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Stance Detection
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges
Language Models Learn Metadata: Political Stance Detection Case Study
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model
Can Large Language Models Address Open-Target Stance Detection?
Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation
dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
Advancing Annotation of Stance in Social Media Posts: A Comparative Analysis of Large Language Models and Crowd Sourcing
Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models
Zero-Shot Stance Detection using Contextual Data Generation with LLMs
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation
Stance Detection on Social Media with Fine-Tuned Large Language Models
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
Collaborative Knowledge Infusion for Low-resource Stance Detection
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
A Challenge Dataset and Effective Models for Conversational Stance Detection
SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
DEEM: Dynamic Experienced Expert Modeling for Stance Detection
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration
Multi-modal Stance Detection: New Datasets and Model
Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA
Putting Context in Context: the Impact of Discussion Structure on Text Classification
Cross-target Stance Detection by Exploiting Target Analytical Perspectives
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