# Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection

arXiv: https://arxiv.org/abs/1909.01720 · HTML: https://mlsanity.com/paper/1909.01720/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets](https://mlsanity.com/paper/2211.03533/) (2022)
- [Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection](https://mlsanity.com/paper/2309.16424/) (2023)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [Unified Dual-view Cognitive Model for Interpretable Claim Verification](https://mlsanity.com/paper/2105.09567/) (2021)
- [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023)
- [Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction](https://mlsanity.com/paper/2108.02314/) (2021)
- [Dynamically Addressing Unseen Rumor via Continual Learning](https://mlsanity.com/paper/2104.08775/) (2021)
- [Tackling Multiple Tasks with One Single Learning Framework](https://mlsanity.com/paper/2206.06322/) (2022)
- [Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study](https://mlsanity.com/paper/2301.05863/) (2023)

## Abstract (arXiv)

Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%, 1.31%, respectively.