David Garcia, 2023
Welcome to the online materials for Social Media Data Analysis at the University of Konstanz.
Social media data analysis is introduced as a set of techniques to analyze human behaviour and social interaction through openly-available digital traces. The course focuses both on the fundamentals and applications of data science to social media, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective
This course focuses both on the fundamentals and applications of Data Science to Social Media in the Social Sciences. Through this course, students learn how to gather data from social media, search trends, and other online and offline sources, how to process and store that data, and how to combine, analyze, and visualize data to address specific questions. The course makes a special emphasis in interpretation and critique of Social Media Data Science in the Social Sciences, aiming at an interdisciplinary approach that can inform students from various disciplines.
I am the Professor for Social and Behavioral Data Science at the University of Konstanz. You can find more about my research group here: http://dgarcia.eu. My background is Computer Science but I worked my whole career with psychologists, sociologists and physicists to learn new ways to understand human behavior. I got my PhD from ETH Zurich in 2012 and a habilitation in 2018, starting to work as full professor TU Graz in 2020 and then at the University of Konstanz in 2022. To learn more about my research, check my publications.
The course is organized in lecture blocks with one practical exercise for each one. In the exercises you will apply part of what you have learned in the block. In exercises, you collect your own data to answer research questions.
- Introduction to social media data analysis within social data science -- [Slides]
- Algorithms and digital traces: The case of Google trends -- [Slides]
- Ethics and privacy in social media data analysis -- [Slides]
Exercise 1: Future orientation and search
- Social impact theory and its application to social media -- [Slides]
- Social trends and the Simmel effect -- [Slides]
Exercise 2: Testing the division of impact hypothesis in social media
- Dictionary methods in social media data analysis -- [Slides]
- Supervised social media text analysis -- [Slides]
- The measurement of meaning in social media -- [Slides]
Exercise 3: Validating sentiment analysis for social media text
- Introduction to social networks and the friendship paradox -- [Slides]
- Centrality measures in online social networks -- [Slides]
- Communities and assortativity -- [Slides]
- Social issues -- [Slides]
Exercise 4: Political assortativity on Twitter
- Handouts, codes, and data can be found on the Github repository of the course
- Updates and announcements will be distributed through the course Ilias: https://ilias.uni-konstanz.de/goto_ILIASKONSTANZ_crs_1559947.html
- The online materials do not contain the solutions to the exercises as they have to be submitted through the Github classroom. The link to github classroom can be found on the tutorial Ilias: https://ilias.uni-konstanz.de/goto_ILIASKONSTANZ_crs_1559949.html
Participants need to be familiar with programming in Python (e.g. having passed Introduction to Computation for the Social Sciences).
The course grade is composed of the grade of four assignments delivered during the semester (40%) and a final project (60%). For more information on projects and reports, see slides of session 09.