# A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks

arXiv: https://arxiv.org/abs/2112.00803 · HTML: https://mlsanity.com/paper/2112.00803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 149 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 30 of 33)

- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [Detecting Photoshopped Faces by Scripting Photoshop](https://mlsanity.com/paper/1906.05856/) (2019)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [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)
- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection](https://mlsanity.com/paper/1911.05351/) (2019)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency](https://mlsanity.com/paper/2003.10421/) (2020)
- [Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation](https://mlsanity.com/paper/1903.09196/) (2019)
- [Deep Multimodal Image-Repurposing Detection](https://mlsanity.com/paper/1808.06686/) (2018)
- [Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning](https://mlsanity.com/paper/1511.03476/) (2015)
- [Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text](https://mlsanity.com/paper/1707.01606/) (2017)
- [Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection](https://mlsanity.com/paper/1705.09765/) (2017)
- [TI-CNN: Convolutional Neural Networks for Fake News Detection](https://mlsanity.com/paper/1806.00749/) (2018)
- [Deception Detection in Videos](https://mlsanity.com/paper/1712.04415/) (2017)
- [Fake News Identification on Twitter with Hybrid CNN and RNN Models](https://mlsanity.com/paper/1806.11316/) (2018)
- [A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections](https://mlsanity.com/paper/2005.02443/) (2020)
- [AIRD: Adversarial Learning Framework for Image Repurposing Detection](https://mlsanity.com/paper/1903.00788/) (2019)

## Abstract (arXiv)

Fake news and misinformation are a matter of concern for people around the globe. Users of the internet and social media sites encounter content with false information much frequently. Fake news detection is one of the most analyzed and prominent areas of research. These detection techniques apply popular machine learning and deep learning algorithms. Previous work in this domain covers fake news detection vastly among text circulating online. Platforms that have extensively been observed and analyzed include news websites and Twitter. Facebook, Reddit, WhatsApp, YouTube, and other social applications are gradually gaining attention in this emerging field. Researchers are analyzing online data based on multiple modalities composed of text, image, video, speech, and other contributing factors. The combination of various modalities has resulted in efficient fake news detection. At present, there is an abundance of surveys consolidating textual fake news detection algorithms. This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text. We provide a comprehensive literature survey of eighty articles presenting state-of-the-art detection techniques, thereby identifying research gaps and building a pathway for researchers to further advance this domain.