Designing National Immigration Policies in a Rapidly Globalizing World: The Impact of Digital Interconnectedness on Policies and Views on Immigration in Canada

Researchers: Abdie Kazemipur1, Hamid Akbary1
Affiliation: University of Calgary1
Keywords: Canada, immigration, parliamentary debates, social media, social absorptive capacity, political absorptive capacity, artificial intelligence, machine learning, computational social sciences, public opinion, social attitudes
Jump to: Methodology, Findings


Overview: This study aimed to expand the concept of ‘absorptive capacity’ used in immigration discourse to capture the capacity of the host societies to accommodate new immigrants. This study proposes to add two new dimensions: ‘political’ and ‘social’ absorptive capacity. These concepts are measured through an analysis of Canadian parliamentary debates and Canadian social media.

Objective: to test the feasibility of using artificial intelligence and machine learning to measure political and social absorptive capacity.

Research Justification: The influx of large numbers of refugees in relatively short periods has often resulted in backlash by local populations and right-wing populism among politicians. This can create tension between the government and public about immigration issues. The research findings will offer predictions for the future trajectories of immigration and refugee discourses and policies in democratic countries like Canada.


The study used the conceptual framework of ‘absorptive capacity’ – the capacity of the existing housing market, job market, and education system to accommodate an increase in the number of admitted immigrants. This concept is extended to ‘social absorptive capacity’ – the degree to which new immigrants are accepted and welcomed by the general population – and ‘political absorptive capacity’ – the dynamics of governmental thinking about immigration. 

Social and political absorptive capacity were measured through an analysis of Canadian parliamentary debates and Canadian social media. In order to analyse such a large volume of data, the research team employed Artificial Intelligence (AI) and Machine-Learning (ML) techniques, developed in the field of computational social science.


As a pilot study, the findings are tentative and in need of further refinement. While some challenges were encountered, the findings were also promising. A full-scale version of this project would apply the methodology to data from other national contexts for a comparative study of different immigrant-receiving countries.

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