# Studying Fake News via Network Analysis: Detection and Mitigation

arXiv: https://arxiv.org/abs/1804.10233 · HTML: https://mlsanity.com/paper/1804.10233/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection](https://mlsanity.com/paper/2107.10648/) (2021)
- [Detection of Human and Machine-Authored Fake News in Urdu](https://mlsanity.com/paper/2410.19517/) (2024)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning](https://mlsanity.com/paper/2402.03357/) (2024)
- [MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection](https://mlsanity.com/paper/2210.05401/) (2022)
- [Identification of COVID-19 related Fake News via Neural Stacking](https://mlsanity.com/paper/2101.03988/) (2021)
- [Fake News Detection Tools and Methods -- A Review](https://mlsanity.com/paper/2112.11185/) (2021)
- [Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- Transformer Ensemble Models Tackling Deception and Persuasion](https://mlsanity.com/paper/2311.18730/) (2023)
- [Supporting verification of news articles with automated search for semantically similar articles](https://mlsanity.com/paper/2103.15581/) (2021)
- [Graphical Models of False Information and Fact Checking Ecosystems](https://mlsanity.com/paper/2208.11582/) (2022)
- [Language-Agnostic Modeling of Source Reliability on Wikipedia](https://mlsanity.com/paper/2410.18803/) (2024)
- [Fake News Classification in Urdu: A Domain Adaptation Approach for a Low-Resource Language](https://mlsanity.com/paper/2512.22778/) (2025)

## Abstract (arXiv)

Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence, news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.