# r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

arXiv: https://arxiv.org/abs/1911.03854 · HTML: https://mlsanity.com/paper/1911.03854/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

## Built on by (most cited first, 30 of 55)

- [End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://mlsanity.com/paper/2205.12487/) (2022)
- [Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models](https://mlsanity.com/paper/2106.00245/) (2021)
- [MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs](https://mlsanity.com/paper/2406.08772/) (2024)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [N24News: A New Dataset for Multimodal News Classification](https://mlsanity.com/paper/2108.13327/) (2021)
- [Benchmarks for Automated Commonsense Reasoning: A Survey](https://mlsanity.com/paper/2302.04752/) (2023)
- [VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias](https://mlsanity.com/paper/2304.14133/) (2023)
- [LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation](https://mlsanity.com/paper/2402.11943/) (2024)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?](https://mlsanity.com/paper/2410.04616/) (2024)
- [Rethinking Multimodal Content Moderation from an Asymmetric Angle with Mixed-modality](https://mlsanity.com/paper/2305.10547/) (2023)
- [Findings of Factify 2: Multimodal Fake News Detection](https://mlsanity.com/paper/2307.10475/) (2023)
- [Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data](https://mlsanity.com/paper/2412.16243/) (2024)
- [XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMs](https://mlsanity.com/paper/2508.09999/) (2025)
- [GPT-4V(ision) as A Social Media Analysis Engine](https://mlsanity.com/paper/2311.07547/) (2023)
- [MMCFND: Multimodal Multilingual Caption-aware Fake News Detection for Low-resource Indic Languages](https://mlsanity.com/paper/2410.10407/) (2024)
- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings](https://mlsanity.com/paper/2104.10715/) (2021)
- [MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection](https://mlsanity.com/paper/2210.05401/) (2022)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications](https://mlsanity.com/paper/2406.02601/) (2024)
- [Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection](https://mlsanity.com/paper/2502.01699/) (2025)
- [Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2509.25991/) (2025)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections](https://mlsanity.com/paper/2403.09858/) (2024)
- [A Comprehensive Dataset for Human vs. AI Generated Text Detection](https://mlsanity.com/paper/2510.22874/) (2025)
- [Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking](https://mlsanity.com/paper/2203.11486/) (2022)
- [FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections](https://mlsanity.com/paper/2312.03730/) (2023)
- [LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification](https://mlsanity.com/paper/2407.18614/) (2024)
- [Experimentation in Content Moderation using RWKV](https://mlsanity.com/paper/2409.03939/) (2024)

## Abstract (arXiv)

Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth of our dataset. We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.