Bioglio Livio, Pensa Ruggero G
University of Turin, C.So Svizzera, 185, I-10149 Turin, Italy.
EPJ Data Sci. 2022;11(1):12. doi: 10.1140/epjds/s13688-022-00324-y. Epub 2022 Mar 3.
User-generated contents often contain private information, even when they are shared publicly on social media and on the web in general. Although many filtering and natural language approaches for automatically detecting obscenities or hate speech have been proposed, determining whether a shared post contains sensitive information is still an open issue. The problem has been addressed by assuming, for instance, that sensitive contents are published anonymously, on anonymous social media platforms or with more restrictive privacy settings, but these assumptions are far from being realistic, since the authors of posts often underestimate or overlook their actual exposure to privacy risks. Hence, in this paper, we address the problem of content sensitivity analysis directly, by presenting and characterizing a new annotated corpus with around ten thousand posts, each one annotated as sensitive or non-sensitive by a pool of experts. We characterize our data with respect to the closely-related problem of self-disclosure, pointing out the main differences between the two tasks. We also present the results of several deep neural network models that outperform previous naive attempts of classifying social media posts according to their sensitivity, and show that state-of-the-art approaches based on anonymity and lexical analysis do not work in realistic application scenarios.
用户生成的内容通常包含私人信息，即使这些内容在社交媒体和网络上公开发布也是如此。尽管已经提出了许多用于自动检测淫秽或仇恨言论的过滤和自然语言方法，但确定共享帖子是否包含敏感信息仍然是一个悬而未决的问题。例如，有人通过假设敏感内容是在匿名社交媒体平台上匿名发布的，或者是在隐私设置更为严格的情况下发布的来解决这个问题，但这些假设远非现实，因为帖子的作者往往低估或忽视了他们实际面临的隐私风险。因此，在本文中，我们直接解决内容敏感性分析问题，通过展示和描述一个新的带注释语料库，该语料库包含约一万个帖子，每个帖子都由一组专家注释为敏感或不敏感。我们针对与自我披露密切相关的问题对我们的数据进行了特征描述，指出了这两项任务之间的主要区别。我们还展示了几个深度神经网络模型的结果，这些模型优于以往根据敏感性对社交媒体帖子进行分类的简单尝试，并表明基于匿名性和词汇分析的最先进方法在实际应用场景中不起作用。
