# Graphical Models of False Information and Fact Checking Ecosystems

arXiv: https://arxiv.org/abs/2208.11582 · HTML: https://mlsanity.com/paper/2208.11582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 76 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [False Information on Web and Social Media: A Survey](https://mlsanity.com/paper/1804.08559/) (2018)
- [Hoaxy: A Platform for Tracking Online Misinformation](https://mlsanity.com/paper/1603.01511/) (2016)
- [Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements](https://mlsanity.com/paper/2001.00623/) (2020)
- [Human and technological infrastructures of fact-checking](https://mlsanity.com/paper/2205.10894/) (2022)
- [Studying Fake News via Network Analysis: Detection and Mitigation](https://mlsanity.com/paper/1804.10233/) (2018)
- [TweetCred: Real-Time Credibility Assessment of Content on Twitter](https://mlsanity.com/paper/1405.5490/) (2014)
- [Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms](https://mlsanity.com/paper/2204.13568/) (2022)
- [NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges](https://mlsanity.com/paper/2108.01536/) (2021)

## Abstract (arXiv)

The wide spread of false information online, including misinformation and disinformation, has become a major problem for our highly digitised and globalised society. A lot of research has been done to better understand different aspects of false information online such as behaviours of different actors and patterns of spreading, and also on better detection and prevention of such information using technical and socio-technical means. One major approach to detecting and debunking false information online is to use fact-checkers. Despite a lot of research on online false information, we noticed a lack of conceptual models describing the complicated ecosystem of false information and fact-checking. In this paper, we introduce the first comprehensive graphical model of the ecosystem, focusing on false information online in multiple contexts, including traditional media outlets, as well as user-generated and AI-generated content. The proposed enhanced entity-relationship (EER) model covers a wide range of entities and relationships, and it can be a new useful tool for researchers and practitioners to study false information online and the effects of fact-checking. To demonstrate its usefulness, we provide two example applications of our proposed model, modelling real-world scenarios and reviewing the research literature.