Native language influence detection for forensic authorship analysis: Identifying L1 persian bloggers

Research output: Contribution to journalArticle

Abstract

This article demonstrates and examines the potential use of interlingual identifiers for forensic authorship analysis and native language influence detection (NLID). The work focuses on the practical applications of native language (L1) identifiers by a human analyst in investigative situations. Using naturally occurring blog posts where the writer self-identifies as a native Persian speaker, a human analyst derived and coded sets of non-native features. Two logistic regression models were built: the first was used to select features to distinguish L1 Persian speakers from L1 English speakers in their English writings, the second developed a feature list to contrast L1 languages that are geographically and linguistically close to Persian. The results clearly demonstrate that interlingual identifiers have the potential to aid in determining the L1 of an anonymous author and can be used by a human analyst in a short forensically realistic example text. This article demonstrates that NLID is possible beyond the more common computational approaches and can form a useful tool in the forensic linguist’s toolbox. This study is not a statistical validation study; instead it demonstrates how a sociolinguistic approach can complement more traditional computational approaches.

LanguageEnglish
Pages1-20
Number of pages20
JournalInternational Journal of Speech, Language and the Law
Volume25
Issue number1
DOIs
Publication statusPublished - 10 Sep 2018

Fingerprint

language
sociolinguistics
weblog
logistics
writer
regression

Bibliographical note

©2018, equinox publishing

Keywords

  • Authorship analysis
  • Linguistic profiling
  • Native language identification
  • Native language influence detection
  • Persian

Cite this

@article{119324c5576f4e0284070ee63353a054,
title = "Native language influence detection for forensic authorship analysis: Identifying L1 persian bloggers",
abstract = "This article demonstrates and examines the potential use of interlingual identifiers for forensic authorship analysis and native language influence detection (NLID). The work focuses on the practical applications of native language (L1) identifiers by a human analyst in investigative situations. Using naturally occurring blog posts where the writer self-identifies as a native Persian speaker, a human analyst derived and coded sets of non-native features. Two logistic regression models were built: the first was used to select features to distinguish L1 Persian speakers from L1 English speakers in their English writings, the second developed a feature list to contrast L1 languages that are geographically and linguistically close to Persian. The results clearly demonstrate that interlingual identifiers have the potential to aid in determining the L1 of an anonymous author and can be used by a human analyst in a short forensically realistic example text. This article demonstrates that NLID is possible beyond the more common computational approaches and can form a useful tool in the forensic linguist’s toolbox. This study is not a statistical validation study; instead it demonstrates how a sociolinguistic approach can complement more traditional computational approaches.",
keywords = "Authorship analysis, Linguistic profiling, Native language identification, Native language influence detection, Persian",
author = "Ria Perkins and Tim Grant",
note = "{\circledC}2018, equinox publishing",
year = "2018",
month = "9",
day = "10",
doi = "10.1558/ijsll.30844",
language = "English",
volume = "25",
pages = "1--20",
journal = "International Journal of Speech, Language and the Law",
issn = "1748-8885",
publisher = "Equinox Publishing Ltd",
number = "1",

}

TY - JOUR

T1 - Native language influence detection for forensic authorship analysis

T2 - International Journal of Speech, Language and the Law

AU - Perkins, Ria

AU - Grant, Tim

N1 - ©2018, equinox publishing

PY - 2018/9/10

Y1 - 2018/9/10

N2 - This article demonstrates and examines the potential use of interlingual identifiers for forensic authorship analysis and native language influence detection (NLID). The work focuses on the practical applications of native language (L1) identifiers by a human analyst in investigative situations. Using naturally occurring blog posts where the writer self-identifies as a native Persian speaker, a human analyst derived and coded sets of non-native features. Two logistic regression models were built: the first was used to select features to distinguish L1 Persian speakers from L1 English speakers in their English writings, the second developed a feature list to contrast L1 languages that are geographically and linguistically close to Persian. The results clearly demonstrate that interlingual identifiers have the potential to aid in determining the L1 of an anonymous author and can be used by a human analyst in a short forensically realistic example text. This article demonstrates that NLID is possible beyond the more common computational approaches and can form a useful tool in the forensic linguist’s toolbox. This study is not a statistical validation study; instead it demonstrates how a sociolinguistic approach can complement more traditional computational approaches.

AB - This article demonstrates and examines the potential use of interlingual identifiers for forensic authorship analysis and native language influence detection (NLID). The work focuses on the practical applications of native language (L1) identifiers by a human analyst in investigative situations. Using naturally occurring blog posts where the writer self-identifies as a native Persian speaker, a human analyst derived and coded sets of non-native features. Two logistic regression models were built: the first was used to select features to distinguish L1 Persian speakers from L1 English speakers in their English writings, the second developed a feature list to contrast L1 languages that are geographically and linguistically close to Persian. The results clearly demonstrate that interlingual identifiers have the potential to aid in determining the L1 of an anonymous author and can be used by a human analyst in a short forensically realistic example text. This article demonstrates that NLID is possible beyond the more common computational approaches and can form a useful tool in the forensic linguist’s toolbox. This study is not a statistical validation study; instead it demonstrates how a sociolinguistic approach can complement more traditional computational approaches.

KW - Authorship analysis

KW - Linguistic profiling

KW - Native language identification

KW - Native language influence detection

KW - Persian

UR - http://www.scopus.com/inward/record.url?scp=85053307409&partnerID=8YFLogxK

U2 - 10.1558/ijsll.30844

DO - 10.1558/ijsll.30844

M3 - Article

VL - 25

SP - 1

EP - 20

JO - International Journal of Speech, Language and the Law

JF - International Journal of Speech, Language and the Law

SN - 1748-8885

IS - 1

ER -