发表机构
Universidade Federal de Ouro Preto; National Research Council (CNR); University of Pisa（欧鲁普雷图联邦大学; 国家研究委员会（CNR）; 比萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用语言模型分析巴西YouTube上十年间葡萄牙语评论的气候立场，提出基于Llama 3.1的自训练立场检测方法，发现否认者评论虽少但引发更多跨立场争辩，而支持共识话语在同类讨论中更受强化。
AI 中文摘要
在线平台已成为公众就气候变化进行争论的舞台，塑造了科学知识、否认和不确定性如何被表达和争辩。然而，针对YouTube的纵向证据仍然有限，尤其是葡萄牙语话语。为弥补这一空白，我们通过巴西相关的气候搜索检索了大量葡萄牙语YouTube评论，刻画了气候立场随时间如何被表达和争辩。为在嘈杂、不平衡且资源匮乏的环境中支持这一分析，我们收集了2014年至2024年间发布的超过240,000条评论，并将立场检测表述为一个三分类任务（相信者、否认者和不确定者）。我们通过基于Llama 3.1的可扩展自训练流程来实现立场归因，使用低秩适应（LoRA）和混合实例选择，用高置信度的伪标记示例扩展训练集，同时保持类别多样性。该方法改善了对少数类和修辞复杂类的覆盖率和类别平衡，使得无需大量人工标注即可进行大规模立场归因。我们的结果表明，极化以交互不对称性为特征：否认主义评论较少见，但与之相关的跨立场争辩比例相对较高，而支持共识的话语在立场同质的讨论串中得到更强化的支持。
英文摘要
Online platforms have become arenas for the public contestation of climate change, shaping how scientific knowledge, denial, and uncertainty are expressed and disputed. Yet longitudinal evidence remains limited for YouTube, especially for Portuguese-language discourse. Addressing this gap, we characterize how climate stances are expressed and contested over time in a large corpus of Portuguese-language YouTube comments retrieved through Brazil-oriented climate-related searches. To support this analysis in a noisy, imbalanced, and low-resource setting, we collect more than 240,000 comments posted between 2014 and 2024 and formulate stance detection as a three-way classification task (Believer, Denier, and Inconclusive). We operationalize stance attribution through a scalable self-training pipeline based on Llama 3.1, using Low-Rank Adaptation (LoRA) and hybrid instance selection to expand the training set with high-confidence pseudo-labeled examples while preserving class diversity. This approach improves coverage and class balance for minority and rhetorically complex classes, enabling large-scale stance attribution without extensive manual annotation. Our results show that polarisation is marked by interactional asymmetries: denialist comments are less prevalent, but they are associated with a comparatively higher share of cross-stance contestation, while pro-consensus discourse is more strongly reinforced within stance-homogeneous threads.
CommentsAccepted at ASONAM 2026
