Political Analysis of Social Media Data

Course content

The rapid growth in the use of social media and the availability of data to analyze it has opened up immense new and exciting possibilities for social and political inquiry. To equip students with the ability to conduct such research themselves, this course provides an introduction to the analysis of social media data. It covers the analysis of these data from the research design stage through to data collection, data cleaning, and methods for analysis. The course thus takes a hands-on approach to conducting empirical research to answer some of the big questions in social media research. Students will become familiar with the many research designs and methods available for conducting social media research; learn to be critical of existing methods and research designs; and develop the technical skills to conduct such research themselves.

Education

Bachelor student (2012 programme curriculum): 20 ECTS

Bachelor student (2017 programme curriculum): 15 ECTS

Master student: 15 ECTS

 

Notice: It is only possible to enroll for one course having a 3-day compulsory written take-home assignment exam due to coincident exam periods.

Learning outcome

Knowledge:

Upon completion of the course, students will (1) be able to develop research designs concerning the key questions in the study of the politics of social media, (2) discuss and critically analyze the methods available for social media analysis, and (3) have the technical abilities to put their research designs into practice.

 

Skills:

Students will have the technical skills to collect, clean, and analyze social media data.

 

Competences:

Students will be able to critically analyze the research designs of existing studies in social media research.

Teaching will be conducted through a combination of weekly lectures, student presentations, and labs.

Examples of material included in the course include:

 

Lazer, D., Pentland, A., Adamic, L., Aral, S., Albert-László, Barabási, Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., and Alstyne, M. V. (2009). Computational social science. Science, 323:721–723.

 

Lazer, D. and Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 43:7.1–7.21.

 

Ruths, D. and Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213):1063– 1064.

 

Golder, S. A. and Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40:129–152.

 

Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media.

 

Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press, Princeton, NJ.

 

Steinert-Threlkeld, Z. C. (2018). Twitter as Data. Cambridge Elements: Quantitative and Computational Methods in Social Science. Cambridge University Press, Cambridge, UK.

 

Bond, R. and Messing, S. (2015). Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on facebook. American Political Science Review, 109(1):62–78.

 

Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Political Analysis, 23(1):76–91.

 

Beauchamp, N. (2017). Predicting and interpolating state-level polls using twitter textual data. American Journal of Political Science, 61(2):490–503.

 

Barberá, P. and Zeitzoff, T. (2018). The new public address system: Why do world leaders adopt social media? International Studies Quarterly, 62(1):121–130.

 

Zeitzoff, T. (2011). Using social media to measure conflict dynamics: An application to the 2008-2009 gaza conflict. Journal of Conflict Resolution, 55(6):938–969.

 

Rheault, L., Rayment, E., and Musulan, A. (2019). Politicians in the line of fire: Incivility and the treatment of women on social media. Research & Politics, January-March:1–7.

 

Sivak, E. and Smirnov, I. (2019). Parents mention sons more often than daughters on social media. Proceedings of the National Academy of Sciences, 116(6):2039–2041.

 

Munger, K. (2017). Tweetment effects on the tweeted: Experimentally reducing racist harass- ment. Political Behavior, 39(3):629–649.

 

Munger, K., Bonneau, R., Nagler, J., and Tucker, J. A. (Forthcoming). Elites tweet to get feet off the streets: Measuring regime social media strategies during protest. Political Science Research and Methods, pages 1–20.

 

Eady, G., Nagler, J., Guess, A., Zilinsky, J., and Tucker, J. A. (2019). How many people live in political bubbles on social media? evidence from linked survey and twitter data. SAGE open, January-March:1–21.

 

Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., and Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489:295–298.

 

Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., and Lazer, D. (2019). Fake news on twitter during the 2016 u.s. presidential election. Science, 363:374–378.

 

Allcott, H., Gentzkow, M., and Yu, C. (2018). Trends in the diffusion of misinformation on social media. Unpublished manuscript, September.

 

Guess, A., Nagler, J., and Tucker, J. (2019). Less than you think: Prevalence and predictors of fake news dissemination on facebook. Science Advances, 5(1):1–8.

 

Pennycook, G. and Rand, D. G. (Forthcoming). Fighting misinformation on social media using crowdsourced judgments of news source quality. Proceedings of the National Academy of Sciences, pages 1–6.

 

Bail, C., Argyle, L., Brown, T., Bumpus, J., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., and Volfovsky, A. (2018). Exposure to opposing views can increase political polarization: Evidence from a large-scale field experiment on social media. Proceedings of the National Academy of Sciences, 115(37):9216–9221.

This course focuses on the analysis of data. Students should therefore be relatively familiar with quantitative research, and have a basic understanding of statistical analysis and software (e.g. Stata, R, or Python). The course is taught in R, although students are not expected to have experience with the language prior to taking the course. An introduction to R will be provided at the beginning of the class.

Written
Oral
Peer feedback (Students give each other feedback)

 

Students will receive written feedback from the instructor on assignments; oral feedback from the instructor and peers following presentations; and technical feedback during labs.

ECTS
15 ECTS
Type of assessment
Written assignment
3-day compulsory written take-home assignment
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment
  • Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
  • Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
  • Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner

Single subject courses (day)

  • Category
  • Hours
  • Class Instruction
  • 56
  • English
  • 56