Hoaxes are a recognised form of disinformation created deliberately, with
potential serious implications in the credibility of reference knowledge
resources such as Wikipedia. What makes detecting Wikipedia hoaxes hard is that
they often are written according to the official style guidelines. In this
work, we first provide a systematic analysis of the similarities and
discrepancies between legitimate and hoax Wikipedia articles, and introduce
Hoaxpedia, a collection of 311 Hoax articles (from existing literature as well
as official Wikipedia lists) alongside semantically similar real articles. We
report results of binary classification experiments in the task of predicting
whether a Wikipedia article is real or hoax, and analyze several settings as
well as a range of language models. Our results suggest that detecting
deceitful content in Wikipedia based on content alone, despite not having been
explored much in the past, is a promising direction.

此研究通过系统分析真实与骗局维基百科文章之间的相似性和差异，并引入 Hoaxpedia，一个包含 311 篇骗局文章和语义相似的真实文章的集合，通过多种设置和语言模型进行二元分类实验，结果显示，基于内容的检测维基百科中的欺骗性内容是一个有前景的方向。