While this course is primarily aimed at graduate-level students, undergraduates may be considered on a case-by-case basis under exceptional circumstances. IMPORTANT: IF YOU COULD NOT BOOK THE CLASS - USE THIS FORM - WILL THEN GET IN TOUCH WITH YOU: https://forms.gle/SeawN6pe6xaQkkcs9 This course provides an in-depth examination of social media data analysis focusing on social network analysis. You will learn how to utilize the R programming language to collect, process, and analyze digital trace data, with practical examples that can be applied in fields such as data-driven journalism and business analytics. The course begins with an introduction to R and progresses to cover topics such as reading data, performing statistical procedures, and visualizing results with high-quality plots. You will also learn how to collect data from social media platforms such as YouTube, Spotify, or PTT using R and techniques for working with text data. The course will also include a module on using transformer models for automatic text classification in Python, equipping you with the latest tools in machine learning for text analysis. In the final block of the course, you will have the opportunity to plan and work on your own project, and the course will conclude with a presentation of state-of-the-art methods in the field.

Introduction to R Data analysis and visualization of digital trace data Social media data can be collected automatically Learn new methods Text mining

This class is designed for beginners, with no specific requirements needed. The only prerequisites are an interest in learning about social media data analysis and a willingness to familiarize yourself with the R programming language

Beginners should expect to spend approximately 3 hours per week reviewing the code and completing assignments.

Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data (Second edition). O’Reilly. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press. Sanchez, G. (2013). Handling and processing strings in R. Berkeley: Trowchez Editions. http://gastonsanchez. com/Handling_and_Processing_Strings_in_R. pdf Wickham, H. (2019). Advanced R (Second edition). CRC Press/Taylor and Francis Group. Zweig, K. A. & Springer-Verlag. (2018). Network Analysis Literacy A Practical Approach to the Analysis of Networks.