  Internet-based economies and societies are drowning in deceptive attacks.
These attacks take many forms, such as fake news, phishing, and job scams,
which we call ``domains of deception.'' Machine-learning and
natural-language-processing researchers have been attempting to ameliorate this
precarious situation by designing domain-specific detectors. Only a few recent
works have considered domain-independent deception. We collect these disparate
threads of research and investigate domain-independent deception. First, we
provide a new computational definition of deception and break down deception
into a new taxonomy. Then, we analyze the debate on linguistic cues for
deception and supply guidelines for systematic reviews. Finally, we investigate
common linguistic features and give evidence for knowledge transfer across
different forms of deception.
