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| Content Provider | ACM Digital Library |
|---|---|
| Author | Epstein, Daniel A. McDonald, David W. Bales, Elizabeth Munson, Sean A. Jacobson, Bradley H. |
| Abstract | Many research applications and popular commercial applications include features for sharing personally collected data with others in social awareness streams. Prior work has identified several barriers to use as well as discrepancies between designer goals and how these features are used in practice. We develop a framework for designing and evaluating these features based on an extensive review of prior literature. We demonstrate the value of this framework by analyzing physical activity sharing on Twitter, coding 4,771 tweets and their responses and gathering 444 reactions from 97 potential tweet recipients, learning that specific user-generated content leads to more responses and is better received by the post audience. We conclude by extending our findings to other sharing problems and discussing the value of our design framework. |
| Starting Page | 1622 |
| Ending Page | 1636 |
| Page Count | 15 |
| File Format | |
| ISBN | 9781450329224 |
| DOI | 10.1145/2675133.2675135 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-02-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Social network sites Health Social sharing Self-tracking Social awareness streams Personal informatics |
| Content Type | Text |
| Resource Type | Article |
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