Domain-based Latent Personal Analysis and its use for impersonation detection in social media

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 to frequent (popular) 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 such domain-based personal signatures. LPA determines what words most contributed to the distance between a user's vocabulary from the domain's. We identify the most suitable distance metric for the method among several and construct a personal signature for authors. We validate the correctness and power of the signatures in identifying authors and utilize LPA to identify two types of impersonation in social media: (1) authors with sockpuppets (multiple) accounts; (2) front-user 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 accounts, and corroborate the results employing temporal rate analysis. LPA can be used to devise personal signatures in a wide range of scientific domains in which the constituents have a long-tail distribution of elements.