AI Summary & Key Takeaways
AI InsightThe paper argues that computational methods transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation in building truly multi-method research design.
📌 3 Key Takeaways
Abstract
Computational Social Science emerged as a highly technical and popular discipline in the last few years, owing to the substantial advances in communication technology and daily production of vast quantities of personal data. As per capita data production significantly increased in the last decade, both in terms of its size (bytes) as well as its detail (heartrate monitors, internet-connected appliances, smartphones), social scientists’ ability to extract meaningful social, political and demographic information from digital data also increased. A vast methodological gap exists in ‘computational international relations’, which refers to the use of one or a combination of tools such as data mining, natural language processing, automated text analysis, web scraping, geospatial analysis and machine learning to provide larger and better organized data to test more advanced theories of IR. After providing an overview of the potentials of computational IR and how an IR scholar can establish technical proficiency in computer science (such as starting with Python, R, QGis, ArcGis or Github), this paper will focus on some of the author’s works in providing an idea for IR students on how to think about computational IR. The paper argues that computational methods transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation in building truly multi-method research design.