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 \emph{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
\href{this https URL}{Kaggle} and
\href{this https URL}{HuggingFace}
with accompanying \href{this https URL}{GitHub}
code.

Babel Briefings 是一个新颖的数据集，包含了 2020 年 8 月至 2021 年 11 月的 470 万条新闻标题，涵盖了 30 种语言和 54 个地点，其中包括所有文章的英文翻译。该数据集适用于自然语言处理和媒体研究，是用于训练或评估语言模型的高质量数据集，同时也提供了一个简单易用的文章集合，用于分析全球新闻报道和文化叙述。通过使用基于 TF-IDF 加权相似度度量的基本过程将文章分组为关于同一事件的聚类，我们展示了该数据集所支持的分析功能，并可视化事件的 “事件签名”，展示随时间推移出现的语言和事件的意外程度。该数据集可在 Kaggle 和 HuggingFace 上获取，并附带 GitHub 代码。