FNDEX: Fake News and Doxxing Detection with Explainable AI

TL;DR

FNDEX is a novel AI system that uses transformer models and anonymization techniques to detect fake news and doxxing, providing explainable results and outperforming existing methods.

Contribution

This work introduces a combined fake news and doxxing detection system with explainability and a new anonymization process, addressing a previously unexplored intersection.

Findings

System significantly outperforms existing baselines

Uses three transformer models for high accuracy

Provides coherent explanations for detections

Abstract

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