Stance Detection
106 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
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.
Cross-target Stance Detection by Exploiting Target Analytical Perspectives
In this paper, we propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge.
A Logically Consistent Chain-of-Thought Approach for Stance Detection
Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets.
InfoPattern: Unveiling Information Propagation Patterns in Social Media
Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation.
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Social media platforms are rife with politically charged discussions.
Evolving Domain Adaptation of Pretrained Language Models for Text Classification
Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance detection.
Enhancing Stance Classification with Quantified Moral Foundations
The results highlight the importance of considering deeper psychological attributes in stance analysis and underscores the role of moral foundations in guiding online social behavior.
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Current model testing work has mostly focused on creating test cases.
STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive Learning and Counterfactual Generation
We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection.
