Linguistic identifiers of L1 Persian speakers writing in English:NLID for authorship analysis

This research focuses on Native Language Identification (NLID), and in particular, on the linguistic identifiers of L1 Persian speakers writing in English. This project comprises three sub-studies; the first study devises a coding system to account for interlingual features present in a corpus of L1 Persian speakers blogging in English, and a corpus of L1 English blogs. Study One then demonstrates that it is possible to use interlingual identifiers to distinguish authorship by L1 Persian speakers. Study Two examines the coding system in relation to the L1 Persian corpus and a corpus of L1 Azeri and L1 Pashto speakers. The findings of this section indicate that the NLID method and features designed are able to discriminate between L1 influences from different languages. Study Three focuses on elicited data, in which participants were tasked with disguising their language to appear as L1 Persian speakers writing in English. This study indicated that there was a significant difference between the features in the L1 Persian corpus, and the corpus of disguise texts. The findings of this research indicate that NLID and the coding system devised have a very strong potential to aid forensic authorship analysis in investigative situations. Unlike existing research, this project focuses predominantly on blogs, as opposed to student data, making the findings more appropriate to forensic casework data.

Theme and thematic patterns in Spanish and English history texts

The aim of this research project is to compare published history textbooks written for upper-secondary/tertiary study in the U.S. and Spain using Halliday's (1994) Theme/Rheme construct. The motivation for using the Theme/Rheme construct to analyze professional texts in the two languages is two-fold. First of all, while there exists a multitude of studies at the grammatical and phonological levels between the two languages, very little analysis has been carried out in comparison at the level of text, beyond that of comparing L1/L2 student writing. Secondly, thematic considerations allow the analyst to highlight areas of textual organization in a systematic way for purposes of comparison. The basic hypothesis tested here rests on the premise that similarity in the social function of the texts results in similar Theme choice and thematic patterning across languages, barring certain linguistic constraints. The corpus for this study consists of 20 texts: 10 from various history textbooks published in the U.S. and 10 from various history textbooks published in Spain. The texts chosen represent a variety of authors, in order to control for author style or preference. Three overall areas of analysis were carried out, representing Halliday's (1994) three metafunctions: the ideational, the interpersonal and the textual. The ideational analysis shows similarities across the two corpora in terms of participant roles and circumstances as Theme, with a slight difference in participants involved in material processes, which is shown to reflect a minor difference in the construal of the field of history in the two cultures. The textual analysis shows overall similarities with respect to text organization, and the interpersonal analysis shows overall similarities as regards the downplay of discrepant interpretations of historical events as well as a low frequency of interactive textual features, manifesting the informational focus of the texts. At the same time, differences in results amongst texts within each of the corpora demonstrate possible effect of subject matter, in many cases, and individual author style in others. Overall, the results confirm that similarity in content, but above all in purpose and audience, result in texts which show similarities in textual features, setting aside certain grammatical constraints.

Quantitative authorship attribution:an evaluation of techniques

The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author's writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of these measurements been tested on the same dataset. A large-scale comparison of textual measurements is crucial if current techniques are to be used effectively and if new and more powerful techniques are to be developed. This article presents the results of a comparison of thirty-nine different types of textual measurements commonly used in attribution studies, in order to determine which are the best indicators of authorship. Based on the results of these tests, a more accurate approach to quantitative authorship attribution is proposed, which involves the analysis of many different textual measurements.

Native language identification (NLID) for forensic authorship analysis of weblogs

This chapter introduces Native Language Identification (NLID) and considers the casework applications with regard to authorship analysis of online material. It presents findings from research identifying which linguistic features were the best indicators of native (L1) Persian speakers blogging in English, and analyses how these features cope at distinguishing between native influences from languages that are linguistically and culturally related. The first chapter section outlines the area of Native Language Identification, and demonstrates its potential for application through a discussion of relevant case history. The next section discusses a development of methodology for identifying influence from L1 Persian in an anonymous blog author, and presents findings. The third part discusses the application of these features to casework situations as well as how the features identified can form an easily applicable model and demonstrates the application of this to casework. The research presented in this chapter can be considered a case study for the wider potential application of NLID.