Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy risks. Limited research efforts, thus far, have been dedicated to the privacy detection from users’ unstructured data (i.e., UGCs). Moreover, existing efforts mainly focus on applying conventional machine learning techniques directly to traditional hand-crafted privacy-oriented features, ignoring the powerful representing capability of the advanced neural networks. In light of this, in this article, we present a fine-grained privacy detection network (GrHA) equipped with graph-regularized hierarchical attentive representation learning. In particular, the proposed GrHA explores the semantic correlations among personal aspects with graph convolutional networks to enhance the regularization for the UGC representation learning, and, hence, fulfil effective fine-grained privacy detection. Extensive experiments on a real-world dataset demonstrate the superiority of the proposed model over state-of-the-art competitors in terms of eight standard metrics. As a byproduct, we have released the codes and involved parameters to facilitate the research community.

中文翻译：

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具有图正则化分层注意表示学习的细粒度隐私检测

由于社交媒体环境的复杂性和动态性，用户生成内容（UGC）可能会在不经意间泄露用户的个人信息，例如个人属性、人际关系甚至健康状况，从而使用户面临很高的隐私风险。迄今为止，有限的研究工作一直致力于从用户的非结构化数据（即 UGC）中检测隐私。此外，现有的努力主要集中在将传统的机器学习技术直接应用于传统的手工制作的面向隐私的特征，而忽略了先进神经网络的强大表示能力。有鉴于此，在本文中，我们提出了一个配备图正则化分层注意表示学习的细粒度隐私检测网络（GrHA）。特别是，所提出的 GrHA 通过图卷积网络探索个人方面之间的语义相关性，以增强 UGC 表示学习的正则化，从而实现有效的细粒度隐私检测。在真实世界数据集上进行的大量实验证明了所提出的模型在八个标准指标方面优于最先进的竞争对手。作为副产品，我们发布了代码和相关参数，以方便研究社区。在真实世界数据集上进行的大量实验证明了所提出的模型在八个标准指标方面优于最先进的竞争对手。作为副产品，我们发布了代码和相关参数，以方便研究社区。在真实世界数据集上进行的大量实验证明了所提出的模型在八个标准指标方面优于最先进的竞争对手。作为副产品，我们发布了代码和相关参数，以方便研究社区。