# Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation

arXiv: https://arxiv.org/abs/2311.01766 · HTML: https://mlsanity.com/paper/2311.01766/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (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)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources](https://mlsanity.com/paper/2112.00061/) (2021)
- [Reasoning Over Semantic-Level Graph for Fact Checking](https://mlsanity.com/paper/1909.03745/) (2019)
- [Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News](https://mlsanity.com/paper/2009.07698/) (2020)
- [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)
- [Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection](https://mlsanity.com/paper/2306.02137/) (2023)
- [Understanding the Use of Fauxtography on Social Media](https://mlsanity.com/paper/2009.11792/) (2020)
- [AIRD: Adversarial Learning Framework for Image Repurposing Detection](https://mlsanity.com/paper/1903.00788/) (2019)
- [QSAN: A Quantum-probability based Signed Attention Network for Explainable False Information Detection](https://mlsanity.com/paper/2009.03823/) (2020)

## Built on by (most cited first, 21 of 21)

- [RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection](https://mlsanity.com/paper/2311.09939/) (2023)
- [COVE: COntext and VEracity prediction for out-of-context images](https://mlsanity.com/paper/2502.01194/) (2025)
- [Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?](https://mlsanity.com/paper/2407.13488/) (2024)
- [E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2502.10455/) (2025)
- [XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMs](https://mlsanity.com/paper/2508.09999/) (2025)
- [CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2505.23449/) (2025)
- [On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs](https://mlsanity.com/paper/2410.12600/) (2024)
- [Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models](https://mlsanity.com/paper/2505.15489/) (2025)
- [RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking](https://mlsanity.com/paper/2507.09174/) (2025)
- ["Image, Tell me your story!" Predicting the original meta-context of visual misinformation](https://mlsanity.com/paper/2408.09939/) (2024)
- [Learning Domain-Invariant Features for Out-of-Context News Detection](https://mlsanity.com/paper/2406.07430/) (2024)
- [EXCLAIM: An Explainable Cross-Modal Agentic System for Misinformation Detection with Hierarchical Retrieval](https://mlsanity.com/paper/2504.06269/) (2025)
- [Multi-MLLM Knowledge Distillation for Out-of-Context News Detection](https://mlsanity.com/paper/2505.22517/) (2025)
- ["Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection](https://mlsanity.com/paper/2508.20670/) (2025)
- [Multimodal Coherent Explanation Generation of Robot Failures](https://mlsanity.com/paper/2410.00659/) (2024)
- [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)
- [CoVLM: Leveraging Consensus from Vision-Language Models for Semi-supervised Multi-modal Fake News Detection](https://mlsanity.com/paper/2410.04426/) (2024)
- [XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception](https://mlsanity.com/paper/2603.22453/) (2026)
- [Evidence Triangulation for Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2606.31367/) (2026)

## Abstract (arXiv)

Mis- and disinformation online have become a major societal problem as major sources of online harms of different kinds. One common form of mis- and disinformation is out-of-context (OOC) information, where different pieces of information are falsely associated, e.g., a real image combined with a false textual caption or a misleading textual description. Although some past studies have attempted to defend against OOC mis- and disinformation through external evidence, they tend to disregard the role of different pieces of evidence with different stances. Motivated by the intuition that the stance of evidence represents a bias towards different detection results, we propose a stance extraction network (SEN) that can extract the stances of different pieces of multi-modal evidence in a unified framework. Moreover, we introduce a support-refutation score calculated based on the co-occurrence relations of named entities into the textual SEN. Extensive experiments on a public large-scale dataset demonstrated that our proposed method outperformed the state-of-the-art baselines, with the best model achieving a performance gain of 3.2% in accuracy. The source code and checkpoints are publicly available at https://github.com/yx3266/SEN.