This thesis offers a corpus-based analysis and description of two of the most frequently used words in Modern Standard Arabic (MSA), namely بَعْدَ /ba’da/ (after, after [that]) and قَبْلَ /qabla/ (before, ago). To this end, a corpus of 4,102,134 tokens was compiled from two prominent Arabic news websites: CNN and BBC as a purposive sample. The investigation of this study adopted the linguistic analysis as a main research method to characterize these two adverbial nouns, using WordSmith Tools as a main instrument for data analysis, based on Sinclair's model (1999) for data sampling, and Hunston’s classification (2002, p. 46) for data categorization. Three linguistic qualities were addressed in the present analysis: semantic values and cohesive functions, syntactic features, and idiomatic usages. The study identified a set of 459 occurrences for بَعْدَ /ba’da/, and classified them into three categories according to its meanings: after, after that, and idiomatic expressions. The study also identified a set of 374 instances for قَبْلَ /qabla/, and classified them also into three categories according to its meanings: before, ago, and idiomatic expressions. The study indicated that the target words play a crucial role in the organization and interpretation of the Arabic online news. The findings can create a basis for future research where a linguistic analysis of the adverbial nouns in Arabic could be carried out in order to increase knowledge of AFL learners about the adverbial use. Findings could also be used for further pedagogical purposes.


Applied Linguistics Department

Degree Name

MA in Teaching Arabic as a Foreign Language

Graduation Date


Submission Date

June 2016

First Advisor

Taha, Zeinab

Committee Member 1

Elessawi, Raghda

Committee Member 2

Agameya, Amira


174 p.

Document Type

Master's Thesis


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Institutional Review Board (IRB) Approval

Not necessary for this item