Building Textual Fuzzy Interpretive Structural Modeling to Analyze Factors of Student Mobility Based on User Generated Content

Abstract

Many factors influence student mobility across regions and countries. The roles of these factors, along with their interrelationship and interaction, make student mobility a complex decision-making issue. Many textual data generated on social media can answer many open questions about factors affecting human behavior, particularly social mobility. We have developed a novel methodology, called Textual Fuzzy Interpretive Structural Modeling (TFISM), that automatically analyses large textual datasets to identify the internal and external relationships between management or decision-making problems. This computational social science methodology enhances Interpretive Structural Modeling (ISM) approaches to allow the input to be textual data. It is multi-disciplinary and integrates ISM with Artificial Intelligence, Text extraction, and information retrieval techniques. TFISM is a domain-free method, while we have validated this methodology on two different datasets from social media and academic articles. In this paper, we present the results of our study to identify the critical factors and most influential factors for global student mobility.