# A Multilingual Similarity Dataset for News Article Frame

arXiv: https://arxiv.org/abs/2405.13272 · HTML: https://mlsanity.com/paper/2405.13272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 4 of 4)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [Crosslingual Document Embedding as Reduced-Rank Ridge Regression](https://mlsanity.com/paper/1904.03922/) (2019)
- [Riveter: Measuring Power and Social Dynamics Between Entities](https://mlsanity.com/paper/2312.09536/) (2023)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings](https://mlsanity.com/paper/2506.00277/) (2025)
- [CrossNews-UA: A Cross-lingual News Semantic Similarity Benchmark for Ukrainian, Polish, Russian, and English](https://mlsanity.com/paper/2510.19628/) (2025)

## Abstract (arXiv)

Understanding the writing frame of news articles is vital for addressing social issues, and thus has attracted notable attention in the fields of communication studies. Yet, assessing such news article frames remains a challenge due to the absence of a concrete and unified standard dataset that considers the comprehensive nuances within news content. To address this gap, we introduce an extended version of a large labeled news article dataset with 16,687 new labeled pairs. Leveraging the pairwise comparison of news articles, our method frees the work of manual identification of frame classes in traditional news frame analysis studies. Overall we introduce the most extensive cross-lingual news article similarity dataset available to date with 26,555 labeled news article pairs across 10 languages. Each data point has been meticulously annotated according to a codebook detailing eight critical aspects of news content, under a human-in-the-loop framework. Application examples demonstrate its potential in unearthing country communities within global news coverage, exposing media bias among news outlets, and quantifying the factors related to news creation. We envision that this news similarity dataset will broaden our understanding of the media ecosystem in terms of news coverage of events and perspectives across countries, locations, languages, and other social constructs. By doing so, it can catalyze advancements in social science research and applied methodologies, thereby exerting a profound impact on our society.