200083 SE Advanced Seminar: Mind and Brain (2023S)
Introduction to Computational Social Sciences
Labels
Registration/Deregistration
- Registration is open from Th 02.02.2023 09:00 to Th 23.02.2023 09:00
- Deregistration possible until Fr 03.03.2023 09:00
Details
Lecturers
Classes (iCal) - next class is marked with N
Week 1 - Introduction to digital humanities. Brief introduction to computational social sciences; description of the general set of tools and datasets.
Week 2 - Bag of words approach. Refresher of Python syntax and objects; How to create a bag of words using dictionary tools like WordNet.
Week 3 - Preprocessing in NLP and Frequency analysis. Why preprocessing, general steps; How to extract the most frequent words after preprocessing; How to extract the most frequent words in a text;
Week - Introduction to datasets with Pandas. Iterate over cells and texts; Generate clouds of words using WordCloud and average frequency plots.
Week 5 - Sentiment analysis. Overview of techniques used to implement sentiment analysis; Implementation examples.
Week 6 - Introduction to Web scraping; How to read and extract information from a website; Using BeautifulSoup to read HTML and XML files.
Week 7 - Iteratively collect information from a website using loops; Implementation of the sentiment analysis pipeline to online information.
Week 8 - Web scraping using API. What are APIs. How to use APIs to extract information from the internet (e.g. IMDB, Twitter); Implementation of the sentiment analysis pipeline to API information.
Week 9 - Culture as fossilized psychology. The challenge of analyzing historical texts. How to use psychometric tools to build valid bags-of-words; plot historical sentiment time series using Seaborn.
Week 10 - Handling Socioeconomical data. Surveying the major datasets for contemporary and historical socioeconomic estimates; Understanding the meaning and differences between socioeconomic variables and how they are affected by historical events; Extracting socio-economic data from datasets and adding them to a dataset of textual word frequencies.
Week 11 - Testing hypotheses on the relationship between historic events, socioeconomics, and word-frequencies. Quasi-experimental methods, time series trends, linear mixed models; Linear mixed model trend comparison, model selection with stepAIC; Implementation with R.
Week 12 - Practical session - Students work on their projects in the classroom.
Week 13 - Project presentations and Q&A
Week 14 - Practical session - Students work on their projects in the classroom.
Information
Aims, contents and method of the course
Assessment and permitted materials
40% Final Project Oral Presentation (Intro and Methods) and Q&A (Individual)
40% Writing up a Project in Article Style (Introduction, Methods, Results and Discussion) (Individual)
Minimum requirements and assessment criteria
Examination topics
1. Automatically scrape large numbers of texts from the internet
2. Use natural language processing tools to perform automatic text analysis, including sentiment analysis.
3. Quantify the change of values and preferences across time, its relationship with historical events and with socio-economic conditions using basic econometrics analysis tools.Learning activities
1. Lectures: students engage in discussions about the readings and the theory provided by the professors.
2. Practical work: students will aim to design experiments individually (this will also include training students in R and Python).
3. Student Activities (Individual/group): students develop their final projects and work on their assignment.
Reading list
- https://www.youtube.com/watch?v=nLRL_NcnK-4&t=7492sComputational 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/10.4324/9781003025245Historical 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/