Stance detection is an important task for many applications that analyse or support online political discussions. Common approaches include fine-tuning transformer based models. However, these models require a large amount of labelled data, which might not be available. In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model. Second, we propose a new active learning method called SQBC based on the "Query-by-Comittee" approach. The key idea is to use LLM-generated synthetic data as an oracle to identify the most informative unlabelled samples, that are selected for manual labelling. Comprehensive experiments show that both ideas can improve the stance detection performance. Curiously, we observed that fine-tuning on actively selected samples can exceed the performance of using the full dataset.
Understanding and Mitigating Political Stance Cross-topic Generalization in Large Language Models
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明负责任的人工智能和数据分析实验室）；King Abdullah University of Science and Technology (KAUST)（卡布斯科学技术大学）；University of Copenhagen（哥本哈根大学）；University of Macau（澳门大学）
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）
How Identity and Opinion Shape Political Sycophancy in LLMs
National Yang Ming Chiao Tung University（国立阳明交通大学）；Academia Sinica（中央研究院）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）
University of Michigan（密歇根大学）；Northeastern University（东北大学）
Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology predict…
Department of Linguistics, University of British Columbia（不列颠哥伦比亚大学语言学系）；Department of Political Science, University of Toronto Scarborough（多伦多大学斯卡伯勒分校政治学系）；Vector Institute, Department of Computer Science, University of Toronto（向量研究所、多伦多大学计算机科学系）
Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMs
Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）；Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气与计算机学院）；University of Manchester（曼彻斯特大学）
Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions
Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore, Indore 453552, India（计算机科学与工程系，印度理工学院（IIT）印多尔，印多尔453552，印度）
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）
Contextualized Prompting For Stance Detection On Social Media
Institute of Intensive Care, University Hospital of Zurich and University of Zurich（重症医学研究所，苏黎世大学医院和苏黎世大学）；Institute of Computer Science, University of Goettingen（计算机科学研究所，哥廷根大学）；GESIS Leibniz Institute for the Social Sciences（社会科学研究莱比锡研究所）；Institut für Publizistik, Johannes Gutenberg-University Mainz（主笔研究所，美因茨约翰· Gutenberg 大学）；Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt（无处不在知识处理实验室，达姆施塔特技术大学）
We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We then compare these language-based metrics to existing social scientific measures of the ideology of the Supreme Court and the public. Through this cross-disciplinary analysis, we find that justices who are more responsive to public opinion tend to express their ideology during oral …
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Microsoft Research（微软研究院）
Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief hold…
Multilingual Political Views of Large Language Models: Identification and Steering
Saarland University（萨尔兰州大学）；University of Trento（特伦特大学）；Brno University of Technology（布拉格技术大学）；Kempelen Institute of Intelligent Technologies（凯姆佩尔智能技术研究所）；German Research Center for AI (DFKI)（人工智能德国研究中心（DFKI））
Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division
Department of Methodology and Statistics（方法学与统计学系）
Università degli Studi di Torino（都灵大学）；Universitat Politécnica de Valéncia（瓦伦西亚理工大学）
Stance detection, the task of identifying the speaker's opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task6 on Detecting stance in tw…
TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）；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）
