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

## TL;DR

This paper presents a sequential Branch-LSTM model for rumour stance classification on Twitter, achieving state-of-the-art accuracy by modeling conversational structure to identify user attitudes towards rumours.

## Contribution

Introduces a novel Branch-LSTM model that leverages conversational structure for improved rumour stance classification on Twitter.

## Key findings

- Achieved 78.4% accuracy on RumourEval test set.
- Outperformed all other systems in Subtask A.
- Demonstrated effectiveness of modeling conversational structure.

## Abstract

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.