With the rise of Web 2.0 platforms such as online social media, people’s private information, such as their location, occupation and even family information, is often inadvertently disclosed through online discussions. Therefore, it is important to detect such unwanted privacy disclosures to help alert people affected and the online platform. In this paper, privacy disclosure detection is modeled as a multi-label text classification (MLTC) problem, and a new privacy disclosure detection model is proposed to construct an MLTC classifier for detecting online privacy disclosures. This classifier takes an online post as the input and outputs multiple labels, each reflecting a possible privacy disclosure. The proposed presentation method combines three different sources of information, the input text itself, the label-to-text correlation and the label-to-label correlation. A double-attention mechanism is used to combine the first two sources of information, and a graph convolutional network is employed to extract the third source of information that is then used to help fuse features extracted from the first two sources of information. Our extensive experimental results, obtained on a public dataset of privacy-disclosing posts on Twitter, demonstrated that our proposed privacy disclosure detection method significantly and consistently outperformed other state-of-the-art methods in terms of all key performance indicators.

随着网络社交媒体等Web 2.0平台的兴起，人们的隐私信息，例如位置、职业甚至家庭信息，常常通过在线讨论而被无意间泄露。因此，检测此类不必要的隐私泄露以帮助提醒受影响的人和在线平台非常重要。本文将隐私泄露检测建模为多标签文本分类（MLTC）问题，并提出了一种新的隐私泄露检测模型来构建用于检测在线隐私泄露的MLTC分类器。该分类器以在线帖子作为输入并输出多个标签，每个标签都反映了可能的隐私泄露。所提出的表示方法结合了三种不同的信息源：输入文本本身、标签到文本相关性和标签到标签相关性。使用双注意力机制来组合前两个信息源，并使用图卷积网络来提取第三个信息源，然后用于帮助融合从前两个信息源中提取的特征。我们在 Twitter 上隐私披露帖子的公共数据集上获得的广泛实验结果表明，我们提出的隐私披露检测方法在所有关键性能指标方面均显着且始终优于其他最先进的方法。