Social media has become a popular means for people to consume news.
Meanwhile, it also enables the wide dissemination of fake news, i.e., news with
intentionally false information, which brings significant negative effects to
the society. Thus, fake news detection is attracting increasing attention.
However, fake news detection is a non-trivial task, which requires multi-source
information such as news content, social context, and dynamic information.
First, fake news is written to fool people, which makes it difficult to detect
fake news simply based on news contents. In addition to news contents, we need
to explore social contexts such as user engagements and social behaviors. For
example, a credible user's comment that "this is a fake news" is a strong
signal for detecting fake news. Second, dynamic information such as how fake
news and true news propagate and how users' opinions toward news pieces are
very important for extracting useful patterns for (early) fake news detection
and intervention. Thus, comprehensive datasets which contain news content,
social context, and dynamic information could facilitate fake news propagation,
detection, and mitigation; while to the best of our knowledge, existing
datasets only contains one or two aspects. Therefore, in this paper, to
facilitate fake news related researches, we provide a fake news data repository
FakeNewsNet, which contains two comprehensive datasets that includes news
content, social context, and dynamic information. We present a comprehensive
description of datasets collection, demonstrate an exploratory analysis of this
data repository from different perspectives, and discuss the benefits of
FakeNewsNet for potential applications on fake news study on social media.

通过提供包含新闻内容、社会背景和动态信息的数据集，本文旨在促进对社交媒体上假新闻的传播、检测和干预的研究，强调了假新闻检测的复杂性，并结合多源信息提出了可能的解决方案。

FakeNewsNet: 一个数据仓库，提供新闻内容、社交背景和时空信息，用于研究社交媒体上的虚假新闻

FakeNewsNet: A Data Repository with News Content, Social Context and  Spatialtemporal Information for Studying Fake News on Social Media

In a previous work, it was shown that there is a curious problem with the
benchmark ColorChecker dataset for illuminant estimation. To wit, this dataset
has at least 3 different sets of ground-truths. Typically, for a single
algorithm a single ground-truth is used. But then different algorithms, whose
performance is measured with respect to different ground-truths, are compared
against each other and then ranked. This makes no sense. We show in this paper
that there are also errors in how each ground-truth set was calculated. As a
result, all performance rankings based on the ColorChecker dataset - and there
are scores of these - are inaccurate.
In this paper, we re-generate a new 'recommended' set of ground-truth based
on the calculation methodology described by Shi and Funt. We then review the
performance evaluation of a range of illuminant estimation algorithms. Compared
with the legacy ground-truths, we find that the difference in how algorithms
perform can be large, with many local rankings of algorithms being reversed.
Finally, we draw the readers attention to our new 'open' data repository
which, we hope, will allow the ColorChecker set to be rehabilitated and once
again to become a useful benchmark for illuminant estimation algorithms.

本文针对 ColorChecker 数据集中不同的 ground-truth 数据集，重新生成了一个基于 Shi 和 Funt 描述的推荐 ground-truth，对多种光源估计算法进行了性能评估并发现了许多本地算法排名的差别。此外，我们还提供了一个开放式的数据存储库，希望 ColorChecker 数据集能够再次成为估计光源算法的一个有用基准。