论文标题
从在线遗憾中学习：从已删除的帖子到社交网站的风险意识
Learning from Online Regrets: From Deleted Posts to Risk Awareness in Social Network Sites
论文作者
论文摘要
社交网络网站（SNSS）（例如Facebook或Instagram）是人们将生活暴露在广泛而多样的受众中的空间。这种做法可能导致不必要的事件，例如当私人信息接收到意外接收者时，声誉损失，失业或骚扰。结果，用户经常后悔在这些平台中发布了私人信息，并在获得负面体验后继续删除此类内容。风险意识是一种可以用来说服用户采取更安全隐私决策的策略。但是，许多针对SNS的风险意识技术都认为有关风险的信息是由该领域专家检索和衡量的。因此，风险估计是一项活动，尽管其重要性很重要。在这项工作中，我们介绍了一种采用删除帖子作为风险信息工具的方法，以测量SNS中自我披露模式的频率和后果水平。在这种方法中，用户通过顺序量表报告了后果，并以后用于计算风险临界指数。我们随后展示了该索引如何在SNS的自适应隐私设计中使用。
Social Network Sites (SNSs) like Facebook or Instagram are spaces where people expose their lives to wide and diverse audiences. This practice can lead to unwanted incidents such as reputation damage, job loss or harassment when pieces of private information reach unintended recipients. As a consequence, users often regret to have posted private information in these platforms and proceed to delete such content after having a negative experience. Risk awareness is a strategy that can be used to persuade users towards safer privacy decisions. However, many risk awareness technologies for SNSs assume that information about risks is retrieved and measured by an expert in the field. Consequently, risk estimation is an activity that is often passed over despite its importance. In this work we introduce an approach that employs deleted posts as risk information vehicles to measure the frequency and consequence level of self-disclosure patterns in SNSs. In this method, consequence is reported by the users through an ordinal scale and used later on to compute a risk criticality index. We thereupon show how this index can serve in the design of adaptive privacy nudges for SNSs.
