Date of Degree

9-2016

Document Type

Thesis

Degree Name

M.A.

Program

Linguistics

Advisor(s)

Martin Chodorow

Subject Categories

Computational Linguistics

Keywords

Computational Linguistics, Natural Language Processing, Authorship Attribution, Twitter

Abstract

In recent years, Twitter has become a popular testing ground for techniques in authorship attribution. This is due to both the ease of building large corpora as well as the challenges associated with the character limit imposed by the service and the writing styles that have developed as a result. As both false and genuine claims of hacked Twitter accounts have made international news, there is an increasing need for this type of work. For newer Twitter accounts, however, there is little training data. Thus, this study looks to lay the groundwork for cross-domain authorship attribution: training on one source of writing, but testing on another. This work examines three types of feature sets – word n-grams, character n-grams, and stop words – and three machine learning algorithms – Naïve Bayes, Logistic Regression, and Linear Support Vector Classification.

 
 

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