# FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection

arXiv: https://arxiv.org/abs/2404.01336 · HTML: https://mlsanity.com/paper/2404.01336/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 73 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift](https://mlsanity.com/paper/2003.04475/) (2020)
- [A Unified View of Label Shift Estimation](https://mlsanity.com/paper/2003.07554/) (2020)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [RLSbench: Domain Adaptation Under Relaxed Label Shift](https://mlsanity.com/paper/2302.03020/) (2023)
- [Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization](https://mlsanity.com/paper/2112.12072/) (2021)
- [KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis](https://mlsanity.com/paper/2005.00791/) (2020)
- [Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19](https://mlsanity.com/paper/2208.09578/) (2022)
- [Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection](https://mlsanity.com/paper/2306.02137/) (2023)
- [On Target Shift in Adversarial Domain Adaptation](https://mlsanity.com/paper/1903.06336/) (2019)
- [MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection](https://mlsanity.com/paper/2403.09092/) (2024)
- [Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer](https://mlsanity.com/paper/2308.09724/) (2023)
- [CISum: Learning Cross-modality Interaction to Enhance Multimodal Semantic Coverage for Multimodal Summarization](https://mlsanity.com/paper/2302.09934/) (2023)
- [Evons: A Dataset for Fake and Real News Virality Analysis and Prediction](https://mlsanity.com/paper/2209.08129/) (2022)

## Built on by (most cited first, 6 of 6)

- [The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs](https://mlsanity.com/paper/2506.11094/) (2025)
- [Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2509.25991/) (2025)
- [Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time Scaling](https://mlsanity.com/paper/2603.02519/) (2026)
- [Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios](https://mlsanity.com/paper/2603.21054/) (2026)
- [Retrieval-Augmented Multimodal Model for Fake News Detection](https://mlsanity.com/paper/2604.18112/) (2026)
- [Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection](https://mlsanity.com/paper/2609.18597/) (2026)

## Abstract (arXiv)

Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content. However, these benchmarks typically focus solely on news pertaining to a single semantic topic or originating from a single platform, thereby failing to capture the diversity of multi-domain news in real scenarios. In order to understand fake news across various domains, the external knowledge and fine-grained annotations are indispensable to provide precise evidence and uncover the diverse underlying strategies for fabrication, which are also ignored by existing benchmarks. To address this gap, we introduce a novel multi-domain knowledge-enhanced benchmark with fine-grained annotations, named FineFake. FineFake encompasses 16,909 data samples spanning six semantic topics and eight platforms. Each news item is enriched with multi-modal content, potential social context, semi-manually verified common knowledge, and fine-grained annotations that surpass conventional binary labels. Furthermore, we formulate three challenging tasks based on FineFake and propose a knowledge-enhanced domain adaptation network. Extensive experiments are conducted on FineFake under various scenarios, providing accurate and reliable benchmarks for future endeavors. The entire FineFake project is publicly accessible as an open-source repository at urlhttps://github.com/Accuser907/FineFake.