Zurück zur Auswahl
74710-01 - Seminar: Introduction to Social Data Science (3 KP)
| Semester | Frühjahrsemester 2025 |
| Angebotsmuster | unregelmässig |
| Dozierende | Rosa Lavelle-Hill (rosa.lavelle-hill@unibas.ch, BeurteilerIn) |
| Inhalt | 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. |
| Lernziele | 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. |
| Literatur | 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. |
| Bemerkungen | Only three absences are permitted; medical attestation is required for further absences! |
| Teilnahmevoraussetzungen | The number of participants is limited. In case of over-subscription, students of Digital Humanities will be given priority. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Dienstag | 16.15-18.00 | Kollegienhaus, Hörsaal 119 |
Einzeltermine
| Datum | Zeit | Raum |
|---|---|---|
| Dienstag 25.02.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 04.03.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 11.03.2025 | 16.15-18.00 Uhr | Fasnachstferien |
| Dienstag 18.03.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 25.03.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 01.04.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 08.04.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 15.04.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 22.04.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 29.04.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 06.05.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 13.05.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 20.05.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Dienstag 27.05.2025 | 16.15-18.00 Uhr | Kollegienhaus, Hörsaal 119 |
| Module |
Modul: Erweiterung Gesellschaftswissenschaften M.A. (Master Studienfach: Politikwissenschaft) Modul: Introduction to Digital Humanities (Master Studienfach: Digital Humanities) Modul: Methoden der Gesellschaftswissenschaften (Masterstudium: European Global Studies) Modul: Methoden der Near & Middle Eastern Studies und der Gesellschaftswissenschaften (Master Studienfach: Near & Middle Eastern Studies) Modul: Research Skills (Master Studiengang: African Studies) Modul: Transfer: Digital History (Master Studiengang: Europäische Geschichte in globaler Perspektive ) |
| Prüfung | Lehrveranst.-begleitend |
| An-/Abmeldung zur Prüfung | Anmelden: Belegen; Abmelden: nicht erforderlich |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | Pass / Fail |
| Belegen bei Nichtbestehen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Historische Fakultät, studadmin-philhist@unibas.ch |
| Anbietende Organisationseinheit | Digital Humanities Lab |