# FNDEX: Fake News and Doxxing Detection with Explainable AI

arXiv: https://arxiv.org/abs/2410.22390 · HTML: https://mlsanity.com/paper/2410.22390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [Network-based Fake News Detection: A Pattern-driven Approach](https://mlsanity.com/paper/1906.04210/) (2019)
- [Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements](https://mlsanity.com/paper/2001.00623/) (2020)
- [Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking](https://mlsanity.com/paper/2011.13253/) (2020)
- [Transformer based Automatic COVID-19 Fake News Detection System](https://mlsanity.com/paper/2101.00180/) (2021)
- [From Deception to Detection: The Dual Roles of Large Language Models in Fake News](https://mlsanity.com/paper/2409.17416/) (2024)
- [Transforming Fake News: Robust Generalisable News Classification Using Transformers](https://mlsanity.com/paper/2109.09796/) (2021)
- [Automated Detection of Doxing on Twitter](https://mlsanity.com/paper/2202.00879/) (2022)
- [Fake news as we feel it: perception and conceptualization of the term "fake news" in the media](https://mlsanity.com/paper/1807.06926/) (2018)
- [FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media](https://mlsanity.com/paper/2112.01131/) (2021)

## Abstract (arXiv)

The widespread and diverse online media platforms and other internet-driven communication technologies have presented significant challenges in defining the boundaries of freedom of expression. Consequently, the internet has been transformed into a potential cyber weapon. Within this evolving landscape, two particularly hazardous phenomena have emerged: fake news and doxxing. Although these threats have been subjects of extensive scholarly analysis, the crossroads where they intersect remain unexplored. This research addresses this convergence by introducing a novel system. The Fake News and Doxxing Detection with Explainable Artificial Intelligence (FNDEX) system leverages the capabilities of three distinct transformer models to achieve high-performance detection for both fake news and doxxing. To enhance data security, a rigorous three-step anonymization process is employed, rooted in a pattern-based approach for anonymizing personally identifiable information. Finally, this research emphasizes the importance of generating coherent explanations for the outcomes produced by both detection models. Our experiments on realistic datasets demonstrate that our system significantly outperforms the existing baselines