# Dynamically Addressing Unseen Rumor via Continual Learning

arXiv: https://arxiv.org/abs/2104.08775 · HTML: https://mlsanity.com/paper/2104.08775/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Episodic Memory in Lifelong Language Learning](https://mlsanity.com/paper/1906.01076/) (2019)
- [LAMOL: LAnguage MOdeling for Lifelong Language Learning](https://mlsanity.com/paper/1909.03329/) (2019)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [Towards Few-Shot Fact-Checking via Perplexity](https://mlsanity.com/paper/2103.09535/) (2021)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Efficient Meta Lifelong-Learning with Limited Memory](https://mlsanity.com/paper/2010.02500/) (2020)
- [Memory-based Parameter Adaptation](https://mlsanity.com/paper/1802.10542/) (2018)
- [Drinking from a Firehose: Continual Learning with Web-scale Natural Language](https://mlsanity.com/paper/2007.09335/) (2020)
- [Continual Learning for Sentence Representations Using Conceptors](https://mlsanity.com/paper/1904.09187/) (2019)
- [Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection](https://mlsanity.com/paper/1909.01720/) (2019)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)

## Built on by (most cited first, 3 of 3)

- [Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning](https://mlsanity.com/paper/2203.11720/) (2022)
- [Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors](https://mlsanity.com/paper/2507.05939/) (2025)
- [Taming the Beast: Learning to Control Neural Conversational Models](https://mlsanity.com/paper/2108.10561/) (2021)

## Abstract (arXiv)

Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address this issue by improving the model's generalizability, with an assumption that the model will stay unchanged even after the new outbreak of an event. In this work, we propose an alternative solution to continuously update the model in accordance with the dynamics of rumor domain creations. The biggest technical challenge associated with this new approach is the catastrophic forgetting of previous learnings due to new learnings. We adopt continual learning strategies that control the new learnings to avoid catastrophic forgetting and propose an additional strategy that can jointly be used to strengthen the forgetting alleviation.