# A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities

arXiv: https://arxiv.org/abs/1812.00315 · HTML: https://mlsanity.com/paper/1812.00315/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

## Built on by (most cited first, 28 of 28)

- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://mlsanity.com/paper/2101.11206/) (2021)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [Misinformation Detection on YouTube Using Video Captions](https://mlsanity.com/paper/2107.00941/) (2021)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?](https://mlsanity.com/paper/2107.07970/) (2021)
- [A Multi-Label Dataset of French Fake News: Human and Machine Insights](https://mlsanity.com/paper/2403.16099/) (2024)
- [TI-Capsule: Capsule Network for Stock Exchange Prediction](https://mlsanity.com/paper/2102.07718/) (2021)
- [Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021](https://mlsanity.com/paper/2207.05133/) (2022)
- [NoFake at CheckThat! 2021: Fake News Detection Using BERT](https://mlsanity.com/paper/2108.05419/) (2021)
- [Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals](https://mlsanity.com/paper/2305.11349/) (2023)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features](https://mlsanity.com/paper/2109.04835/) (2021)
- [Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebook](https://mlsanity.com/paper/2203.11669/) (2022)
- [Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach](https://mlsanity.com/paper/2207.13500/) (2022)
- [Detecting Fake Job Postings Using Bidirectional LSTM](https://mlsanity.com/paper/2304.02019/) (2023)
- [Written and spoken corpus of real and fake social media postings about COVID-19](https://mlsanity.com/paper/2310.04237/) (2023)
- [ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction](https://mlsanity.com/paper/2511.01188/) (2025)
- [ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs](https://mlsanity.com/paper/2109.12777/) (2021)
- [A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model](https://mlsanity.com/paper/2202.01907/) (2022)
- [Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprints](https://mlsanity.com/paper/2206.02067/) (2022)
- [Multilingual Disinformation Detection for Digital Advertising](https://mlsanity.com/paper/2207.10649/) (2022)
- [Deep Fake Detection, Deterrence and Response: Challenges and Opportunities](https://mlsanity.com/paper/2211.14667/) (2022)
- [No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation](https://mlsanity.com/paper/2303.18049/) (2023)
- [Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation](https://mlsanity.com/paper/2403.14706/) (2024)
- [Analysis of child development facts and myths using text mining techniques and classification models](https://mlsanity.com/paper/2408.13091/) (2024)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)

## Abstract (arXiv)

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its source. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. We hope this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but more importantly, explainable.