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74710-01 - Seminar: Introduction to Social Data Science (3 CP)
| Semester | spring semester 2025 |
| Course frequency | Irregular |
| Lecturers | Rosa Lavelle-Hill (rosa.lavelle-hill@unibas.ch, Assessor) |
| Content | This course will introduce the basics of social data science, following these lecture themes: (1) What is Social Data Science? (2) Big Data and Machine Learning: Advantages and Limitations. (3) What makes good Social Data Science research? (4) Dissemination and evaluation of a social data science research article. In the latter section, students will present in small groups a social data science study from their academic discipline of choice. No prior knowledge of data science or social science methods is assumed. By the end of the course, students will have a grasp of what is social data science. The course introduces different digital data types (structured vs. unstructured; qualitative vs. quantitative) and data collection methods (i.e., surveys, experience sampling, digital traces, and online experiments). Students will learn about different approaches to data analysis (i.e., inductive vs. deductive, explanation vs. prediction, algorithmic modeling vs. data modeling) and evaluate their utility for answering different social data science research questions. Students will also get a conceptual introduction to machine learning as well as presentations of research examples using machine learning methods to answer social questions. All students must contribute towards a group presentation and evaluation of a social data science scientific article from their chosen discipline. In addition, students are expected to participate in class discussions and provide constructive feedback on their peers' presentations. |
| Learning objectives | To think critically about when digital methods, big data, and machine learning should be used for social science and humanities research by assessing the advantages, disadvantages, and limitations. To be able to present and critically evaluate a social data science research study of choice. |
| Bibliography | Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., ... & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216-9221. https://www.pnas.org/doi/pdf/10.1073/pnas.1804840115 Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231. Lavelle-Hill, R., Frenzel, A. C., Goetz, T., Lichtenfeld, S., Marsh, H. W., Pekrun, R., ... & Murayama, K. (2024). How the predictors of math achievement change over time: A longitudinal machine learning approach. Journal of Educational Psychology. https://doi.org/10.1037/edu0000863 Lavelle-Hill, R., Harvey, J., Smith, G., Mazumder, A., Ellis, M., Mwantimwa, K., & Goulding, J. (2022). Using mobile money data and call detail records to explore the risks of urban migration in Tanzania. EPJ Data Science, 11(1), 28. https://link.springer.com/content/pdf/10.1140/epjds/s13688-022-00340-y.pdf Mosleh, M., Martel, C., Eckles, D., & Rand, D. G. (2021). Shared partisanship dramatically increases social tie formation in a Twitter field experiment. Proceedings of the National Academy of Sciences, 118(7), e2022761118. https://www.pnas.org/doi/pdf/10.1073/pnas.2022761118 Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122. |
| Comments | Only three absences are permitted; medical attestation is required for further absences! |
| Admission requirements | The number of participants is limited. In case of over-subscription, students of Digital Humanities will be given priority. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Tuesday | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
Dates
| Date | Time | Room |
|---|---|---|
| Tuesday 25.02.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 04.03.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 11.03.2025 | 16.15-18.00 | Fasnachstferien |
| Tuesday 18.03.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 25.03.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 01.04.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 08.04.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 15.04.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 22.04.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 29.04.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 06.05.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 13.05.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 20.05.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Tuesday 27.05.2025 | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
| Modules |
Modul: Erweiterung Gesellschaftswissenschaften M.A. (Master's degree subject: Political Science) Modul: Methoden der Near & Middle Eastern Studies und der Gesellschaftswissenschaften (Master's degree subject: Near & Middle Eastern Studies) Modul: Transfer: Digital History (Master's degree program: European History in Global Perspective) Module: Introduction to Digital Humanities (Master's degree subject: Digital Humanities) Module: Research Skills (Master's degree program: African Studies) Module: Societal Approaches (Master's Studies: European Global Studies) |
| Assessment format | continuous assessment |
| Assessment registration/deregistration | Reg.: course registration; dereg.: not required |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Humanities and Social Sciences, studadmin-philhist@unibas.ch |
| Offered by | Digital Humanities Lab |