# Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity

arXiv: https://arxiv.org/abs/1909.08211 · HTML: https://mlsanity.com/paper/1909.08211/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2107.11934/) (2021)
- [Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?](https://mlsanity.com/paper/2208.09214/) (2022)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information](https://mlsanity.com/paper/2502.08888/) (2025)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [Rumor Detection on Social Media with Temporal Propagation Structure Optimization](https://mlsanity.com/paper/2412.08316/) (2024)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media](https://mlsanity.com/paper/2212.02419/) (2022)
- [Rumour detection using graph neural network and oversampling in benchmark Twitter dataset](https://mlsanity.com/paper/2212.10080/) (2022)
- [The Branch Not Taken: Predicting Branching in Online Conversations](https://mlsanity.com/paper/2404.13613/) (2024)
- [Towards Effective Planning Strategies for Dynamic Opinion Networks](https://mlsanity.com/paper/2410.14091/) (2024)
- [A Cross-Modal Rumor Detection Scheme via Contrastive Learning by Exploring Text and Image internal Correlations](https://mlsanity.com/paper/2508.11141/) (2025)

## Abstract (arXiv)

Automatically verifying rumorous information has become an important and challenging task in natural language processing and social media analytics. Previous studies reveal that people's stances towards rumorous messages can provide indicative clues for identifying the veracity of rumors, and thus determining the stances of public reactions is a crucial preceding step for rumor veracity prediction. In this paper, we propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter, which consists of two components. The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network. The top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution. Experimental results on two benchmark datasets show that our method outperforms previous methods in both rumor stance classification and veracity prediction.