论文标题
在Twitter上自动检测Doxing
Automated Detection of Doxing on Twitter
论文作者
论文摘要
Doxing是指未经同意就披露有关某人的敏感个人信息的做法。这种网络欺凌形式对于在线社交网络来说是一种令人不快的，有时甚至是危险的现象。尽管对其他类型的网络欺凌行为的自动识别存在先前的工作，但仍存在对能够在Twitter上检测Doxing的方法。我们提出并评估了一组方法，用于自动检测敏感私人信息的Twitter上的第二和第三方披露，其中该信息构成了Doxing。我们总结了Doxing情节背后的共同意图的发现，并根据弦乐匹配和单热编码的启发式方法以及词和上下文化的字符串嵌入Tweet的表示，比较了九种不同的方法进行自动检测。我们确定了一种使用上下文化的字符串嵌入的方法，可提供96.86％的准确性和97.37％的召回，并通过讨论我们提出的方法的实用性来得出结论。
Doxing refers to the practice of disclosing sensitive personal information about a person without their consent. This form of cyberbullying is an unpleasant and sometimes dangerous phenomenon for online social networks. Although prior work exists on automated identification of other types of cyberbullying, a need exists for methods capable of detecting doxing on Twitter specifically. We propose and evaluate a set of approaches for automatically detecting second- and third-party disclosures on Twitter of sensitive private information, a subset of which constitutes doxing. We summarize our findings of common intentions behind doxing episodes and compare nine different approaches for automated detection based on string-matching and one-hot encoded heuristics, as well as word and contextualized string embedding representations of tweets. We identify an approach providing 96.86% accuracy and 97.37% recall using contextualized string embeddings and conclude by discussing the practicality of our proposed methods.
