2022-01-252022-01-252021https://hdl.handle.net/1920/12586Over the past decade there has been a dramatic increase in forced migration of populations that are impacted by state instability failure, which is often characterized by an increase in violence and human rights violations. A prominent example of this type of migration is the most recent crisis in Syria, which has caused more than 50 percent of the population to be displaced both within the country and as refugee populations that have crossed into neighboring countries and continued to destinations such as the European Union (EU). While such forced migration is not a new phenomenon, advances in Information and Communications Technology (ICT) and increased online connectivity (e.g., through social media usage) have transformed the way migrant populations create, foster, and develop support networks during the three major stages of migration: pre-migrant, post-migrant, and settled migrant. In conjunction, such advances have also led to the development of various centralized and crowdsourced migration-related data collection efforts that now produce substantial amounts of openly available data. The confluence of these two trends is creating new opportunities for developing a better data-driven understanding of forced migration. Within this context, this research seeks to address two key questions: first, can open and in particular social-media data provide reliable quantification during the three stages of migration; and second, can open data, both authoritative and crowdsourced, be used to model forced migration phenomena. Using the Syrian crisis (2012-2018) as a case study, this research addresses the first question by proposing a framework for characterizing and analyzing the utility of open data (and crowdsourced data) for understanding forced migration. To address the second question, a novel conceptual (causal) model for state instability-driven forced migration is proposed based entirely on openly available data. Based on such data, this model is then implemented, verified, and calibrated using a System Dynamics Modeling (SDM) approach, suggesting that the combination of open data and SDM is a valid approach for modeling forced migration. Furthermore, the research demonstrates that such an approach could be used to study the possible impacts of various policy changes (e.g., border closure) on the flow of migrants.