Online self-disclosure is perhaps one of the last decade's most studied communication processes, thanks to the introduction of Online Social Networks (OSNs) like Facebook. Self-disclosure research has contributed significantly to the design of preventative nudges seeking to support and guide users when revealing private information in OSNs. Still, assessing the effectiveness of these solutions is often challenging since changing or modifying the choice architecture of OSN platforms is practically unfeasible. In turn, the effectiveness of numerous nudging designs is supported primarily by self-reported data instead of actual behavioral information. This work presents ENAGRAM, an app for evaluating preventative nudges, and reports the first results of an empirical study conducted with it. Such a study aims to showcase how the app (and the data collected with it) can be leveraged to assess the effectiveness of a particular nudging approach. We used ENAGRAM as a vehicle to test a risk-based strategy for nudging the self-disclosure decisions of Instagram users. For this, we created two variations of the same nudge and tested it in a between-subjects experimental setting. Study participants (N=22) were recruited via Prolific and asked to use the app regularly for 7 days. An online survey was distributed at the end of the experiment to measure some privacy-related constructs. From the data collected with ENAGRAM, we observed lower (though non-significant) self-disclosure levels when applying risk-based interventions. The constructs measured with the survey were not significant either, except for participants' External Information Privacy Concerns. Our results suggest that (i) ENAGRAM is a suitable alternative for conducting longitudinal experiments in a privacy-friendly way, and (ii) it provides a flexible framework for the evaluation of a broad spectrum of nudging solutions.
翻译:在线自我披露或许是过去十年来最受研究的通信流程之一,这归功于在线社交网络(OSNs)的推出,比如Facebook。自我披露研究极大地促进了设计预防性手段,以寻求在OSNs披露私人信息时支持和指导用户。然而,评估这些解决方案的有效性往往具有挑战性,因为改变或修改OSN平台的选择架构几乎是行不通的。反过来,许多自定义设计的效力主要靠自我报告数据而不是实际行为信息来支持。这项工作展示了用于评价预防性动作的ENAGRAM应用程序,并报告了与之进行的经验性研究的第一个结果。这种研究旨在展示如何利用该应用程序(及其收集的数据)来评估某个特定标识方法的有效性。我们利用ENAGRAM测试基于风险的战略来验证Instagram用户的自我披露决定。为此,我们创建了两种不同版本的直观数据变量变换了两种版本,一种用于在实验的选项设置中评估预防性动作,一种是用内部数据库定期进行自我评估,一种是用内部数据库访问,一种是用内部数据采集的系统,一种是用内部数据,一种是用内部数据采集的系统,一种测量,一种不是通过内部数据,一种测量,一种是用来进行内部测试,一种是用来进行内部数据,一种测量,一种测量,一种测量,一种是用来进行内部调查的。在7天里程内测测测测。