Uncovering Temporal Framing in the News
Quick Answer
This study introduces a taxonomy of eight temporal frames in news discourse, revealing how temporal language shapes interpretation.
Quick Take
This study introduces a taxonomy of eight temporal frames in news discourse, revealing how temporal language shapes interpretation. Analyzed across 458 articles, the research demonstrates that supervised models significantly outperform zero-shot classification in detecting temporal framing, with over 3,000 annotations provided in a publicly released multilingual corpus.
Key Points
- Introduces eight temporal frames that influence news interpretation.
- Analyzed 458 articles, yielding over 3,000 temporal framing annotations.
- Supervised models significantly outperform zero-shot classification.
- Dataset includes English and German news articles for multilingual analysis.
- Corpus publicly available to support future research on temporal framing.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 00294v1 Announce Type: new Abstract: 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/.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
The study evaluates three NLP approaches—Named Entity Recognition, Keyword Extraction, and Topic Modelling—using the Their Finest Hour Online Archive to automate keyword extraction from crowdsourced WWII collections. Findings suggest that while NLP methods show promise, no single approach is sufficient, and ethical considerations in automated keyword extraction are crucial for responsible stewardship.