This course aims to help students to have theoretical and methodological familiarity with the computational social science (CSS, henceforth) approaches and tools using the programming language R. Students will acquire practical skills in CSS by completing weekly readings and participating in in-class coding exercises. Although the course will use applied examples from Political Science and International Relations, the content will still be useful and informative to students interested in the social sciences more broadly.
This course provides a gentle introduction to CSS methods and these methods’ real-world applications (mostly based on Political Science and International Relations), including theoretical explanations and hands-on practical exercises using R. The course is designed for students with little or no experience but desire to advance in computational methods. Topics covered include data manipulation and visualization, web scraping, spatial analysis, fundamentals of text and image as data frameworks, network analysis and usage of machine learning in causal identification. The course will be held in person and will consist of lectures and in-class hands-on exercises. By the end of the course, participants will have practical experience in R, providing the necessary baseline to further improve data science in academia as well as industry. .
Vertical Tabs
Course Learning Outcomes
| Learning Outcomes | Program Learning Outcomes | Teaching Methods | Assessment Methods |
| Developing background on historical development of CSS and digesting the fundamental principles and research methods of CSS | 1, 5-7, 9-14 | 1,2,3 | A,B |
| Evaluate the effectiveness of CSS on analyzing people’s attitudes and behavior | 5-7, 9-15 | 1,2,3 | A,B |
| Understanding several data structures (including but not limited to: panel, time-series, spatial, network, text, and image) | 2-7, 9-15 | 1,2,3 | A,B |
| Gain proficiency in using R for data processing, analysis, and visualization | 2-7, 9-15 | 1,2,3 | A,B |
| Writing simple programs in R | 2-7, 9-15 | 1,2,3 | A,B |
| Profiling computer codes to identify the lines/pieces causing performance problems | 2-7, 9-15 | 1,2,3 | A,B |
Course Flow
| COURSE CONTENT | ||
| Week | Topics | Study Materials |
| 1 | Introduction to Computational Social Science and R |
[3] Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Jebara, T. (2009). Life in the network: The coming age of computational social science. Science, 323(5915), 721-3. DOI: 10.1126/science.1167742
[2] Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 595, 189-196. DOI: s41586-021-03660-7
[1] Grimmer, J. (2015). We are all social scientists now: How big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80-83. DOI: 10.1017/S1049096514001784 |
| 2 | Data Management in R | [1] Llaudet, E. & Imai, K. (2022). Data analysis for social science: A friendly and practical introduction (pp. 1-26, 47-50, 90-96, 126-128, 159-161, 192-195, 226-229). Princeton University Press. |
| 3 | Data Visualization in R | [1] Healy, K. (2019). Data visualization: A practical introduction (pp. 54-92). Princeton University Press. |
| 4 | Basics of Spatial Analysis in R | [1] Lansley, G. & Cheshire, J. (2016). An introduction to spatial data analysis and visualization in R (pp. 39-69). The Consumer Data Research Centre. |
| 5 | APIs and Web Scraping in R | [1] Salganik, M. (2017). Bit by bit: Social research in the digital age (pp. 1-83). Princeton University Press. |
| 6 | Online Surveys and Survey Experiments: Preparation and Analyses using R |
[3] Zhang, B., Mildenberger, M., Howe, P. D., Marlon, J., Rosenthal, S. A., & Leiserowitz, A. (2020). Quota sampling using Facebook advertisements. Political Science Research and Methods, 8(3), 558-564. DOI: 10.1017/psrm.2018.49
[2] Beauchamp, N. (2017). Predicting and interpolating state-level polls using Twitter textual data. American Journal of Political Science, 61(2), 490–503. DOI: 10.1111/ajps.12274
[1] Guess, A. M. (2021). Experiments using social media data. In J. N. Druckman & D. P. Green (Eds.), Advances in experimental political science (pp. 184-198). Cambridge University Press. DOI: 10.1017/9781108777919.013 |
| 7 | Introduction to Textual Data Analysis in R |
[3] Wilkerson, J. & Casas, A. (2017). Large-scale computerized text analysis in political science: Opportunities and challenges. Annual Review of Political Science, 20, 529-44. DOI: 10.1146/annurev-polisci-052615-025542
[2] Grimmer, J. (2022). Text as data: A new framework for machine learning and the social sciences (pp.13-32). Princeton University Press.
[1] Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774-774. DOI: 10.21105/joss.00774 |
| 8 | Midterm | |
| 9 | Statistical Analyses of Textual Data in R |
[3] Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267-297. DOI: 10.1093/pan/mps028
[2] Grimmer, J. (2022). Text as data: A new framework for machine learning and the social sciences (pp.48-77). Princeton University Press.
[1] Denny, M. J., & Spirling, A. (2018). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 26(2): 168-189. DOI: 10.1017/pan.2017.44 |
| 10 | Dictionary-based Text Analyses in R |
[3] Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24-54. DOI: 10.1177/0261927X09351676
[2] Grimmer, J. (2022). Text as data: A new framework for machine learning and the social sciences (pp.171-83). Princeton University Press.
[1] Barberá, P., Boydstun, A. E., Linn, S., McMahon, R., & Nagler, J. (2021). Automated text classification of news articles: A practical guide. Political Analysis, 29(1), 19-42. DOI: 10.1017/pan.2020.8 |
| 11 | Scaling and Classification of Textual Data in R |
[3] Watanabe, K. (2021). Latent semantic scaling: A semisupervised text analysis technique for new domains and languages. Communication Methods and Measures, 15(2), 81-102. DOI: 10.1080/19312458.2020.1832976
[2] Klemmensen, R., Hobolt, S. B., & Hansen, M. E. (2007). Estimating policy positions using political texts: An evaluation of the Wordscores approach. Electoral Studies, 26(4), 746-755. DOI: 10.1016/j.electstud.2007.07.006
[1] Grimmer, J. (2022). Text as data: A new framework for machine learning and the social sciences (pp.184-217). Princeton University Press. |
| 12 | Introduction to Images as Data in R |
[2] Ooms, J. (2022). tesseract: Open Source OCR Engine. The Comprehensive R Archive Network.
