  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 along four
dimensions. First, we provide a new computational definition of deception and
formalize it using probability theory. Second, we break down deception into a
new taxonomy. Third, we analyze the debate on linguistic cues for deception and
supply guidelines for systematic reviews. Fourth, we provide some evidence and
some suggestions for domain-independent deception detection.
