# Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM

arXiv: https://arxiv.org/abs/1704.07221 · HTML: https://mlsanity.com/paper/1704.07221/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 20 of 20)

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- [A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning](https://mlsanity.com/paper/2204.02626/) (2022)
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- [Rumor Detection on Social Media with Temporal Propagation Structure Optimization](https://mlsanity.com/paper/2412.08316/) (2024)
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- [Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification](https://mlsanity.com/paper/2112.12809/) (2021)
- [Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media](https://mlsanity.com/paper/2212.02419/) (2022)
- [Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification](https://mlsanity.com/paper/2303.12665/) (2023)
- [POLygraph: Polish Fake News Dataset](https://mlsanity.com/paper/2407.01393/) (2024)
- [FarExStance: Explainable Stance Detection for Farsi](https://mlsanity.com/paper/2412.14008/) (2024)
- [Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit](https://mlsanity.com/paper/2606.04274/) (2026)
- [TruthStance: An Annotated Dataset of Conversations on Truth Social](https://mlsanity.com/paper/2602.14406/) (2026)

## Abstract (arXiv)

This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.