# XFake: Explainable Fake News Detector with Visualizations

## TL;DR

XFake is an explainable fake news detection system that combines attribute and statement analysis with visualizations to help users assess news credibility effectively.

## Contribution

The paper introduces XFake, a novel system integrating multiple frameworks for explainable fake news detection with visual support.

## Key findings

- Effective detection of fake news using combined attribute and statement analysis.
- Provides visual explanations and supporting examples for user interpretation.
- Demonstrated on real-world political news dataset from PolitiFact.

## Abstract

In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact, where thousands of verified political news have been collected.