Understanding and Predicting Weight Loss with Mobile Social Networking Data
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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Ten million users of a weight monitoring app were used to show that people are more likely to lose weight when they had more friends of the opposite sex.
“Ten million users of a weight monitoring app were used to show that people are more likely to lose weight when they had more friends of the opposite sex [128].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [128]
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Best practices for analyzing large-scale health data from wearables and smartphone apps
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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
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- Authors
- Wang, Z., Derr, T., Yin, D., & Tang, J.
- Affiliations
- Michigan State University (US)
- Jingdong (China) (CN)
- Venue
- Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2017-11-06
- Pages
- 1269-1278
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2767690471
- Cited by
- 9
- References
- 45
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 3
- FWCI
- 9.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Funders
- National Science Foundation (IIS-1715940, IIS-1714741)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- weight loss prediction, mobile social networking, social factors, personal factors, weight management, social media data, user behavior
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