How Arabic Media Construct Armed Conflict: A Corpus-Driven Concordance Analysis - M Indra Mulyadi; Chairunnisa Ahsana Amalan Shaliha; Abzari Jafar; Azhari; Akmal Fajri (Jurnal Bahasa dan Sastra Pusaka Cendekia, 2025)
This study analyzes the construction of armed conflict in Arabic news discourse through the nuanced application of four lexical anchors in Al Jazeera Arabic reporting: ḥarb (war), ṣirāʿ (conflict), silāḥ (weapons), and ḍaḥīya (victim). The issue at hand is that assessments of responsibility, humanitarian impact, and political legitimacy are frequently embedded in local lexical selections that are insufficiently analyzed in limited Arabic corpora. The goal is to figure out what these words mean in context and how they shape the way we talk about violence, competition, ability, and agency. The research utilizes a concordance-only corpus linguistic design through Sketch Engine. Ten hard news articles are collected into a custom corpus, and every Key Word in Context line for the four target words is read and coded, keeping clause-level co-text like intensifiers, numeric strings, attribution verbs, and source nouns. The findings show that there are different roles in the discourse. Ḥarb co selects with more intense descriptors and more precise numbers for humanitarian harm, such as detailed casualty and economic figures. This moralizes and measures war. Ṣirāʿ records competition between multiple actors and structural conflict, often placing current conflicts in the context of longer historical patterns. Silāḥ manifests in claims concerning military action that are evidentially circumscribed, as well as in material records detailing logistics, funding, and safe havens, while simultaneously leveraging public opinion data. ḍaḥīya functions as a transition from passive victimhood to an agentive identity through value-laden lexicon and the legitimization of surveys. Implications encompass the efficacy of concordance-based deep analysis for small Arabic corpora, practical monitoring criteria for newsrooms regarding numeric displays and source attribution, and groundwork for cross-outlet comparisons and Arabic-sensitive quantitative layering in subsequent research.