Universität Wien

200226 SE Theorie und Empirie wissenschaftlichen Arbeitens (Geist und Gehirn) 2 (2025S)

8.00 ECTS (4.00 SWS), SPL 20 - Psychologie
Prüfungsimmanente Lehrveranstaltung

Dieses TEWA 2 kann für alle Schwerpunkte absolviert werden.
Moodle
Di 20.05. 14:00-18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 20 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 04.03. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 11.03. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 18.03. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 25.03. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 01.04. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 08.04. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 29.04. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 06.05. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 13.05. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 27.05. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 03.06. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 10.06. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 17.06. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Dienstag 24.06. 14:00 - 18:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

In the course Introduction to Computational Social Sciences, students learn how to use natural language processing tools to analyze the content of large numbers of texts automatically. Students learn to perform sentiment analyses of modern textual sources from the internet, such as movies and books, and online reviews of these media and others, such as video games, music, etc. Furthermore, students learn to analyze historical sources such as fiction works from the past. They can quantify the temporal change of values and preferences expressed in these sources and their relationship to historical events and socio-economic dynamics. Evaluation depends on three short assignments, a final project, and an oral presentation with Q&A.

The course roughly covers the following modules:
Introduction to Python. Variables, numbers, strings, lists, loops, modules, and functions;
Introduction to modules. NLTK, Matplotlib, Regular Expressions, etc.;
Bag-of-words analyses. Preprocessing. Finding word synonyms and hyponyms using WordNet;
Word Embeddings. Building semantic clouds and navigating meaning spaces using Word2vec;
Pandas and simple visualizations: Creating datasets, word clouds, and frequency plots. Sentiment analysis. Overview and implementation of lexical and machine learning techniques;
Web scraping. Reading HTML/XML files with Beautiful Soup; Website interaction with Selenium;
APIs. Extracting structured information from social media and popular repositories;
Diachronic Analyses. Using psychometric tools to build valid bags-of-words; historical time series;
Designing studies and testing hypotheses. Relationship between historical events, socioeconomics, and word frequencies. Cross-correlations, lag analyses, and linear mixed models.

Art der Leistungskontrolle und erlaubte Hilfsmittel

50% Final Project Oral Presentation (Intro and Methods) and Q&A
50% Class homework assignments, including 3 small assignments (3 x 10%) and a final project (20%) (Individual)

Mindestanforderungen und Beurteilungsmaßstab

>50% is necessary for a positive end result; >50% to 63% : grade 4, >63% to 75% : grade 3, >75% to 88% : grade 2, >88% : grade 1

Prüfungsstoff

All of which was covered in class

Literatur

Harvard python course:
- https://www.youtube.com/watch?v=nLRL_NcnK-4&t=7492s
Computational social science courses:
- https://ayoubbagheri.nl/applied_tm/
- https://github.com/JanaLasser/SICSS-aachen-graz
- http://digitalmedia.andreasjungherr.de/docs/intro.html
- Engel, U., Quan-Haase, A., Liu, S., & Lyberg, L. (Eds.). (2021). Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods (1st ed.). Routledge. https://doi.org/10.4324/9781003025245
Historical Psychology:
- Martins & Baumard (2022). How to develop reliable instruments to measure the cultural evolution of preferences and feelings in history? Frontiers in Psychology (13) https://osf.io/acukm/

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Di 04.03.2025 11:26