Region Based Instance Document (RID) Approach Using Compression Features for Authorship Attribution

Authorship attribution is concerned with identifying authors of disputed or anonymous documents, which are potentially conspicuous in legal, criminal/civil cases, threatening letters and terroristic communications also in computer forensics. There are two basic approaches for authorship attribution...

Full description

Saved in:
Bibliographic Details
Published in:Annals of data science 2018-09, Vol.5 (3), p.437-451
Main Authors: Raju, N. V. Ganapathi, Chinta, Someswara Rao
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Authoring
Authorship
Business and Management
Compression tests
Crime
Economics
Finance
Forensic computing
Insurance
Management
Similarity
Statistics for Business
Threats
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Authorship attribution is concerned with identifying authors of disputed or anonymous documents, which are potentially conspicuous in legal, criminal/civil cases, threatening letters and terroristic communications also in computer forensics. There are two basic approaches for authorship attribution one is instance based (treat each training text individually) and the other is profile based (treat each training text cumulatively). Both of these methods have their own advantages and disadvantages. The present paper proposes a new region based document model for authorship identification, to address the dimensionality problem of instance based approaches and scalability problem of profile based approaches. The proposed model concatenates a set of individual ‘n’ instance documents of the author as a single region based instance document (RID). On the RID compression based similarity distance method is used. The compression based methods requires no pre-processing and easy to apply. This paper uses Gzip compression algorithm with two compression based similarity measures NCD, CDM. The proposed compression model is character based and it can automatically capture easily non word features such as word stems, punctuations etc. The only disadvantage of compression models is complexity is high. The proposed RID approach addresses this issue by reducing the repeated words in the document. The present approach is experimented on English editorial columns. We achieved approximately 98% of accuracy in identifying the author.
ISSN:2198-5804
2198-5812
DOI:10.1007/s40745-018-0145-4