SOTAVerified

Stance Detection

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–343 of 343 papers

TitleStatusHype
Stance Detection on Social Media: State of the Art and Trends—0
Deep Ensemble Learning for News Stance Detection—0
DeInStance: Creating and Evaluating a German Corpus for Fine-Grained Inferred Stance Detection—0
Stance Detection on Social Media with Fine-Tuned Large Language Models—0
Stance Detection on Tweets: An SVM-based Approach—0
Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media—0
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment—0
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse—0
Active learning for detection of stance components—0
Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection—0
A Survey on Stance Detection for Mis- and Disinformation Identification—0
DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model—0
Do Dependency Relations Help in the Task of Stance Detection?—0
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks—0
dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers—0
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets—0
Stance Detection with Hierarchical Attention Network—0
A Crowdsourcing Approach for Annotating Causal Relation Instances in Wikipedia—0
A Survey of Stance Detection on Social Media: New Directions and Perspectives—0
Stance-Driven Multimodal Controlled Statement Generation: New Dataset and Task—0
Empirical Evaluation of Topic Zero- and Few-Shot Learning for Stance Dissonance Detection—0
Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation—0
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning—0
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge—0
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy—0
Enhancing Stance Classification on Social Media Using Quantified Moral Foundations—0
Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph—0
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News—0
Evolving Domain Adaptation of Pretrained Language Models for Text Classification—0
Stance-In-Depth Deep Neural Approach to Stance Classification—0
Abstract, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning—0
Examining Temporalities on Stance Detection towards COVID-19 Vaccination—0
University of Copenhagen Participation in TREC Health Misinformation Track 2020—0
Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings—0
Exploiting stance hierarchies for cost-sensitive stance detection of Web documents—0
Assessing Sentiment of the Expressed Stance on Social Media—0
Exploring Vision Language Models for Multimodal and Multilingual Stance Detection—0
Fake news stance detection using stacked ensemble of classifiers—0
FAKTA: An Automatic End-to-End Fact Checking System—0
Stance Quantification: Definition of the Problem—0
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination—0
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings—0
Few-Shot Stance Detection via Target-Aware Prompt Distillation—0
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All datasetsMGTABRumourEvalFNC-1ARC (AI2 Reasoning Challenge)argminDhoroniemergentiac1ibmcsmtsdPerspectrumpoldeb

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RGTAcc87.8—Unverified
2Simple-HGNAcc85.3—Unverified
3GCNAcc82.4—Unverified
4GATAcc82.2—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF166.58—Unverified
2Kochkina et al. 2017Accuracy0.78—Unverified
3Bahuleyan and Vechtomova 2017Accuracy0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF183.17—Unverified
2Transition MatrixF177.76—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF164.82—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF162.79—Unverified
#ModelMetricClaimedVerifiedStatus
1BanglaBERT-DhoroniAccuracy0.64—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF182.1—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF156.97—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF188.06—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF163.96—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF183.11—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF152.76—Unverified
#ModelMetricClaimedVerifiedStatus
1COLA+GPT3.5Average F183.4—Unverified
#ModelMetricClaimedVerifiedStatus
1BoostingF10.87—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF164.71—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF158.72—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF178.61—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTAverage F166—Unverified
#ModelMetricClaimedVerifiedStatus
1MUSE + UMAP (Unsupervised)Avg F10.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MUSE + UMAP (Unsupervised)Avg F10.84—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF157.47—Unverified
#ModelMetricClaimedVerifiedStatus
1TESTEDF170.98—Unverified