# Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

arXiv: https://arxiv.org/abs/2209.08902 · HTML: https://mlsanity.com/paper/2209.08902/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 61 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [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)
- [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://mlsanity.com/paper/1805.11749/) (2018)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2201.06885/) (2022)
- [Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies](https://mlsanity.com/paper/1905.05475/) (2019)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository](https://mlsanity.com/paper/2002.00837/) (2020)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Multimodal Emergent Fake News Detection via Meta Neural Process Networks](https://mlsanity.com/paper/2106.13711/) (2021)
- [Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection](https://mlsanity.com/paper/2201.01004/) (2022)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [Detecting False Rumors from Retweet Dynamics on Social Media](https://mlsanity.com/paper/2201.13103/) (2022)

## Built on by (most cited first, 19 of 19)

- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs](https://mlsanity.com/paper/2403.01988/) (2024)
- [Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments](https://mlsanity.com/paper/2406.09815/) (2024)
- [Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://mlsanity.com/paper/2306.14728/) (2023)
- [Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting](https://mlsanity.com/paper/2310.10429/) (2023)
- [Why Misinformation is Created? Detecting them by Integrating Intent Features](https://mlsanity.com/paper/2407.19196/) (2024)
- [Robust Domain Misinformation Detection via Multi-modal Feature Alignment](https://mlsanity.com/paper/2311.14315/) (2023)
- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing](https://mlsanity.com/paper/2304.12888/) (2023)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection](https://mlsanity.com/paper/2406.11260/) (2024)
- [Learning Domain-Invariant Features for Out-of-Context News Detection](https://mlsanity.com/paper/2406.07430/) (2024)
- [Robust Misinformation Detection by Visiting Potential Commonsense Conflict](https://mlsanity.com/paper/2504.21604/) (2025)
- [Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News Detection](https://mlsanity.com/paper/2312.01006/) (2023)
- [Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection](https://mlsanity.com/paper/2509.01660/) (2025)
- [COOL: Comprehensive Knowledge Enhanced Prompt Learning for Domain Adaptive Few-shot Fake News Detection](https://mlsanity.com/paper/2406.10870/) (2024)
- [Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach](https://mlsanity.com/paper/2406.10238/) (2024)
- [Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts](https://mlsanity.com/paper/2602.21214/) (2026)
- [CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detection](https://mlsanity.com/paper/2604.04174/) (2026)

## Abstract (arXiv)

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance's contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario.