# Automated Detection of Doxing on Twitter

arXiv: https://arxiv.org/abs/2202.00879 · HTML: https://mlsanity.com/paper/2202.00879/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 67 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms](https://mlsanity.com/paper/1801.06482/) (2018)
- [Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study](https://mlsanity.com/paper/1812.08046/) (2018)
- [Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media](https://mlsanity.com/paper/2105.04913/) (2021)

## Built on by (most cited first, 3 of 3)

- [Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation](https://mlsanity.com/paper/2205.08675/) (2022)
- [A survey of textual cyber abuse detection using cutting-edge language models and large language models](https://mlsanity.com/paper/2501.05443/) (2025)
- [FNDEX: Fake News and Doxxing Detection with Explainable AI](https://mlsanity.com/paper/2410.22390/) (2024)

## Abstract (arXiv)

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.