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OBME 2130 - Digital Methods for Social Sciences

Type d'enseignement : Seminar

Semester : Spring 2018-2019

Number of hours : 24

Language of tuition : English

Pre-requisite

No prerequisites, although basic knowledge on descriptive statistics and/or coding skills are welcome.

Course Description

The first ambition of this course is to teach students how to appraise and study digital environments using quantitative analysis and softwares. The focus will also be put on the design of an original research project making use of quantitative analysis of online empirical data. Classes will alternate theoretical discussions around recent scientific papers (case studies or methodological articles) with more practical training. Beyond readings, students will also have to produce an original empirical analysis of a web corpus (online comments, tweets, reviews, emails, interaction networks, etc.): framing of the research question, data collection, research strategy, visualization of the outcome, etc. Possible research methodologies for the (group) projects will be introduced and discussed in class throughout the semester. The first session will introduce the challenges of data analytics in web studies at large. It will be followed by 8 sessions that will focus on specific methodological aspects (corpus collection, textual coding, network analysis, topic detection, etc.). The three last sessions will be centered around the collective projects of students.

Teachers

COINTET, Jean-Philippe (Associate professor)

Pedagogical format

The pedagogical format is strongly oriented toward a workshop-style class. A short theoretical talk will be given to introduce each course topic to start with. A discussion of the reading will follow before the class turns into applied mode where students will practice data analysis by themselves. It is required that they bring their own laptop and have proper software installed first (the list of (open source) software will be provided along the semester).

Course validation

Students will be assessed based on group work assignments throughout the semester (70%). An individual research proposal due by the end of the semester will complement the final grading (30%).

Workload

Throughout the semester, students will be asked to read a paper on a weekly basis. Those readings will be complemented by more practical, collective assignments (groups of 3 -4 students). Additionally, a final individual paper is due on the 11th week.

Required reading

  • Evans, James A., and Pedro Aceves. "Machine translation: mining text for social theory." Annual Review of Sociology 42 (2016): 21-50.
  • McFarland, D. A., Lewis, K., Goldberg, A., Sep. 2015. Sociology in the Era of Big Data : The Ascent of Forensic Social Science. The American Sociologist.
  • Boyd, Danah, and Kate Crawford. "Six provocations for big data." A decade in internet time: Symposium on the dynamics of the internet and society. Vol. 21 Oxford: Oxford Internet Institute, 2011
  • Manovich, L., 2011. Trending : The promises and the challenges of big social data. Debates in the digital humanities 2, 460–475.
  • Grimmer, J., Stewart, B. M., 2013. Text as data : The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 267–297.

Additional required reading

  • Lazer, D., Kennedy, R., King, G., Vespignani, A., 2014. The parable of google flu : traps in big data analysis. Science 343 (6176), 1203–1205.
  • Grimmer, J., Stewart, B. M., 2013. Text as data : The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 267–297.
  • Demazière, D., Brossaud, C., Trabal, P., Van Meter, K. M., 2006. Analyses textuelles en sociologie(logiciels, méthodes, usages). Didact. Méthodes (Rennes).
  • Moretti, F., 2004. Distant Reading, 2nd Edition. Addison–Wesley