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.
Birincil Dil | İngilizce |
---|---|
Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 1 Temmuz 2019 |
Yayımlandığı Sayı | Yıl 2019 |
Widening the World of IR