theory, i.e., people have the tendency to disclose information concerning their feelings, intentions, and acts (e.g., food consumption) online, we aim to leverage social media platforms to develop an unobtrusive approach to predicting overweight. However, traditional statistical and machine learning-based approaches either deliver unsatisfactory performance or demand a large number of features. In this paper, we present a novel social media-based overweight prediction approach based on deep learning as applied in the context of Natural Language Processing (NLP). The input to this approach is food-related Twitter posts. Our computational results show the effectiveness of our method, with remarkable improvement in terms of accuracy over a set of benchmark methods.
AB - Overweight is epidemic in the United States and elsewhere in the world, causing major health concerns. Based on self-disclosure theory, i.e., people have the tendency to disclose information concerning their feelings, intentions, and acts (e.g., food consumption) online, we aim to leverage social media platforms to develop an unobtrusive approach to predicting overweight. However, traditional statistical and machine learning-based approaches either deliver unsatisfactory performance or demand a large number of features. In this paper, we present a novel social media-based overweight prediction approach based on deep learning as applied in the context of Natural Language Processing (NLP). The input to this approach is food-related Twitter posts. Our computational results show the effectiveness of our method, with remarkable improvement in terms of accuracy over a set of benchmark methods.
KW - Deep learning
KW - Food
KW - Language
KW - Overweight
KW - Public health
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85054242543&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054242543&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85054242543
SN - 9780996683166
T3 - Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018
BT - Americas Conference on Information Systems 2018
PB - Association for Information Systems
ER -