This study investigates the use of Large Language Models (LLMs) for political stance detection in informal online discourse, where language is often sarcastic, ambiguous, and context-dependent. We explore whether providing contextual information, specifically user profile summaries derived from historical posts, can improve classification accuracy. Using a real-world political forum dataset, we generate structured profiles that summarize users' ideological leaning, recurring topics, and linguistic patterns. We evaluate seven state-of-the-art LLMs across baseline and context-enriched setups through a comprehensive cross-model evaluation. Our findings show that contextual prompts significantly boost accuracy, with improvements ranging from +17.5\% to +38.5\%, achieving up to 74\% accuracy that surpasses previous approaches. We also analyze how profile size and post selection strategies affect performance, showing that strategically chosen political content yields better results than larger, randomly selected contexts. These findings underscore the value of incorporating user-level context to enhance LLM performance in nuanced political classification tasks.
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse
MaiNLP & MCML, LMU Munich, Germany（LMU慕尼黑媒体与传播系）；Department of Media and Communication, LMU Munich, Germany（LMU慕尼黑媒体与传播系）；National Research Council Canada, Ottawa, Canada（加拿大国家研究委员会）
Cross-target Stance Detection by Exploiting Target Analytical Perspectives
Shenzhen University（深圳大学）；Shenzhen X-Institute（深圳X-Institute）；Shenzhen Technology University（深圳技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；Aoyama Gakuin University（青山学院大学）；South China Agricultural University（华南农业大学）
Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments
Department of Business and Technology Management, Islamic University of Technology, Gazipur, Dhaka, Bangladesh（伊斯兰科技大学商学院与技术管理系，加兹ipur，达卡，孟加拉国）；Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Dhaka, Bangladesh（伊斯兰科技大学计算机科学与工程系，加兹ipur，达卡，孟加拉国）；Department of Electrical and Electronic Engineering, University of Dhaka, Nilkhet Road, Dhaka, Bangladesh（达卡大学电子与电气工程系，Nilkhet Road，达卡，孟加拉国）；Department of Computer Science, American International University–Bangladesh, Kuratoli, Dhaka, Bangladesh（孟加拉国美国国际大学计算机科学系，Kuratoli，达卡，孟加拉国）
Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction
Department of Methodology and Statistics, Utrecht University（方法论与统计学系，乌特雷赫特大学）；Department of Languages, Literature and Communication, Utrecht University（语言、文学与传播系，乌特雷赫特大学）
Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics. In this paper, in order to train a system independent from topics, we propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. The extraction relies on specific discourse relation information, which is shown as a reliable and accurate source for providing stance information. We also propose a 3-stage training framework where the noisy …
University of Warwick（华威大学）；Alan Turing Institute（艾伦·图灵研究所）；University of Sheffield（谢菲尔德大学）；University of Melbourne（墨尔本大学）；University of Copenhagen（哥本哈根大学）
Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or 'conversational threads'. Testing the effectiveness of four sequential classifiers -- Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF)…
University of Melbourne（墨尔本大学）；Curtin University（科廷大学）
Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax t…
A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）；School of Urban Planning and Design, Peking University（北京大学城市规划与设计学院）
Human Language Technology Research Institute, University of Texas at Dallas（德克萨斯大学达拉斯分校人类语言技术研究所）
Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel…
Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion - we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent s…
ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of Advanced Intelligence and Computing, A*STAR（新加坡科研局先进智能与计算研究所）
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media
Centro Singular de Investigación en Tecnoloxías Intelixentes da USC（乌拉特大学智能技术研究中心）；IRLab, CITIC Research Centre, Universidade da Coruña（IR实验室，CITIC研究中心，科鲁纳大学）；Universidade de Évora（埃夫拉大学）；Universidad de La Rioja（里瓦达维亚大学）；GESIS Leibniz Institute for the Social Sciences（莱比锡社会科学院）
