People rely on news to know what is happening around the world and inform
their daily lives. In today's world, when the proliferation of fake news is
rampant, having a large-scale and high-quality source of authentic news
articles with the published category information is valuable to learning
authentic news' Natural Language syntax and semantics. As part of this work, we
present a News Category Dataset that contains around 210k news headlines from
the year 2012 to 2022 obtained from HuffPost, along with useful metadata to
enable various NLP tasks. In this paper, we also produce some novel insights
from the dataset and describe various existing and potential applications of
our dataset.

本论文介绍了一份包含近 21 万篇新闻头条的数据集，收集自 HuffPost，并探讨了该数据集在自然语言处理领域的现有和潜在应用，这对于学习真实新闻的语法和语义至关重要，尤其在当前假新闻泛滥的背景下。

新闻分类数据集

News Category Dataset

Past studies in Sarcasm Detection mostly make use of Twitter datasets
collected using hashtag-based supervision but such datasets are noisy in terms
of labels and language. Furthermore, many tweets are replies to other tweets,
and detecting sarcasm in these requires the availability of contextual tweets.
To overcome the limitations related to noise in Twitter datasets, we curate
News Headlines Dataset from two news websites: TheOnion aims at producing
sarcastic versions of current events, whereas HuffPost publishes real news. The
dataset contains about 28K headlines out of which 13K are sarcastic. To make it
more useful, we have included the source links of the news articles so that
more data can be extracted as needed. In this paper, we describe various
details about the dataset and potential use cases apart from Sarcasm Detection.

本文介绍了使用 TheOnion 和 HuffPost 两个新闻网站的头条新闻构建的包含两万八千条标题的数据集，其中包含了 13K 个讽刺标题，以辅助对 Twitter 数据噪声问题进行的讽刺检测研究，并探讨了数据集的潜在用途。