# User Preference-aware Fake News Detection

arXiv: https://arxiv.org/abs/2104.12259 · HTML: https://mlsanity.com/paper/2104.12259/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- ["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)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT](https://mlsanity.com/paper/2003.04985/) (2020)
- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation](https://mlsanity.com/paper/1903.09196/) (2019)
- [Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection](https://mlsanity.com/paper/1804.03124/) (2018)
- [Graph-based Modeling of Online Communities for Fake News Detection](https://mlsanity.com/paper/2008.06274/) (2020)
- [Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks](https://mlsanity.com/paper/2010.02394/) (2020)
- [Studying Fake News via Network Analysis: Detection and Mitigation](https://mlsanity.com/paper/1804.10233/) (2018)
- [VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter](https://mlsanity.com/paper/2101.08210/) (2021)

## Built on by (most cited first, 30 of 79)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices](https://mlsanity.com/paper/2111.03220/) (2021)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models](https://mlsanity.com/paper/2310.05046/) (2023)
- [Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND](https://mlsanity.com/paper/2404.17099/) (2024)
- [Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization](https://mlsanity.com/paper/2306.15902/) (2023)
- [GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking](https://mlsanity.com/paper/2312.05739/) (2023)
- [Explore the Potential of LLMs in Misinformation Detection: An Empirical Study](https://mlsanity.com/paper/2311.12699/) (2023)
- [The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection](https://mlsanity.com/paper/2210.12384/) (2022)
- [On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach](https://mlsanity.com/paper/2212.07056/) (2022)
- [Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection](https://mlsanity.com/paper/2309.16424/) (2023)
- [A General Black-box Adversarial Attack on Graph-based Fake News Detectors](https://mlsanity.com/paper/2404.15744/) (2024)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [MSynFD: Multi-hop Syntax aware Fake News Detection](https://mlsanity.com/paper/2402.14834/) (2024)
- [On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks](https://mlsanity.com/paper/2409.17475/) (2024)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)
- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)
- [SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection](https://mlsanity.com/paper/2310.10237/) (2023)
- [Multitask Active Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2401.13210/) (2024)
- [Distributed-Order Fractional Graph Operating Network](https://mlsanity.com/paper/2411.05274/) (2024)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks](https://mlsanity.com/paper/2212.12621/) (2022)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs](https://mlsanity.com/paper/2410.12600/) (2024)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)

## Abstract (arXiv)

Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored. The confirmation bias theory has indicated that a user is more likely to spread a piece of fake news when it confirms his/her existing beliefs/preferences. Users' historical, social engagements such as posts provide rich information about users' preferences toward news and have great potential to advance fake news detection. However, the work on exploring user preference for fake news detection is somewhat limited. Therefore, in this paper, we study the novel problem of exploiting user preference for fake news detection. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. We release our code and data as a benchmark for GNN-based fake news detection: https://github.com/safe-graph/GNN-FakeNews.