A diverse Multilingual News Headlines Dataset from around the World (2024)

Abstract: 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 [Kaggle](https://www.kaggle.com/datasets/felixludos/babel-briefings) and [HuggingFace](https://huggingface.co/datasets/felixludos/babel-briefings) with accompanying [GitHub](https://github.com/felixludos/babel-briefings) code.

## introduction analyzing news headlines can be an invaluable source of data for a wide variety of natural language processing tasks such as bias detection @xcite , topic classification @xcite , or event tracking @xcite . furthermore, news headlines can provide insights for sociologists and political scientists about how people think about and discuss current events. the coverage and discussion of current events varies significantly across different media outlets worldwide, however, these distinctions may be difficult to integrate in data mining or machine learning systems due to the language barrier. there are relatively few datasets offering extensive, diverse, and multilingual content @xcite . this is especially problematic for natural language processing tasks, which have been shown to exhibit language biases @xcite . we seek to address these limitations with a new dataset called babel briefings, which is an accessible dataset representing a wide variety of languages and cultures. babel briefings provides daily headlines of articles from across the world, originally written in one of 30 languages from 54 locations around the world published between august 2020 and november 2021, for a total of about 4.7 million distinct articles. consequently, our dataset offers a rich source for analyses of world events, cultural narratives, media framing, and more. we make this dataset available on kaggle and huggingface for easy and open access under the cc by-nc-sa 4.0 license foot_0 , as well as providing all code used to collect and process the data on github. ## analysis a particularly interesting type of analysis enabled by this dataset is the longitudinal comparison of how the same news event is reported in different languages and around the world. to illustrate, let's consider a basic example. we begin by clustering individual articles that discuss the same event. then, we analyze the distribution and frequency of articles from different countries over time, focusing on that specific event. to cluster articles that are about the same event, we begin by extracting a bag of words from the article's (english) title where each word is lemmatized as well as removing punctuation and common stopwords (such as "the" or "a"). we use term frequency-inverse document frequency (tf-idf) @xcite to define the relevance r d of each token relative to the other tokens of the articles that occurred on the same day d. r d (w) = tf(w, d) in which the word w occurs, and n is the total number of days in the dataset. using the tf-idf scores for each word, we define a relevance score rd (x) = @xmath0. furthermore, we define a similarity criterion between two articles as the ratio between the sum of all words that occur in both articles weighted by the relevance of each word and the largest relevance score between the two articles. if this ratio is greater than some threshold (= 0.25 in our experiments), we consider the two articles to be in the same group. this means that if the candidate articles have significant overlap between words weighted by how specific those words are to the day. for the top ten articles with the highest relevance scores every day, we identify all articles in the dataset which, based on our similarity criterion are in the same group to form an event cluster. figure 1 presents clusters of such articles, identifying the top tf-idf scores where the clusters are largest-that is, events with the most articles published about them. next, we take a closer look at a few of the largest clusters in figures 2-5. we visualize the event signatures, which show how the coverage of the same event varies across different languages by how many articles are published every day. for each of the four examples, the plot shows a streamplot breaking down how many articles were published figure 2 : articles reporting on riots in washington dc on 6 january 2021. note how the event is reported in many different languages, but the majority of articles are in english. additionally, there are several subsequent smaller spikes corresponding to related events, such as the beginning of the formal investigation into the riots. figure 3 : articles reporting on diego maradona's death on 25 november 2020 (and his declining health in the weeks before). note how in after a few weeks only spanish articles about the topic continue to appear, underscoring the relative importance of the event in spanishspeaking countries. for each of the top ten most common languages for the event as well as the most frequent tokens occurring that cluster in the top right. one interesting result from this precursory analysis is a distinct qualitative difference in the event signatures of "expected" events (such as in figure 4 ) compared to "unexpected" events (such as in figures 2 and 5). for expected events, there is a clear lead-up to the event, with a peak on the day of the event, and a sharp drop-off afterwards. meanwhile, unexpected events show a sudden spike in coverage, followed by a gradual decline over time. this provides a demonstration of the types of analyses that can be conducted with this dataset, offering insights into the diversity and scope of global news coverage. ## conclusion in this paper, we introduce a dataset of news headlines from around the world called babel brief-ings. the dataset can readily be used for a wide variety of both supervised and unsupervised natural language processing tasks. for example, the included category, location, and language labels can directly be used for article categorization, location classification, or language detection. however, the dataset also enables more nuanced analyses of global news coverage, such as tracking the evolution of events over time, comparing the coverage of events across different countries and languages, or identifying cultural biases in reporting. despite the breadth across languages and time, our dataset is limited to the headlines and short descriptions of news articles. however, urls to the full articles are included, and since many outlets are incentivized to make their headlines as informative as possible, headlines alone are already a rich source of information for many purposes. additionally, the dataset is limited to 54 locations, which is a significant improvement over existing datasets which are often limited to a single country or outlet. there are some minor issues with the news api, for example that for each location only a single language is represented. we aim to mitigate this issue by collecting headlines directly from the rss feeds of individual outlets from around the world. however, this may come at the cost of consistency across sources around the world. in summary, our dataset is a powerful tool for studying the nuances of global news coverage when breaking beyond the language barrier. it provides a simple yet rich foundation for capturing cultural differences in news reporting, offering invaluable data and insights for researchers in the fields of natural language processing, as well as social sciences like media studies or international relations.