# Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations

arXiv: https://arxiv.org/abs/1609.09028 · HTML: https://mlsanity.com/paper/1609.09028/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

## Built on by (most cited first, 12 of 12)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation](https://mlsanity.com/paper/2210.13865/) (2022)
- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
- [VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text](https://mlsanity.com/paper/2102.00816/) (2021)
- [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)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [A Weakly Supervised Approach for Classifying Stance in Twitter Replies](https://mlsanity.com/paper/2103.07098/) (2021)
- [Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis](https://mlsanity.com/paper/2302.02813/) (2023)
- [Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification](https://mlsanity.com/paper/2112.12809/) (2021)
- [Examining Political Rhetoric with Epistemic Stance Detection](https://mlsanity.com/paper/2212.14486/) (2022)
- [SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media](https://mlsanity.com/paper/2403.05216/) (2024)

## Abstract (arXiv)

Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users' replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.