arXiv AI
Not All or None: Dynamic Construction of Target-aware Memory Graph for Conversational Stance Detection
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arXiv Computation and Language
Oct 9
Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs
The paper evaluates Jev, a specialized decision model, on stance detection tasks using the VAST (English) and ZS-CSD (Chinese) datasets. Jev matches GPT‑5.6 and outperforms other general‑purpose LLMs on VAST, but lags behind stronger LLMs on ZS‑CSD, especially in distinguishing favor from against. The authors suggest that Jev’s limitations may stem from difficulties in understanding reply relationships and stance direction rather than conversation length.
arXiv AI
Jul 28
StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting
arXiv:2607. 24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling.
arXiv Computation and Language
Sep 1
MMDS-Bench: Benchmarking Multimodal Large Language Models on Dynamic Stance in Social Media Interactions
MMDS-Bench is a new diagnostic benchmark for multimodal dynamic stance classification in social media parent‑reply interactions. It contains 3,482 multimodal instances annotated with a seven‑label stance taxonomy, plus an 800‑instance subset that demands structured reasoning over parent and reply understanding and stance‑relation inference. The benchmark also tags each instance with five challenge factors—multimodal fusion, parent framing, non‑literal expression, interaction reasoning, and label‑boundary ambiguity—and evaluates 12 multimodal large language models using a reference‑grounded LLM‑judge protocol, revealing that current models still struggle with relational inference beyond separate parent and reply comprehension.
arXiv AI
Sep 2
Visual Framing for News Stance Detection via Image Generation
The paper introduces VFStance, a method that uses image generation to make implicit stance cues in news articles more explicit through visual framing. It targets article-level news stance detection, a task complicated by subtle, structurally complex texts. Experiments show VFStance outperforms existing methods, and a user study with 200 participants demonstrates that the visual framing makes stance signals more noticeable in a snippet-based news consumption setting.
arXiv AI
Jul 14
StanceMoE: Mixture-of-Experts Architecture for Stance Detection
arXiv:2604. 00878v2 Announce Type: replace-cross Abstract: Actor-level stance detection aims to determine an author expressed position toward specific geopolitical actors mentioned or implicated in a text.
arXiv Computation and Language
Oct 5
Mawqif-XT: An Arabic Benchmark Dataset for Cross-Target Stance Detection
The paper introduces Mawqif-XT, a new Arabic benchmark dataset comprising 996 manually annotated tweets from three public targets: Women Driving, E-Cars, and Trimester System. Each tweet is labeled for stance, sentiment, and sarcasm following the Mawqif annotation scheme, and the dataset is intended as a held‑out evaluation set to test cross‑target generalization. Baseline results are provided using Arabic and multilingual transformer models as well as zero‑shot large language models, enabling reproducible evaluation alongside the original Mawqif dataset.