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
Latest papers with no code
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model.
Collaborative Knowledge Infusion for Low-resource Stance Detection
Specifically, our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment.
SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target.
DIVERSE: Deciphering Internet Views on the U.S. Military Through Video Comment Stance Analysis, A Novel Benchmark Dataset for Stance Classification
Stance detection of social media text is a key component of downstream tasks involving the identification of groups of users with opposing opinions on contested topics such as vaccination and within arguments.
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
We detail (i) a method of claim identification -- the task of identifying if a post title contains a claim and (ii) an opinion mining-driven evaluation framework for stance detection using LLMs.
Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance
We investigate the performance of LLM-based zero-shot stance detection on tweets.
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection at CASE 2024.
DEEM: Dynamic Experienced Expert Modeling for Stance Detection
Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results.
Multi-modal Stance Detection: New Datasets and Model
Stance detection is a challenging task that aims to identify public opinion from social media platforms with respect to specific targets.
Mitigating Biases of Large Language Models in Stance Detection with Calibration
Large language models (LLMs) have achieved remarkable progress in many natural language processing tasks.
