  Zipf's law defines an inverse proportion between a word's ranking in a given
corpus and its frequency in it, roughly dividing the vocabulary into frequent
words and infrequent ones. Here, we stipulate that within a domain an author's
signature can be derived from, in loose terms, the author's missing popular
words and frequently used infrequent-words. We devise a method, termed Latent
Personal Analysis (LPA), for finding domain-based attributes for entities in a
domain: their distance from the domain and their signature, which determines
how they most differ from a domain. We identify the most suitable distance
metric for the method among several and construct the distances and personal
signatures for authors, the domain's entities. The signature consists of both
over-used terms (compared to the average), and missing popular terms. We
validate the correctness and power of the signatures in identifying users and
set existence conditions. We then show uses for the method in explainable
authorship attribution: we define algorithms that utilize LPA to identify two
types of impersonation in social media: (1) authors with sockpuppets (multiple)
accounts; (2) front users accounts, operated by several authors. We validate
the algorithms and employ them over a large scale dataset obtained from a
social media site with over 4000 users. We corroborate these results using
temporal rate analysis. LPA can further be used to devise personal attributes
in a wide range of scientific domains in which the constituents have a
long-tail distribution of elements.
