分析新闻中时间表述如何影响观点塑造，揭示八类时间修辞框架。
Uncovering Temporal Framing in the News
- 基于语义理论构建八类时间修辞框架，通过多语言新闻数据标注验证。
- 在458篇英德新闻中识别超2000句时间修辞句，3000+标注支持模型训练。
- 监督学习模型显著优于零样本方法，适合传播学与自然语言处理研究者。
时间语言不仅标记事件顺序，更在新闻话语中作为修辞手段影响认知与说服。本文研究时间修辞——即利用时间相关语言构建意义而非单纯记录时序的修辞策略。提出基于前人研究的八类时间框架，并通过专家标注构建多语言新闻语料库。该数据集包含458篇英德文新闻，覆盖超过2000句时间修辞句，从2万余句中识别出约3000个时间修辞标注。分析框架分布、共现模式及词汇线索，评估监督微调与零样本分类的表现。实验表明，句子级时间修辞可被有效学习，监督模型显著优于零样本方法。论文公开语料库以支持后续研究：https://mbzuai-nlp.github.io/temporal-framing/。
原文摘要 · Abstract (English)
Temporal language does more than place events on a timeline. In news discourse, references to the past, present, and future can function as rhetorical devices that shape interpretation and persuasion. Here, we study temporal framing, defined as the persuasive use of time-related language to structure meaning rather than to report chronology. We propose a taxonomy of eight temporal frames grounded in prior work on temporality and framing, and we realize it through expert annotation of a multilingual news corpus. The resulting dataset includes 458 English and German news articles, with over 2K temporally framed sentences and approximately 3K temporal framing annotations identified from a corpus of more than 20K sentences. We analyze frame prevalence, co-occurrence patterns, and lexical cues, and evaluate temporal framing detection using supervised fine-tuning and zero-shot classification. Our experiments show that temporal framing is learnable at the sentence level, with supervised models substantially outperforming zero-shot approaches. We publicly release the corpus to support future research on temporal framing: https://mbzuai-nlp.github.io/temporal-framing/.
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