Social data science – machine learning in the humanities and social sciences (MASSHINE Summerschool)

Course organizer (name, department and research group):

MASSHINE 

Title and date of the course: 

“Social data science – machine learning in the humanities and social sciences”. 26 – 30 August 2024

Location:

 Hotel Højgaarden, Slettestrandvej 50, 9690 Fjerritslev

Lecturer(s):

 Associate Professor Roman Jurowetzki (Aalborg University Business School)

Associate Professor Rolf Lyneborg Lund (Department of Sociology and Social Work, Aalborg University)

Associate Professor Anders Kristian Munk (Department of Culture and Learning, Aalborg University)

Professor Birger Larsen (Department of Communication and Psychology)

Assistant Professor Mathieu Jacomy (Department of Culture and Learning, Aalborg University)

Course description, incl. learning objectives and prerequisites:

The developments in computer science technologies and the increasing amount of accessible data present a range of new methodological opportunities for the social sciences and humanities.

Data from websites, social media and electronic devices (often referred to as ‘Big Data’) allow for new approaches and perspectives on issues relevant for both the social sciences and humanities. Meanwhile, the increasing computational power and development of artificial intelligence algorithms provide the means for accessing, combining and analyzing a variety of data types (numerical, textual, relational) in new and meaningful ways.

 This course is a hands-on practical introduction with no prerequisites in applying computer science techniques (like programming and machine learning) in humanities and social science research. It will cover a broad range of techniques and methods representing the latest methodological innovations in social science and humanities applications of machine learning and artificial intelligence.

Some techniques include:

·        Collecting data from the web using web scraping methods and API’s

·        Processing textual data for quantitative analysis (Natural Language Processing)

·        Working and visualizing networks (network analysis)

·        Dimensionality reduction and clustering techniques (topic models and k-means clustering)

·        Visualization techniques for text data and networks

·        Building and understanding machine learning classifiers

This course is meant as a hands-on tools course focusing on the practical use of these methods and will not go in depth with the mathematical and theoretical foundations. It will rather provide a broad overview of the data science ecosystem and toolbox and enable immediate application.

Teaching methods:

Each day will consist of a mixture of lectures and exercises using interactive online notebooks allowing participants to try out and use the various methods as they are being taught.

Participants are expected to work on a portfolio during the week with each day having hours dedicated to portfolio work with the possibility of sparring with the course lecturers. Here, participants will work on applying the methods and techniques presented on various cases.

Description of paper requirements, if applicable:

The course teaches the methods in python using the Jupyter Notebook IDE on Google Colab.

It is not a prerequisite to know Python beforehand as access to relevant courses will be provided and the first day of the course provides the relevant introduction.

Participants are expected to complete assigned introductory e-courses (e.g. on DataCamp or other selected platforms) before the course. Access to DataCamp will be provided 4 weeks in advance. Two mandatory online check-in sessions are scheduled to properly prepare participants for the course.

Key literature:

Mandatory literature: VanderPlas, Jake. Python data science handbook: Essential tools for working with data. ” O’Reilly Media, Inc.”, 2016. https://jakevdp.github.io/PythonDataScienceHandbook/

Suggested literature:

Mitchell, R. (2018). Web scraping with Python: Collecting more data from the modern web. ” O’Reilly Media, Inc.”.

Alammar, J., & Grootendorst, M. P. (2024). Hands-On Large Language Models. O’Reilly Media, Inc. (forthcoming)

Number of ECTS:

5