  Fake news and deceptive machine-generated text are serious problems
threatening modern societies, including in the Arab world. This motivates work
on detecting false and manipulated stories online. However, a bottleneck for
this research is lack of sufficient data to train detection models. We present
a novel method for automatically generating Arabic manipulated (and potentially
fake) news stories. Our method is simple and only depends on availability of
true stories, which are abundant online, and a part of speech tagger (POS). To
facilitate future work, we dispense with both of these requirements altogether
by providing AraNews, a novel and large POS-tagged news dataset that can be
used off-the-shelf. Using stories generated based on AraNews, we carry out a
human annotation study that casts light on the effects of machine manipulation
on text veracity. The study also measures human ability to detect Arabic
machine manipulated text generated by our method. Finally, we develop the first
models for detecting manipulated Arabic news and achieve state-of-the-art
results on Arabic fake news detection (macro F1=70.06). Our models and data are
publicly available.
