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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Stance Detection models and implementationsDatasets
LIAR
ARC (AI2 Reasoning Challenge)
Snopes
FNC-1
VAST
COVID-CQ
Subtasks
Latest papers
EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection
Anthropogenic ecological crisis constitutes a significant challenge that all within the academy must urgently face, including the Natural Language Processing (NLP) community.
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics.
Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices
radiukpavlo/transition-matrix-dl
•
•
Mathematics 2024
In this work, we propose a novel approach that utilizes a transition matrix to interpret results from DL models through more comprehensible machine learning (ML) models.
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
szu-ddj/edda
•
•
To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework.
STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
isabellelorge/contradiction
•
•
The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change.
IUST at ClimateActivism 2024: Towards Optimal Stance Detection: A Systematic Study of Architectural Choices and Data Cleaning Techniques
This work presents a systematic search of various model architecture configurations and data cleaning methods.
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
We present Stance Reasoner, an approach to zero-shot stance detection on social media that leverages explicit reasoning over background knowledge to guide the model's inference about the document's stance on a target.
A Challenge Dataset and Effective Models for Conversational Stance Detection
Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions.
Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA
This study details our approach for the CASE 2024 Shared Task on Climate Activism Stance and Hate Event Detection, focusing on Hate Speech Detection, Hate Speech Target Identification, and Stance Detection as classification challenges.
Putting Context in Context: the Impact of Discussion Structure on Text Classification
dhfbk/pucc
•
•
We also experiment with different amounts of training data and analyse the topology of local discussion networks in a privacy-compliant way.
