# Findings of Factify 2: Multimodal Fake News Detection

arXiv: https://arxiv.org/abs/2307.10475 · HTML: https://mlsanity.com/paper/2307.10475/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [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)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.05509/) (2021)
- [Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification](https://mlsanity.com/paper/2302.07740/) (2023)
- [Memotion Analysis through the Lens of Joint Embedding](https://mlsanity.com/paper/2111.07074/) (2021)
- [IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images](https://mlsanity.com/paper/2305.10438/) (2023)
- [INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification](https://mlsanity.com/paper/2303.01510/) (2023)
- [Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture](https://mlsanity.com/paper/2301.03127/) (2023)

## Built on by (most cited first, 7 of 7)

- [RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection](https://mlsanity.com/paper/2311.09939/) (2023)
- [The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval](https://mlsanity.com/paper/2503.14828/) (2025)
- [A Comprehensive Dataset for Human vs. AI Generated Text Detection](https://mlsanity.com/paper/2510.22874/) (2025)
- [The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking](https://mlsanity.com/paper/2602.09516/) (2026)
- [Overview of Factify5WQA: Fact Verification through 5W Question-Answering](https://mlsanity.com/paper/2410.04236/) (2024)
- [MalFake: A Multimodal Fake News Identification for Malayalam using Recurrent Neural Networks and VGG-16](https://mlsanity.com/paper/2310.18263/) (2023)
- [Evidence Triangulation for Multimodal Fact-Checking in the Wild](https://mlsanity.com/paper/2606.31367/) (2026)

## Abstract (arXiv)

With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-modal fact verification and satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data calls for a comparison based approach to the task by pairing social media claims with supporting documents, with both text and image, divided into 5 classes based on multi-modal relations. In the second iteration of this task we had over 60 participants and 9 final test-set submissions. The best performances came from the use of DeBERTa for text and Swinv2 and CLIP for image. The highest F1 score averaged for all five classes was 81.82%.