In this modern era, communication has become faster and easier. This means
fallacious information can spread as fast as reality. Considering the damage
that fake news kindles on the psychology of people and the fact that such news
proliferates faster than truth, we need to study the phenomenon that helps
spread fake news. An unbiased data set that depends on reality for rating news
is necessary to construct predictive models for its classification. This paper
describes the methodology to create such a data set. We collect our data from
snopes.com which is a fact-checking organization. Furthermore, we intend to
create this data set not only for classification of the news but also to find
patterns that reason the intent behind misinformation. We also formally define
an Internet Claim, its credibility, and the sentiment behind such a claim. We
try to realize the relationship between the sentiment of a claim with its
credibility. This relationship pours light on the bigger picture behind the
propagation of misinformation. We pave the way for further research based on
the methodology described in this paper to create the data set and usage of
predictive modeling along with research-based on psychology/mentality of people
to understand why fake news spreads much faster than reality.

本文介绍了利用 snopes.com 收集数据并构建数据集，帮助理解所谓 “假新闻” 传播背后的机制，我们还形式化定义了网络主张以及其可信度和情感，并探讨了情感与可信度之间的关系。