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Understanding and Predicting Weight Loss with Mobile Social Networking Data

Authors:

Jiliang TangAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 1269 - 1278

https://doi.org/10.1145/3132847.3133019

Published: 06 November 2017 Publication History

Abstract

It has become increasingly popular to use mobile social networking applications for weight loss and management. Users can not only create profiles and maintain their records but also perform a variety of social activities that shatter the barrier to share or seek information. Due to the open and connected nature, these applications produce massive data that consists of rich weight-related information which offers immense opportunities for us to enable advanced research on weight loss. In this paper, we conduct the initial investigation to understand weight loss with a large-scale mobile social networking dataset with near 10 million users. In particular, we study individual and social factors related to weight loss and reveal a number of interesting findings that help us build a meaningful model to predict weight loss automatically. The experimental results demonstrate the effectiveness of the proposed model and the significance of social factors in weight loss.

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Cited By

Dang TSpathis DGhosh AMascolo C(2023)Human-centred artificial intelligence for mobile health sensing: challenges and opportunitiesRoyal Society Open Science10.1098/rsos.23080610:11Online publication date: 15-Nov-2023
https://doi.org/10.1098/rsos.230806
Sandborn MOlea CSaid AShabbir MVolgyesi PKoutsoukos XWhite J(2022)What a drag! Streamlining the UAV design process with design grammars and drag surrogates2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00053(279-283)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00053
Che KQiu JYi LZou MLi ZCarr ASnipe RBenardot D(2021)Effects of a Short-Term “Fat Adaptation with Carbohydrate Restoration” Diet on Metabolic Responses and Exercise Performance in Well-Trained RunnersNutrients10.3390/nu1303103313:3(1033)Online publication date: 23-Mar-2021
https://doi.org/10.3390/nu13031033
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cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

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Published: 06 November 2017

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CIKM '17

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CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Dang TSpathis DGhosh AMascolo C(2023)Human-centred artificial intelligence for mobile health sensing: challenges and opportunitiesRoyal Society Open Science10.1098/rsos.23080610:11Online publication date: 15-Nov-2023
https://doi.org/10.1098/rsos.230806
Sandborn MOlea CSaid AShabbir MVolgyesi PKoutsoukos XWhite J(2022)What a drag! Streamlining the UAV design process with design grammars and drag surrogates2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00053(279-283)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00053
Che KQiu JYi LZou MLi ZCarr ASnipe RBenardot D(2021)Effects of a Short-Term “Fat Adaptation with Carbohydrate Restoration” Diet on Metabolic Responses and Exercise Performance in Well-Trained RunnersNutrients10.3390/nu1303103313:3(1033)Online publication date: 23-Mar-2021
https://doi.org/10.3390/nu13031033
Karimi HDerr TTorphy KFrank KTang J(2020)Towards Improving Sample Representativeness of Teachers on Online Social Media: A Case Study on PinterestArtificial Intelligence in Education10.1007/978-3-030-52240-7_24(130-134)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-3-030-52240-7_24
Hicks JAlthoff TSosic RKuhar PBostjancic BKing ALeskovec JDelp S(2019)Best practices for analyzing large-scale health data from wearables and smartphone appsnpj Digital Medicine10.1038/s41746-019-0121-12:1Online publication date: 3-Jun-2019
https://doi.org/10.1038/s41746-019-0121-1
Tu HWen JSun AWang X(2018)Joint Implicit and Explicit Neural Networks for Question Recommendation in CQA ServicesIEEE Access10.1109/ACCESS.2018.28811196(73081-73092)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2881119

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