Papers Stance Classification
“Stance Classification” 태그가 달린 논문 101편 · 필터 해제
SCRum-9: Multilingual Stance Classification over Rumours on Social Media
We introduce SCRum-9, a multilingual dataset for Rumour Stance Classification, containing 7,516 tweet-reply pairs from X. SCRum-9 goes beyond existing stance classification datasets by covering more languages (9), linkin…
Rumour DetectionStance ClassificationAcquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings
Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classificat…
Discourse ParsingStance ClassificationStance DetectionLabel Set Optimization via Activation Distribution Kurtosis for Zero-shot Classification with Generative Models
In-context learning (ICL) performance is known to be sensitive to the prompt design, yet the impact of class label options in zero-shot classification has been largely overlooked. This study presents the first comprehens…
ClassificationIn-Context LearningStance Classificationzero-shot-classification+1A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media
Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing ben…
Stance ClassificationExamining the Influence of Political Bias on Large Language Model Performance in Stance Classification
Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racia…
ClassificationLanguage ModelingLanguage ModellingLarge Language Model+2USDC: A Dataset of $\underline{U}$ser $\underline{S}$tance and $\underline{D}$ogmatism in Long $\underline{C}$onversations
Identifying user's opinions and stances in long conversation threads on various topics can be extremely critical for enhanced personalization, market research, political campaigns, customer service, conflict resolution, …
Stance ClassificationExU: AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread
Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on developing AI-based models for multilin…
Fact CheckingRetrievalStance ClassificationSurveyDetecting Statements in Text: A Domain-Agnostic Few-Shot Solution
Many tasks related to Computational Social Science and Web Content Analysis involve classifying pieces of text based on the claims they contain. State-of-the-art approaches usually involve fine-tuning models on large ann…
Few-Shot LearningNatural Language InferenceStance ClassificationWhat is Sentiment Meant to Mean to Language Models?
Sentiment analysis is one of the most widely used techniques in text analysis. Recent advancements with Large Language Models have made it more accurate and accessible than ever, allowing researchers to classify text wit…
Sentiment AnalysisStance ClassificationWIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining
We propose WIBA, a novel framework and suite of methods that enable the comprehensive understanding of "What Is Being Argued" across contexts. Our approach develops a comprehensive framework that detects: (a) the existen…
Argument MiningLanguage ModellingPrompt EngineeringSentence+1Rumour Evaluation with Very Large Language Models
Conversational prompt-engineering-based large language models (LLMs) have enabled targeted control over the output creation, enhancing versatility, adaptability and adhoc retrieval. From another perspective, digital misi…
MisinformationPrompt EngineeringRumour DetectionStance ClassificationDIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model
Public opinion of military organizations significantly influences their ability to recruit talented individuals. As recruitment efforts increasingly extend into digital spaces like social media, it becomes essential to a…
MarketingSentenceStance ClassificationStance DetectionInfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impact a test prediction. Our objectives in …
Sarcasm DetectionStance ClassificationStanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation
The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation. In the Stan…
Cross-Lingual TransferMisinformationStance ClassificationZero-Shot Cross-Lingual TransferMultilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper
Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms curren…
ArticlesInstruction FollowingStance ClassificationBeyond Sentiment: Leveraging Topic Metrics for Political Stance Classification
Sentiment analysis, widely critiqued for capturing merely the overall tone of a corpus, falls short in accurately reflecting the latent structures and political stances within texts. This study introduces topic metrics, …
Sentiment AnalysisStance ClassificationEnhancing Stance Classification on Social Media Using Quantified Moral Foundations
This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals' moral foundations. These theoretically-derived dimensions aim to provide a comprehensive pr…
ClassificationStance ClassificationStance DetectionOverview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining
This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023. The shared task comprises two classificati…
Argument MiningClassificationPersuasivenessStance ClassificationTILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining
A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both text an…
Argument MiningStance ClassificationPrompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to machine learning. Current stance detect…
ClassificationSentenceStance ClassificationStance Detection