# Network-based Fake News Detection: A Pattern-driven Approach

arXiv: https://arxiv.org/abs/1906.04210 · HTML: https://mlsanity.com/paper/1906.04210/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

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

- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://mlsanity.com/paper/2306.14728/) (2023)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Blessing or curse? A survey on the Impact of Generative AI on Fake News](https://mlsanity.com/paper/2404.03021/) (2024)
- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention](https://mlsanity.com/paper/2407.19332/) (2024)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection](https://mlsanity.com/paper/2104.11476/) (2021)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [Financial data analysis application via multi-strategy text processing](https://mlsanity.com/paper/2204.11394/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting](https://mlsanity.com/paper/2102.00509/) (2021)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach](https://mlsanity.com/paper/2402.06443/) (2024)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection](https://mlsanity.com/paper/2409.01787/) (2024)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)
- [Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection](https://mlsanity.com/paper/2108.13892/) (2021)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection](https://mlsanity.com/paper/2503.03107/) (2025)
- [Fake News Detection Tools and Methods -- A Review](https://mlsanity.com/paper/2112.11185/) (2021)
- [FNDaaS: Content-agnostic Detection of Fake News sites](https://mlsanity.com/paper/2212.06492/) (2022)
- [BiMind: A Dual-Head Reasoning Model with Attention-Geometry Adapter for Incorrect Information Detection](https://mlsanity.com/paper/2604.06022/) (2026)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification](https://mlsanity.com/paper/2206.11867/) (2022)
- [Towards Smart Fake News Detection Through Explainable AI](https://mlsanity.com/paper/2207.11490/) (2022)
- [FNDEX: Fake News and Doxxing Detection with Explainable AI](https://mlsanity.com/paper/2410.22390/) (2024)

## Abstract (arXiv)

Fake news gains has gained significant momentum, strongly motivating the need for fake news research. Many fake news detection approaches have thus been proposed, where most of them heavily rely on news content. However, network-based clues revealed when analyzing news propagation on social networks is an information that has hardly been comprehensively explored or used for fake news detection. We bridge this gap by proposing a network-based pattern-driven fake news detection approach. We aim to study the patterns of fake news in social networks, which refer to the news being spread, spreaders of the news and relationships among the spreaders. Empirical evidence and interpretations on the existence of such patterns are provided based on social psychological theories. These patterns are then represented at various network levels (i.e., node-level, ego-level, triad-level, community-level and the overall network) for being further utilized to detect fake news. The proposed approach enhances the explainability in fake news feature engineering. Experiments conducted on real-world data demonstrate that the proposed approach can outperform the state of the arts.