  Coronavirus disease (COVID-19) is an infectious respiratory disease that was
first discovered in late December 2019, in Wuhan, China, and then spread
worldwide causing a lot of panic and death. Users of social networking sites
such as Facebook and Twitter have been focused on reading, publishing, and
sharing novelties, tweets, and articles regarding the newly emerging pandemic.
A lot of these users often employ sarcasm to convey their intended meaning in a
humorous, funny, and indirect way making it hard for computer-based
applications to automatically understand and identify their goal and the harm
level that they can inflect. Motivated by the emerging need for annotated
datasets that tackle these kinds of problems in the context of COVID-19, this
paper builds and releases AraCOVID19-SSD a manually annotated Arabic COVID-19
sarcasm and sentiment detection dataset containing 5,162 tweets. To confirm the
practical utility of the built dataset, it has been carefully analyzed and
tested using several classification models.
