# RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection

arXiv: https://arxiv.org/abs/2311.09939 · HTML: https://mlsanity.com/paper/2311.09939/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [Meta-Transformer: A Unified Framework for Multimodal Learning](https://mlsanity.com/paper/2307.10802/) (2023)
- [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)
- [Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources](https://mlsanity.com/paper/2112.00061/) (2021)
- [Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features](https://mlsanity.com/paper/2210.05916/) (2022)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation](https://mlsanity.com/paper/2210.13865/) (2022)
- [VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias](https://mlsanity.com/paper/2304.14133/) (2023)
- [Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency](https://mlsanity.com/paper/2003.10421/) (2020)
- [Deep Multimodal Image-Repurposing Detection](https://mlsanity.com/paper/1808.06686/) (2018)
- [Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text](https://mlsanity.com/paper/1707.01606/) (2017)
- [Synthetic Misinformers: Generating and Combating Multimodal Misinformation](https://mlsanity.com/paper/2303.01217/) (2023)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation](https://mlsanity.com/paper/2311.01766/) (2023)
- [Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation](https://mlsanity.com/paper/2112.08594/) (2021)
- [Findings of Factify 2: Multimodal Fake News Detection](https://mlsanity.com/paper/2307.10475/) (2023)
- [MMSys'21 Grand Challenge on Detecting Cheapfakes](https://mlsanity.com/paper/2107.05297/) (2021)
- [MemeFier: Dual-stage Modality Fusion for Image Meme Classification](https://mlsanity.com/paper/2304.02906/) (2023)

## Built on by (most cited first, 18 of 18)

- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?](https://mlsanity.com/paper/2407.13488/) (2024)
- [Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking](https://mlsanity.com/paper/2404.18971/) (2024)
- [E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2502.10455/) (2025)
- ["Image, Tell me your story!" Predicting the original meta-context of visual misinformation](https://mlsanity.com/paper/2408.09939/) (2024)
- [T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search](https://mlsanity.com/paper/2505.19768/) (2025)
- ["Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection](https://mlsanity.com/paper/2508.20670/) (2025)
- [Mitigating GenAI-Powered Evidence Pollution for Out-Of-Context Misinformation Detection](https://mlsanity.com/paper/2501.14728/) (2025)
- [Latent Reconstruction from Generated Data for Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.06010/) (2025)
- [Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Training](https://mlsanity.com/paper/2511.10213/) (2025)
- [Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection](https://mlsanity.com/paper/2602.01854/) (2026)
- [Lyapunov-Stable Adaptive Control for Multimodal Concept Drift](https://mlsanity.com/paper/2510.15944/) (2025)
- [NewsRECON: News Article Retrieval for Image Contextualization](https://mlsanity.com/paper/2601.14121/) (2026)
- [Evidence Triangulation for Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2606.31367/) (2026)
- [NepOOC-M: Bilingual Nepali-English Benchmark and Comparative Analysis of Multimodal Architectures for OOC Detection](https://mlsanity.com/paper/2608.19212/) (2026)
- [Self-Evolving Multimedia Verification through Memory Consolidation of Contestation Experiences](https://mlsanity.com/paper/2609.27175/) (2026)

## Abstract (arXiv)

Online misinformation is often multimodal in nature, i.e., it is caused by misleading associations between texts and accompanying images. To support the fact-checking process, researchers have been recently developing automatic multimodal methods that gather and analyze external information, evidence, related to the image-text pairs under examination. However, prior works assumed all external information collected from the web to be relevant. In this study, we introduce a "Relevant Evidence Detection" (RED) module to discern whether each piece of evidence is relevant, to support or refute the claim. Specifically, we develop the "Relevant Evidence Detection Directed Transformer" (RED-DOT) and explore multiple architectural variants (e.g., single or dual-stage) and mechanisms (e.g., "guided attention"). Extensive ablation and comparative experiments demonstrate that RED-DOT achieves significant improvements over the state-of-the-art (SotA) on the VERITE benchmark by up to 33.7%. Furthermore, our evidence re-ranking and element-wise modality fusion led to RED-DOT surpassing the SotA on NewsCLIPings+ by up to 3% without the need for numerous evidence or multiple backbone encoders. We release our code at: https://github.com/stevejpapad/relevant-evidence-detection