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 251–300 of 343 papers

TitleStatusHype
Multi-Task Stance Detection with Sentiment and Stance Lexicons—0
Incorporating Label Dependencies in Multilabel Stance DetectionCode0
Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task—0
A Richly Annotated Corpus for Different Tasks in Automated Fact-CheckingCode0
Contrastive Language Adaptation for Cross-Lingual Stance Detection—0
結合LDA與SVM之社群使用者立場檢測(Stance Detection of Social Network Users by combining Latent Dirichlet Allocation and Support Vector Machine)—0
Deep Ensemble Learning for News Stance Detection—0
Political Stance in DanishCode0
Assessing Sentiment of the Expressed Stance on Social Media—0
Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social MediaCode0
A Weakly-Supervised Attention-based Visualization Tool for Assessing Political Affiliation—0
Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection—0
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning—0
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities—0
FAKTA: An Automatic End-to-End Fact Checking System—0
Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes—0
Tweet Stance Detection Using an Attention based Neural Ensemble Model—0
Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning—0
Adversarial Domain Adaptation for Stance Detection—0
A Tweet Dataset Annotated for Named Entity Recognition and Stance DetectionCode0
Variational Self-attention Model for Sentence Representation—0
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical EncoderCode0
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake NewsCode0
Stance Detection in Fake News A Combined Feature Representation—0
Dave the debater: a retrieval-based and generative argumentative dialogue agent—0
Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles—0
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging—0
Stance Prediction for Russian: Data and AnalysisCode0
Debunking Fake News One Feature at a TimeCode0
How did the discussion go: Discourse act classification in social media conversations—0
Stance Detection with Hierarchical Attention Network—0
Predicting Stances from Social Media Posts using Factorization Machines—0
A Retrospective Analysis of the Fake News Challenge Stance-Detection TaskCode0
Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection—0
A Retrospective Analysis of the Fake News Challenge Stance Detection TaskCode0
Stance-In-Depth Deep Neural Approach to Stance Classification—0
Joker at SemEval-2018 Task 12: The Argument Reasoning Comprehension with Neural Attention—0
Learning Sentence Representations over Tree Structures for Target-Dependent Classification—0
360 ^ Stance Detection—0
An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System—0
Quantifying Qualitative Data for Understanding Controversial Issues—0
Integrating Stance Detection and Fact Checking in a Unified Corpus—0
Automatic Stance Detection Using End-to-End Memory Networks—0
360° Stance Detection—0
Stance Detection on Tweets: An SVM-based Approach—0
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017—0
Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention—0
On the Benefit of Combining Neural, Statistical and External Features for Fake News IdentificationCode0
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling—0
A Crowdsourcing Approach for Annotating Causal Relation Instances in Wikipedia—0
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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