AI Summary & Key Takeaways
AI InsightThe paper argues that computational methods transcend themethodological schism between qualitative and quantitative approaches andform a solid foundation in building truly multi-method research design.
📌 3 Key Takeaways
Abstract
Computational Social Science emerged as a highly technical and populardiscipline in the last few years, owing to the substantial advances in communicationtechnology and daily production of vast quantities of personal data. As per capitadata 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 anddemographic information from digital data also increased. A vast methodologicalgap exists in ‘computational international relations’, which refers to the use ofone or a combination of tools such as data mining, natural language processing,automated text analysis, web scraping, geospatial analysis and machine learningto provide larger and better organized data to test more advanced theories ofIR. After providing an overview of the potentials of computational IR and howan IR scholar can establish technical proficiency in computer science (such asstarting with Python, R, QGis, ArcGis or Github), this paper will focus on someof the author’s works in providing an idea for IR students on how to think aboutcomputational IR. The paper argues that computational methods transcend themethodological schism between qualitative and quantitative approaches andform a solid foundation in building truly multi-method research design.