[1] Joo, J., & Steinert-Threlkeld, Z. C. (2022). Image as data: Automated content analysis for visual presentations of political actors and events. Computational Communication Research, 4(1), 11-67. DOI: 10.5117/CCR2022.1.001.JOO |
| 13 | Basics of Network Analysis and Visualization in R |
[3] Bail, C. A., Guay, B., Maloney, E., Combs, A., Hillygus, D. S., Merhout, F., ... & Volfovsky, A. (2020). Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017. Proceedings of the National Academy of Sciences, 117(1), 243-250. DOI: 10.1073/pnas.1906420116
[2] Lazer, D. (2011). Networks in political science: Back to the future. PS: Political Science & Politics, 44(1), 61-68. DOI: 10.1017/S1049096510001873
[1] Fowler, J. H., Heaney, M. T., Nickerson, D. W., Padgett, J. F., & Sinclair, B. (2011). Causality in political networks. American Politics Research, 39(2), 437-80. DOI: 10.1177/1532673X10396310 |
| 14 | Causal Forests: Machine Learning in Causal Inference using R |
[3] Wuttke, A. (2021). The pleasure principle: Why (some) people develop a taste for politics: Evidence from a preregistered experiment. Politics and the Life Sciences, 40(1), 19-39. DOI: 10.1017/pls.2020.18
[2] Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational Studies, 5(2), 37-51. DOI: 10.1353/obs.2019.0001
[1] Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2): 1148–78. DOI: 10.1214/18-AOS1709 |
| 15 | Review | |
| 16 | Final Examination | |
Recommended Sources
| SOURCES | |
| Textbook | - |
| Additional Resources | GESIS – Leibniz Institute for the Social Sciences (n.d.) Awesome computational social science. https://github.com/gesiscss/awesome-computational-social-science |
Material Sharing
| MATERIAL SHARING | |
| Documents | - |
| Assignments | - |
| Exams | - |
Assessment
| ASSESSMENT | ||
| IN-TERM STUDIES | NUMBER | PERCENTAGE |
| Midterm | 1 | 32% |
| Participation | 14 | 2% |
| Total | 100 | |
| Contribution of Final Examination to Overall Grade | 40% | |
| Contribution of In-Term Studies to Overall Grade | 60% | |
| Total | 100 | |
Course’s Contribution to Program
| COURSE'S CONTRIBUTION TO PROGRAM | |||||||
| No | Program Learning Outcomes | Contribution | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Students will demonstrate their comprehensive knowledge of the basic concepts and theories of Political Science and International Relations as well as other related disciplines such as Law, Economics and Sociology. | X | |||||
| 2 | Students will interpret the structure, institutions and operation of national, international and supranational entities via utilization of the concepts and theories of Political Science and International relations and produce project reports that include possible solutions to problems of such institutions when necessary. | X | |||||
| 3 | Students will demonstrate that they have developed a comparative, analytical and interdisciplinary approach vis-à-vis human societies and political systems. | X | |||||
| 4 | Students will have improved their skills and awareness of personal responsibility and team membership through conducting group or independent research projects, doing internships and producing their graduation dissertations. | X | |||||
| 5 | Students will demonstrate proficiency in quantitative and qualitative data collections methods. | X | |||||
| 6 | Students will prove their understanding of the rapidly-evolving dynamics of national and global environments requires constant self-assessment, life-long learning, and the ability to formulate innovative solutions to maintain their personal and professional development. | X | |||||
| 7 | Students should be able to critically evaluate the body of knowledge in political science, assess self-competency and direct self-learning efforts accordingly. | X | |||||
| 8 | Students will implement written and oral communication skills in English and Turkish in both academic and professional settings. | X | |||||
| 9 | Students should be able to effectively demonstrate their knowledge of written, oral and reading skills in English both in international institutional settings and follow and interpret the global dynamics of the International Relations discipline. | X | |||||
| 10 | Students will demonstrate their social skills and experience required by public or private institutions or in the academia. | X | |||||
| 11 | Students will show empathy and respect towards societies other than one’s own. | X | |||||
| 12 | Students should be able to effectively utilize computer and information technologies commonly-used in the social sciences. | X | |||||
| 13 | Students will interpret domestic and international developments and express opinions, having acquired advanced knowledge and proficiency in the via communication with international scholars and students. | X | |||||
| 14 | Students will respect personal, social and academic ethical norms. | X | |||||
| 15 | Students should understand the personal, social, and ecological dimensions of social responsibility, and show duties of active and global citizenship. | X | |||||
| 16 | Students should know that universality of social-political and legal rights and social justice are the principle components of contemporary society, and that scientific thinking is an essential prerequisite for maintaining social advancement and global competitiveness. | X | |||||
ECTS
| ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION | |||
| Activities | Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 7 | 7 |
| Hours for in-the-classroom study (hands-on practice) | 14 | 1 | 14 |
| Final examination | 1 | 8 | 8 |
| Total Work Load | 125 | ||
| Total Work Load / 25 (h) | 5 | ||
| ECTS Credit of the Course | 5 | ||