Work

Artificial intelligence-driven insights into Arab media’s sustainable development goals coverage

Year: 2025

Type: article

Abstract: This study examines how Arab media have engaged with the United Nations Sustainable Development Goals (SDGs) over the past decade and evaluates the alignment between media coverage and official government priorities. The research addresses the lack of large-scale, Arabic-focused analyses in SDG discourse, which is often dominated by English-language studies. We collected and processed a unique dataset of over 1.2 million Arabic news articles from ten countries between 2010 and 2024. Using a combination of data augmentation, deep learning (specifically, Transformer-based models), and large language models (LLMs), we trained classifiers to detect references to the SDGs and categorize articles by specific SDGs. The results reveal regional patterns in SDG coverage, with North African countries focusing more on governance-related goals, while Gulf countries emphasize economic and environmental themes. Our findings reveal a general alignment between media discourse and official SDG priorities, with notable exceptions. This study is the first to combine artificial intelligence (AI) methods and Arabic media at this scale for SDG analysis, offering new tools and insights for policymakers, media professionals, and development stakeholders.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.3071

Authors: Mohammed Alsuhaibani, Kamel Gaanoun, Ali Mustafa Qamar

Institutions: Qassim University, Institut National de Statistique et d'Economie Appliquée

Language: English

Topic: Media Studies and Communication

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Climate action

Open Access status: gold

Estimated APC paid: $1,395