A CORPUS-BASED STUDY OF ARABIC PREPOSITIONS IN AL-JAZEERA NET NEWS

This study investigates the use of ḥarf al-jar in Al-Jazeera Net news articles through a corpus-based approach, focusing on their frequency, distribution and collocational patterns. The research identifies fī, li, bi, min, and ʿalā as the five most frequently occurring prepositions and examines their usage across five news genres: general, economic, technology, culture and freedom. Employing a mixed-methods design, the study extracts data using AntConc 4.2.4 to analyze patterns of occurrence and collocation, offering insights into the syntactic and semantic roles of these prepositions in Arabic news discourse. Based on a corpus of 50 news articles containing 21,686 tokens, fī is the most frequently used ḥarf al-jar, appearing 699 times (3.22%), followed by li (686 occurrences, 3.16%), bi (603 occurrences, 2.78%), min (578 occurrences, 2.67%) and ʿalā (364 occurrences, 1.68%). These findings underscore the critical role of ḥarf al-jar in structuring meaning and enhancing textual coherence in Arabic news discourse. From a pedagogical perspective, this study provides valuable insights for Arabic language learners and educators, particularly in understanding prepositional usage in authentic media contexts. The emphasis on corpus-based analysis further highlights the importance of data-driven approaches in Arabic language teaching and linguistic research.