Author Profiling and Related Applications
Michael Oakes · 2019
Author profiling is the analysis of people’s writing in an attempt to find out which classes they belong to, such as gender, age group or native language. Many of the techniques for author profiling are derived from the related task of Author Identification, so we will look at this topic first. Author identification is the task of finding out who is most likely to have written a disputed document, and there are a number of computational approaches to this. The three main subtasks are the compilation of corpora of texts known to be written by the candidate authors, the selection of linguistic features to represent those texts, and statistics for discriminating between those features which are most indicative of a particular author’s writing style. Plagiarism is the unacknowledged use of another author’s original work, and we will look at software for its detection. The chapter will cover the types of text obfuscation strategies used by plagiarists, commercial plagiarism detection software and its shortcomings, and recent research systems. Strategies have been developed for both external plagiarism detection (where the original source is searched for in a large document collection) and intrinsic plagiarism detection (where the source text is not available, necessitating a search for inconsistencies within the suspicious document). The specific problems of plagiarism by translation of an original in another language, and the unauthorized copying of sections of computer code, are described. Evaluation forums and publicly available test data sets are covered for each of the main topics of this chapter.