# False Information on Web and Social Media: A Survey

arXiv: https://arxiv.org/abs/1804.08559 · HTML: https://mlsanity.com/paper/1804.08559/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 30 of 33)

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- [Graph Data Augmentation for Graph Machine Learning: A Survey](https://mlsanity.com/paper/2202.08871/) (2022)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering](https://mlsanity.com/paper/2305.04329/) (2023)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Adapting Fake News Detection to the Era of Large Language Models](https://mlsanity.com/paper/2311.04917/) (2023)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model](https://mlsanity.com/paper/2304.07633/) (2023)
- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective](https://mlsanity.com/paper/2105.09985/) (2021)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts](https://mlsanity.com/paper/2405.01660/) (2024)
- [LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media](https://mlsanity.com/paper/2504.12325/) (2025)
- [Hate Speech Classification Using SVM and Naive BAYES](https://mlsanity.com/paper/2204.07057/) (2022)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection](https://mlsanity.com/paper/2508.19633/) (2025)
- [A Study of Fake News Reading and Annotating in Social Media Context](https://mlsanity.com/paper/2109.12523/) (2021)
- [Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models](https://mlsanity.com/paper/2307.12155/) (2023)
- [Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model](https://mlsanity.com/paper/2407.08500/) (2024)
- [KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection](https://mlsanity.com/paper/2102.07857/) (2021)
- [A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs](https://mlsanity.com/paper/2407.01194/) (2024)
- [ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories](https://mlsanity.com/paper/2505.14917/) (2025)
- [Tell Me Who Your Friends Are: Using Content Sharing Behavior for News Source Veracity Detection](https://mlsanity.com/paper/2101.10973/) (2021)
- [Disinformation, Stochastic Harm, and Costly Effort: A Principal-Agent Analysis of Regulating Social Media Platforms](https://mlsanity.com/paper/2106.09847/) (2021)
- [A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp](https://mlsanity.com/paper/2109.10462/) (2021)
- [Graphical Models of False Information and Fact Checking Ecosystems](https://mlsanity.com/paper/2208.11582/) (2022)

## Abstract (arXiv)

False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.