This analysis employs NLP and machine learning to uncover factors behind irregular migration trends in Europe.
Why do individuals from the global South migrate to Europe bypassing legal pathways? This study examines the question using an algorithm that leverages OpenAI ( LLM : GPT -4), NLP , and Python libraries to extract data from literature. It explores two competing concepts: “cyclical global hegemony” and “cyclical domestic instability” as key drivers of irregular migration and refugee flow. Findings indicate migration stems from a self-reinforcing cycle of: 1) global hegemony, where foreign policies, wars, and selective inclusion shape migration routes; 2) colonial and post-colonial dependency, where economic imbalances and aid reliance sustain migration pressures; and 3) domestic instability, where political unrest, poor governance, and ethnic divides push individuals toward irregular migration. These cycles interact, reinforcing migration culture through diaspora networks and the migration industry.
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Muhammad Wajid Tahir (2025) studied this question.
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