We study the problem of finding fake online news. This is an important
problem as news of questionable credibility have recently been proliferating in
social media at an alarming scale. As this is an understudied problem,
especially for languages other than English, we first collect and release to
the research community three new balanced credible vs. fake news datasets
derived from four online sources. We then propose a language-independent
approach for automatically distinguishing credible from fake news, based on a
rich feature set. In particular, we use linguistic (n-gram),
credibility-related (capitalization, punctuation, pronoun use, sentiment
polarity), and semantic (embeddings and DBPedia data) features. Our experiments
on three different testsets show that our model can distinguish credible from
fake news with very high accuracy.

本篇论文研究了如何在社交媒体中找出虚假新闻。作者提出了基于语言学、可信度、语义等各种特征来自动识别虚假新闻的方法，并使用三个不同的测试集测试了该方法，在高精度的情况下可以准确地区分真实新闻和虚假新闻。

寻找可信新闻

In Search of Credible News

Commonsense knowledge about object properties, human behavior and general
concepts is crucial for robust AI applications. However, automatic acquisition
of this knowledge is challenging because of sparseness and bias in online
sources. This paper presents Quasimodo, a methodology and tool suite for
distilling commonsense properties from non-standard web sources. We devise
novel ways of tapping into search-engine query logs and QA forums, and
combining the resulting candidate assertions with statistical cues from
encyclopedias, books and image tags in a corroboration step. Unlike prior work
on commonsense knowledge bases, Quasimodo focuses on salient properties that
are typically associated with certain objects or concepts. Extensive
evaluations, including extrinsic use-case studies, show that Quasimodo provides
better coverage than state-of-the-art baselines with comparable quality.

该研究提出了 Quasimodo 方法，通过从非标准网络来源提取出的候选断言，结合百科全书、图书和图像标签等的统计提示，在进行验证步骤时，重点考虑与某些对象或概念通常相关联的显著属性。与先前的常识知识库研究不同，Quasimodo 着重于获得常用的物体或概念属性，评估表明该方法比现有方法提供更好的覆盖率和可比质量。