  Parody is a figurative device used to imitate an entity for comedic or
critical purposes and represents a widespread phenomenon in social media
through many popular parody accounts. In this paper, we present the first
computational study of parody. We introduce a new publicly available data set
of tweets from real politicians and their corresponding parody accounts. We run
a battery of supervised machine learning models for automatically detecting
parody tweets with an emphasis on robustness by testing on tweets from accounts
unseen in training, across different genders and across countries. Our results
show that political parody tweets can be predicted with an accuracy up to 90%.
Finally, we identify the markers of parody through a linguistic analysis.
Beyond research in linguistics and political communication, accurately and
automatically detecting parody is important to improving fact checking for
journalists and analytics such as sentiment analysis through filtering out
parodical utterances.
