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.

本文就如何在 Twitter 上自动检测敏感私人信息的二级和三级泄露，即 doxing 进行了探讨和分析，经对 9 种不同方法进行实验和比对，利用上下文编码字符串嵌入表征的方法取得了 96.86% 的准确性和 97.37% 的召回。