当图卷积遇上双重注意力：基于多标签文本分类的在线隐私泄露检测
When Graph Convolution Meets Double Attention: Online Privacy Disclosure Detection with Multi-Label Text Classification
AI总结：
本文提出一种结合双重注意力机制与图卷积网络的多标签文本分类模型，用于检测在线社交平台上的隐私泄露，并在Twitter数据集上显著优于现有方法。
摘要：
随着在线社交媒体等Web 2.0平台的兴起，人们的位置、职业甚至家庭信息等隐私常常通过在线讨论被无意泄露。因此，检测此类非预期的隐私泄露以帮助提醒受影响人群和在线平台具有重要意义。本文将隐私泄露检测建模为多标签文本分类（MLTC）问题，并提出一种新的隐私泄露检测模型，用于构建检测在线隐私泄露的MLTC分类器。该分类器以在线帖子为输入，输出多个标签，每个标签反映一种可能的隐私泄露。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、标签与文本之间的相关性以及标签与标签之间的相关性。采用双重注意力机制来融合前两种信息源，并利用图卷积网络（GCN）提取第三种信息源，该信息源随后用于帮助融合从前两种信息源提取的特征。我们在一个包含Twitter上隐私泄露帖子的公开数据集上进行了大量实验，结果表明，我们提出的隐私泄露检测方法在所有关键性能指标上显著且一致地优于其他最先进的方法。
英文摘要：
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 (GCN) 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
