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

Area of Science:

• Media Studies
• Development Studies
• Artificial Intelligence

Background:

• The discourse on United Nations Sustainable Development Goals (SDGs) is predominantly based on English-language research, neglecting Arabic media.
• There is a need for large-scale analyses examining how Arab media engages with the SDGs and aligns with national priorities.

Purpose of the Study:

• To analyze Arab media engagement with the SDGs over the past decade.
• To evaluate the alignment between media coverage and official government priorities in Arabic-speaking countries.
• To address the gap in large-scale, Arabic-focused SDG discourse analysis.

Main Methods:

• Collected and processed over 1.2 million Arabic news articles from ten countries (2010-2024).
• Utilized data augmentation, deep learning (Transformer models), and large language models (LLMs) to train classifiers.
• Developed AI classifiers to detect SDG references and categorize articles by specific SDGs.

Main Results:

• Identified regional patterns in SDG coverage: North Africa focused on governance, Gulf countries on economic/environmental themes.
• Found a general alignment between media discourse and official SDG priorities, with some exceptions.
• Demonstrated the effectiveness of AI methods in analyzing large-scale Arabic media data for SDG research.

Conclusions:

• This study pioneers the use of AI for large-scale SDG analysis in Arabic media.
• Findings provide valuable insights for policymakers, media professionals, and development stakeholders in the Arab region.
• Highlights the potential for AI to bridge research gaps and inform development strategies.