Stance Detection
105 papers with code • 20 benchmarks • 31 datasets
Stance detection is the extraction of a subject's reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment.
Example:
- Source: "Apples are the most delicious fruit in existence"
- Reply: "Obviously not, because that is a reuben from Katz's"
- Stance: deny
Benchmarks
These leaderboards are used to track progress in Stance Detection
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Libraries
Use these libraries to find Stance Detection models and implementationsDatasets
LIAR
ARC (AI2 Reasoning Challenge)
Snopes
FNC-1
VAST
COVID-CQ
Subtasks
Most implemented papers
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
copenlu/TESTED
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The results show that our method outperforms the state-of-the-art with an average of $3. 5$ F1 points increase in-domain, and is more generalizable with an averaged increase of $10. 2$ F1 on out-of-domain evaluation while using $\leq10\%$ of the training data.
IUST at ClimateActivism 2024: Towards Optimal Stance Detection: A Systematic Study of Architectural Choices and Data Cleaning Techniques
ghazaleh-mahmoodi/Climate_Activism_Stance_Detection
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CASE at EACL 2024
This work presents a systematic search of various model architecture configurations and data cleaning methods.
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
myrthereuver/modeldecisionsstance
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In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics.
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets.
Stance Detection with Bidirectional Conditional Encoding
sheffieldnlp/stance-conditional
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EMNLP 2016
Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral".
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).
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this problem.
A Retrospective Analysis of the Fake News Challenge Stance-Detection Task
UKPLab/coling2018_fake-news-challenge
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COLING 2018
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.
Debunking Fake News One Feature at a Time
Identifying the stance of a news article body with respect to a certain headline is the first step to automated fake news detection.
A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions.
