The nuances of languages, as well as the varying degrees of truth observed in news items, make fake news detection a difficult problem to solve. A news item is never launched without a purpose, therefore in order to understand its motivation it is best to analyze the relations between the speaker and its subject, as well as different credibility metrics. Inferring details about the various actors involved in a news item is a problem that requires a hybrid approach that mixes machine learning, semantics and natural language processing. This article discusses a semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments are focused on short texts with different degrees of truth and show that adding semantic features improves accuracy significantly.

语言的细微差别以及新闻中观察到的不同程度的真实性，使伪造新闻检测成为一个难以解决的问题。新闻永远不会没有目的而发布，因此，为了了解新闻动机，最好分析发言人与主题之间的关系以及不同的可信度指标。推断新闻中涉及的各个参与者的详细信息是一个问题，需要混合使用机器学习，语义和自然语言处理的混合方法。本文讨论了一种语义假新闻检测方法，该方法基于关系特征（如情感，实体或直接从文本中提取的事实）构建。我们的实验集中于具有不同真实度的短文本，并表明添加语义特征可以显着提高准确性。