Title

Feature extraction and selection for Arabic tweets authorship authentication

Document Type

Article

Source of Publication

Journal of Ambient Intelligence and Humanized Computing

Publication Date

6-1-2017

Abstract

© 2017, Springer-Verlag Berlin Heidelberg. In tweet authentication, we are concerned with correctly attributing a tweet to its true author based on its textual content. The more general problem of authenticating long documents has been studied before and the most common approach relies on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Inspired by the success of modern automatic document classification problem, some researchers followed the Bag-Of-Words (BOW) approach for authenticating long documents. In this work, we consider both approaches and their application on authenticating tweets, which represent additional challenges due to the limitation in their sizes. We focus on the Arabic language due to its importance and the scarcity of works related on it. We create different sets of features from both approaches and compare the performance of different classifiers using them. We experiment with various feature selection techniques in order to extract the most discriminating features. To the best of our knowledge, this is the first study of its kind to combine these different sets of features for authorship analysis of Arabic tweets. The results show that combining all the feature sets we compute yields the best results.

ISSN

1868-5137

Publisher

Springer Verlag

Volume

8

Issue

3

First Page

383

Last Page

393

Disciplines

Computer Sciences | Social and Behavioral Sciences

Keywords

Authorship authentication, BOW features, Computational intelligence, Correlation-based feature selection, Decision tree, Information gain, NB, Online social networks, PCA, Relief, Stylometric features, SVM

Scopus ID

85019770527

Indexed in Scopus

yes

Open Access

no

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