Hogan Bernie, Melville Joshua R, Philips Gregory Lee, Janulis Patrick, Contractor Noshir, Mustanski Brian S, Birkett Michelle
Oxford Internet Institute, University of Oxford.
Feinberg School of Medicine, Northwestern University.
Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:5360-5371. doi: 10.1145/2858036.2858368.
While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within-subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks.
虽然网上存在大量社交网络数据，但高危人群的关键网络指标仍必须通过自我报告来获取。这种做法在工作流程和应答负担方面存在诸多限制。然而，技术、网络绘图库和数据库的进步使得交互式网络绘图越来越可行。我们描述了一种将基于模拟的个人网络捕获技术转化为数字框架的方法，该框架解决了许多现有缺点，如：1）复杂的数据录入；2）广泛的访谈者干预和实地设置；3）数据重用困难；4）缺乏动态可视化。我们在一项针对高危且难以接触人群的健康行为研究中测试了这种实现方式。我们提供了纸质版和触摸屏版之间的受试者内比较。我们断言，基于触摸屏的社交网络捕获现在是高度敏感数据和社交网络数据录入任务的可行替代方案。
