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
Putting Context in Context: the Impact of Discussion Structure on Text Classification
dhfbk/pucc
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We also experiment with different amounts of training data and analyse the topology of local discussion networks in a privacy-compliant way.
Identification of Multimodal Stance Towards Frames of Communication
Supermaxman/MMVax-Stance
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EMNLP 2023
In this paper we introduce MMVax-Stance, a dataset of 11, 300 multimedia documents retrieved from social media, which have stance annotations towards 113 different frames of communication.
Data and models for stance and premise detection in COVID-19 tweets: insights from the Social Media Mining for Health (SMM4H) 2022 shared task
veranchos/argmining_tweets
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The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination.
Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection
Data collection from manual labeling provides domain-specific and task-aligned supervision for data-driven approaches, and a critical mass of well-annotated resources is required to achieve reasonable performance in natural language processing tasks.
Chain-of-Thought Embeddings for Stance Detection on Social Media
Chain-of-Thought (COT) prompting has recently been shown to improve performance on stance detection tasks -- alleviating some of these issues.
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
hanshanley/tata
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Stance detection is important for understanding different attitudes and beliefs on the Internet.
Stance Detection with Collaborative Role-Infused LLM-Based Agents
Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge.
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
The moderation of content on online platforms is usually non-transparent.
Stance Prediction and Analysis of Twitter data : A case study of Ghana 2020 Presidential Elections
We utilized Logistic Regression to classify all the extracted tweets and subsequently conducted an analysis and discussion of the results.
