# All-in-one: Multi-task Learning for Rumour Verification

arXiv: https://arxiv.org/abs/1806.03713 · HTML: https://mlsanity.com/paper/1806.03713/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2018.

## Built on by (most cited first, 30 of 54)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2107.11934/) (2021)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://mlsanity.com/paper/2306.14728/) (2023)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language](https://mlsanity.com/paper/2103.10195/) (2021)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense](https://mlsanity.com/paper/2203.10346/) (2022)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [Robust Domain Misinformation Detection via Multi-modal Feature Alignment](https://mlsanity.com/paper/2311.14315/) (2023)
- [TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection](https://mlsanity.com/paper/2402.07776/) (2024)
- [VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text](https://mlsanity.com/paper/2102.00816/) (2021)
- [SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking](https://mlsanity.com/paper/2110.13090/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks](https://mlsanity.com/paper/2209.08799/) (2022)
- [CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild](https://mlsanity.com/paper/2301.06494/) (2023)
- [Rumour Detection via Zero-shot Cross-lingual Transfer Learning](https://mlsanity.com/paper/2109.12773/) (2021)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions](https://mlsanity.com/paper/2403.07910/) (2024)
- [Dynamically Addressing Unseen Rumor via Continual Learning](https://mlsanity.com/paper/2104.08775/) (2021)
- [Multi-View Active Learning for Short Text Classification in User-Generated Data](https://mlsanity.com/paper/2112.02611/) (2021)

## Abstract (arXiv)

Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used.