# A diverse Multilingual News Headlines Dataset from around the World

arXiv: https://arxiv.org/abs/2403.19352 · HTML: https://mlsanity.com/paper/2403.19352/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [News Category Dataset](https://mlsanity.com/paper/2209.11429/) (2022)

## Built on by (most cited first, 6 of 6)

- [JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks](https://mlsanity.com/paper/2603.01291/) (2026)
- [How Social is It? A Benchmark for LLMs' Capabilities in Multi-user Multi-turn Social Agent Tasks](https://mlsanity.com/paper/2505.04628/) (2025)
- [The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2512.22337/) (2025)
- [The 2021 Tokyo Olympics Multilingual News Article Dataset](https://mlsanity.com/paper/2502.06648/) (2025)
- [A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT](https://mlsanity.com/paper/2602.22014/) (2026)
- [AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths](https://mlsanity.com/paper/2609.06771/) (2026)

## Abstract (arXiv)

Babel Briefings is a novel dataset featuring 4.7 million news headlines from August 2020 to November 2021, across 30 languages and 54 locations worldwide with English translations of all articles included. Designed for natural language processing and media studies, it serves as a high-quality dataset for training or evaluating language models as well as offering a simple, accessible collection of articles, for example, to analyze global news coverage and cultural narratives. As a simple demonstration of the analyses facilitated by this dataset, we use a basic procedure using a TF-IDF weighted similarity metric to group articles into clusters about the same event. We then visualize the event signatures of the event showing articles of which languages appear over time, revealing intuitive features based on the proximity of the event and unexpectedness of the event. The dataset is available on hrefhttps://www.kaggle.com/datasets/felixludos/babel-briefingsKaggle and hrefhttps://huggingface.co/datasets/felixludos/babel-briefingsHuggingFace with accompanying hrefhttps://github.com/felixludos/babel-briefingsGitHub code.