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78299-01 - Lecture: Introduction to Social Data Science (2 CP)
| Semester | spring semester 2026 |
| Course frequency | Irregular |
| Lecturers | Rosa Lavelle-Hill (rosa.lavelle-hill@unibas.ch, Assessor) |
| Content | This interactive lecture course introduces the conceptual foundations of Social Data Science and critically examines how large digital datasets and computational methods can be used to study social phenomena. The course focuses on theory, methodology, and evaluation rather than technical implementation and is designed to complement the applied exercise-based course Social Data Science Methods. Students are introduced to key sources of social data, including online surveys, experience sampling methods, digital trace data, and experimental data collected in online environments. Central methodological themes include causality and experimentation, prediction versus explanation, inferential statistics versus algorithmic approaches, and the strengths and limitations of Big Data and machine learning for social science research. The course further addresses critical issues such as data quality, bias, validity, explainability, and ethical considerations, including privacy and responsible AI. Special attention is given to Explainable AI (XAI), the replication crisis, and Open Science, with guided discussions on whether and how data science methods can contribute to more robust and transparent research practices. Lectures are combined with interactive activities, paper discussions, and research presentations. Students will engage with contemporary empirical studies from psychology, sociology, political science, and related fields, and will develop the ability to critically evaluate social data science research and its societal implications. The course culminates in group presentations focused on dissemination and evaluation of a social data science research article. |
| Learning objectives | To understand the conceptual foundations and interdisciplinary scope of Social Data Science. To critically evaluate different forms of social data, including surveys, experience sampling, digital trace data, and experimental data. To assess the advantages, limitations, and ethical challenges of Big Data and machine learning in social science research. To distinguish between explanation and prediction, and between inferential and algorithmic approaches to data analysis. To critically reflect on issues of bias, validity, explainability, replicability, and Open Science in the Social Sciences. To effectively discuss and communicate academic social data science research. |
| 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. https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916), 892-895. https://www.science.org/doi/pdf/10.1126/science.1165821 Fried, E. I., Papanikolaou, F., & Epskamp, S. (2021). Mental Health and Social Contact During the COVID-19 Pandemic: An Ecological Momentary Assessment Study. Clinical Psychological Science. https://doi.org/10.1177/21677026211017839 Lavelle-Hill, R., Smith, G., Deininger, H., & Murayama, K. (2025). An explainable artificial intelligence handbook for psychologists: Methods, opportunities, and challenges. Psychological Methods. https://psycnet.apa.org/fulltext/2026-46377-001.pdf Lavelle-Hill, R., Goulding, J., Smith, G., Clarke, D. D., & Bibby, P. A. (2020). Psychological and demographic predictors of plastic bag consumption in transaction data. Journal of Environmental Psychology, 72, 101473. https://doi.org/10.1016/j.jenvp.2020.101473 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), https://www.science.org/doi/pdf/10.1126/science.aac4716 Shmueli, G. (2010). To explain or to predict?. Statistical science, 289-310. https://www.jstor.org/stable/pdf/4105894 Schoedel, R., Kunz, F., Bergmann, M., Bemmann, F., Bühner, M., & Sust, L. (2024). Snapshots of Daily Life: Situations Investigated Through the Lens of Smartphone Sensing. Journal of Personality and Social Psychology: Personality Processes and Individual Differences. https://doi.org/10.1037/pspp0000469 Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122. https://journals.sagepub.com/doi/pdf/10.1177/1745691617693393 |
| 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 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
Dates
| Date | Time | Room |
|---|---|---|
| Tuesday 24.02.2026 | 12.15-14.00 | Fasnachtsferien |
| Tuesday 03.03.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 10.03.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 17.03.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 24.03.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 31.03.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 07.04.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 14.04.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 21.04.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 28.04.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 05.05.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 12.05.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 19.05.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Tuesday 26.05.2026 | 12.15-14.00 | Kollegienhaus, Seminarraum 103 |
| Modules |
Modul: Digital Humanities, Culture and Society (Master's degree subject: Digital Humanities) Module: Introduction to Digital Humanities (Master's degree subject: Digital Humanities) 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 |