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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kil, David Xiao, Xiao Piniewski, Brigitte Phan, NhatHai Dou, Dejing |
| Abstract | Modeling physical activity propagation, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. However, there has been lacking of scientific and quantitative study to elucidate how social communication may deliver physical activity interventions. In this work we introduce a Community-level Physical Activity Propagation (CPP) model to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level). CPP is a novel model which is inspired by the well-known Independent Cascade and Community-level Social Influence models. Given a social network, we utilize a hierarchical approach to detect a set of communities and their reciprocal influence strength of physical activities. CPP provides a powerful tool to discover, summarize, and investigate influence patterns of physical activities in a health social network. The detail experimental evaluation shows not only the effectiveness of our approach but also the correlation of the detected communities with various health outcome measures (i.e., both existing ones and our novel measure, named Wellness score, which is a combination of lifestyle parameters, biometrics, and biomarkers). Our promising results potentially pave a way for knowledge discovery in health social networks. |
| Starting Page | 1329 |
| Ending Page | 1338 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450325981 |
| DOI | 10.1145/2661829.2662025 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Health social network Physical activity propagation |
| Content Type | Text |
| Resource Type | Article |
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