The profusion of user generated content caused by the rise of social media
platforms has enabled a surge in research relating to fields such as
information retrieval, recommender systems, data mining and machine learning.
However, the lack of comprehensive baseline data sets to allow a thorough
evaluative comparison has become an important issue. In this paper we present a
large data set of news items from well-known aggregators such as Google News
and Yahoo! News, and their respective social feedback on multiple platforms:
Facebook, Google+ and LinkedIn. The data collected relates to a period of 8
months, between November 2015 and July 2016, accounting for about 100,000 news
items on four different topics: economy, microsoft, obama and palestine. This
data set is tailored for evaluative comparisons in predictive analytics tasks,
although allowing for tasks in other research areas such as topic detection and
tracking, sentiment analysis in short text, first story detection or news
recommendation.

本文介绍了一个大型数据集，旨在为预测分析任务提供评估比较的基准数据，并涵盖了新闻内容、社交反馈等信息以及经济、微软、奥巴马和巴勒斯坦等四个主题。