# Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media

arXiv: https://arxiv.org/abs/2403.00037 · HTML: https://mlsanity.com/paper/2403.00037/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 27 of 27)

- [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)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Towards Debiasing Fact Verification Models](https://mlsanity.com/paper/1908.05267/) (2019)
- [An Empirical Study of Graph Contrastive Learning](https://mlsanity.com/paper/2109.01116/) (2021)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://mlsanity.com/paper/2110.08527/) (2021)
- [Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://mlsanity.com/paper/2004.11999/) (2020)
- [Gender-preserving Debiasing for Pre-trained Word Embeddings](https://mlsanity.com/paper/1906.00742/) (2019)
- [Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification](https://mlsanity.com/paper/1905.04579/) (2019)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond](https://mlsanity.com/paper/1811.02545/) (2018)
- [GraphCrop: Subgraph Cropping for Graph Classification](https://mlsanity.com/paper/2009.10564/) (2020)
- [APAC: Augmented PAttern Classification with Neural Networks](https://mlsanity.com/paper/1505.03229/) (2015)
- [Automated Data Augmentations for Graph Classification](https://mlsanity.com/paper/2202.13248/) (2022)
- [Label-invariant Augmentation for Semi-Supervised Graph Classification](https://mlsanity.com/paper/2205.09802/) (2022)
- [Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach](https://mlsanity.com/paper/1706.06314/) (2017)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)

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

- [Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator](https://mlsanity.com/paper/2504.04076/) (2025)
- [Robust Misinformation Detection by Visiting Potential Commonsense Conflict](https://mlsanity.com/paper/2504.21604/) (2025)
- [Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations](https://mlsanity.com/paper/2508.03420/) (2025)
- [SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation](https://mlsanity.com/paper/2602.19840/) (2026)
- [Spectral Analysis of Fake News Propagation](https://mlsanity.com/paper/2605.13861/) (2026)

## Abstract (arXiv)

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on future events. Recently, numerous fake news detection models that utilize textual information and the propagation structure of posts have been proposed. Unfortunately, most of the existing approaches can hardly handle this challenge since they rely heavily on event-specific features for prediction and cannot generalize to unseen events. To address this, we introduce Future ADaptive Event-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. We further mitigate event bias by subtracting the event-only predictor's output from the target predictor's output to obtain the final prediction. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches.