Stance Classification
23 papers with code • 1 benchmarks • 8 datasets
Most implemented papers
A Retrospective Analysis of the Fake News Challenge Stance Detection Task
To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
Simple Open Stance Classification for Rumour Analysis
Stance classification determines the attitude, or stance, in a (typically short) text.
Stance Prediction for Russian: Data and Analysis
As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Automated fact-checking systems verify claims against evidence to predict their veracity.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A).
Cross-Target Stance Classification with Self-Attention Networks
In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target.
A Retrospective Analysis of the Fake News Challenge Stance-Detection Task
To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets.
BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers
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).
Danish Stance Classification and Rumour Resolution
Furthermore, experiments show that stance labels can be used across languages and platforms with a HMM to predict the veracity of rumours, achieving an accuracy of 0. 82 and F1 score of 0. 67.