  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, heart rate 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, or ComInt, 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 for building truly multi method research design.
