# A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection

arXiv: https://arxiv.org/abs/2502.14403 · HTML: https://mlsanity.com/paper/2502.14403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [Interpretable Rumor Detection in Microblogs by Attending to User Interactions](https://mlsanity.com/paper/2001.10667/) (2020)
- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [A Topic-Agnostic Approach for Identifying Fake News Pages](https://mlsanity.com/paper/1905.00957/) (2019)
- [GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking](https://mlsanity.com/paper/2312.05739/) (2023)
- [Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention](https://mlsanity.com/paper/2303.11945/) (2023)
- [MSynFD: Multi-hop Syntax aware Fake News Detection](https://mlsanity.com/paper/2402.14834/) (2024)
- [Two-path Deep Semi-supervised Learning for Timely Fake News Detection](https://mlsanity.com/paper/2002.00763/) (2020)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)

## Built on by (most cited first, 5 of 5)

- [FakeZero: Real-Time, Privacy-Preserving Misinformation Detection for Facebook and X](https://mlsanity.com/paper/2510.25932/) (2025)
- [Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection](https://mlsanity.com/paper/2512.21039/) (2025)
- [SARC: Sentiment-Augmented Deep Role Clustering for Fake News Detection](https://mlsanity.com/paper/2511.04692/) (2025)
- [Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling](https://mlsanity.com/paper/2602.01726/) (2026)
- [Retrieval-Augmented Multimodal Model for Fake News Detection](https://mlsanity.com/paper/2604.18112/) (2026)

## Abstract (arXiv)

Cross-domain fake news detection aims to mitigate domain shift and improve detection performance by transferring knowledge across domains. Existing approaches transfer knowledge based on news content and user engagements from a source domain to a target domain. However, these approaches face two main limitations, hindering effective knowledge transfer and optimal fake news detection performance. Firstly, from a micro perspective, they neglect the negative impact of veracity-irrelevant features in news content when transferring domain-shared features across domains. Secondly, from a macro perspective, existing approaches ignore the relationship between user engagement and news content, which reveals shared behaviors of common users across domains and can facilitate more effective knowledge transfer. To address these limitations, we propose a novel macro- and micro- hierarchical transfer learning framework (MMHT) for cross-domain fake news detection. Firstly, we propose a micro-hierarchical disentangling module to disentangle veracity-relevant and veracity-irrelevant features from news content in the source domain for improving fake news detection performance in the target domain. Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms the state-of-the-art baselines.