# Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

arXiv: https://arxiv.org/abs/2412.14686 · HTML: https://mlsanity.com/paper/2412.14686/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [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)
- [NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media](https://mlsanity.com/paper/2104.05893/) (2021)
- [End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://mlsanity.com/paper/2205.12487/) (2022)
- [Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2304.02556/) (2023)
- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [Exploiting Multi-domain Visual Information for Fake News Detection](https://mlsanity.com/paper/1908.04472/) (2019)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [Deep Multimodal Image-Repurposing Detection](https://mlsanity.com/paper/1808.06686/) (2018)
- [AIRD: Adversarial Learning Framework for Image Repurposing Detection](https://mlsanity.com/paper/1903.00788/) (2019)
- [Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment](https://mlsanity.com/paper/1808.09633/) (2018)

## Built on by (most cited first, 11 of 11)

- [SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis](https://mlsanity.com/paper/2506.07603/) (2025)
- [T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search](https://mlsanity.com/paper/2505.19768/) (2025)
- [Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2509.25991/) (2025)
- [UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation](https://mlsanity.com/paper/2502.11132/) (2025)
- [ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction](https://mlsanity.com/paper/2511.01188/) (2025)
- [Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks](https://mlsanity.com/paper/2511.05883/) (2025)
- [Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection](https://mlsanity.com/paper/2512.21039/) (2025)
- [Head-wise Modality Specialization within MLLMs for Robust Fake News Detection under Missing Modality](https://mlsanity.com/paper/2604.09711/) (2026)
- [Insight-A: Attribution-aware for Multimodal Misinformation Detection](https://mlsanity.com/paper/2511.21705/) (2025)
- [ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection](https://mlsanity.com/paper/2606.24112/) (2026)
- [Rethinking Multimodal Fake News Detection in the Generative AI Era](https://mlsanity.com/paper/2609.36850/) (2026)

## Abstract (arXiv)

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset amg, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model our to achieve multimodal fake news detection and attribution. Experimental results demonstrate that amg is a challenging dataset, and its attribution setting opens up new avenues for future research.