# XFake: Explainable Fake News Detector with Visualizations

arXiv: https://arxiv.org/abs/1907.07757 · HTML: https://mlsanity.com/paper/1907.07757/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 30 of 32)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [On Explainability of Graph Neural Networks via Subgraph Explorations](https://mlsanity.com/paper/2102.05152/) (2021)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Generating Literal and Implied Subquestions to Fact-check Complex Claims](https://mlsanity.com/paper/2205.06938/) (2022)
- [Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom](https://mlsanity.com/paper/2405.03371/) (2024)
- [JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims](https://mlsanity.com/paper/2401.08026/) (2024)
- [Benchmarking the Generation of Fact Checking Explanations](https://mlsanity.com/paper/2308.15202/) (2023)
- [FlowX: Towards Explainable Graph Neural Networks via Message Flows](https://mlsanity.com/paper/2206.12987/) (2022)
- [FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models](https://mlsanity.com/paper/2310.05046/) (2023)
- [Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning](https://mlsanity.com/paper/2212.01060/) (2022)
- [Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches](https://mlsanity.com/paper/2407.12853/) (2024)
- [A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection](https://mlsanity.com/paper/2209.14642/) (2022)
- [Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs](https://mlsanity.com/paper/2207.08098/) (2022)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks](https://mlsanity.com/paper/2403.12730/) (2024)
- [Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview](https://mlsanity.com/paper/2503.08420/) (2025)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Attention with Dependency Parsing Augmentation for Fine-Grained Attribution](https://mlsanity.com/paper/2412.11404/) (2024)
- [Face the Facts! Evaluating RAG-based Pipelines for Professional Fact-Checking](https://mlsanity.com/paper/2412.15189/) (2024)
- [Challenges and Considerations with Code-Mixed NLP for Multilingual Societies](https://mlsanity.com/paper/2106.07823/) (2021)
- [ExClaim: Explainable Neural Claim Verification Using Rationalization](https://mlsanity.com/paper/2301.08914/) (2023)
- [Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification](https://mlsanity.com/paper/2305.09400/) (2023)
- [Robust Claim Verification Through Fact Detection](https://mlsanity.com/paper/2407.18367/) (2024)
- [Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages](https://mlsanity.com/paper/2104.11761/) (2021)
- [COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News](https://mlsanity.com/paper/2310.13890/) (2023)
- [Evidence-based Interpretable Open-domain Fact-checking with Large Language Models](https://mlsanity.com/paper/2312.05834/) (2023)
- [Misinformation Detection using Large Language Models with Explainability](https://mlsanity.com/paper/2510.18918/) (2025)
- [Detecting Manipulated Contents Using Knowledge-Grounded Inference](https://mlsanity.com/paper/2504.21165/) (2025)
- [Towards Smart Fake News Detection Through Explainable AI](https://mlsanity.com/paper/2207.11490/) (2022)

## Abstract (arXiv)

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.