# BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers

## TL;DR

This paper presents a system using pre-trained BERT transformers to classify the stance of social media posts towards rumors, achieving high accuracy and second place in a competitive challenge.

## Contribution

The novel application of BERT for rumor stance classification without hand-crafted features, achieving competitive performance in SemEval 2019 Task 7.

## Key findings

- Achieved 61.67% F1 score on test data
- Placed second in the SemEval-2019 Task 7 competition
- Outperformed many existing approaches with a transformer-based model

## Abstract

This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67% on the provided test data. It finished at the 2nd place in the competition, without any hand-crafted features, only 0.2% behind the winner.