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Course title |
Social Media and Social Network Analysis |
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Semester |
111-2 |
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Designated for |
COLLEGE OF SOCIAL SCIENCES GRADUATE INSTITUTE OF JOURNALISM |
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Instructor |
Adrian Rauchfleisch |
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Curriculum Number |
JOUR7094 |
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Curriculum Identity Number |
342EM3100 |
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Class |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Elective |
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Time |
Monday 7,8,9(14:20~17:20) |
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Remarks |
Restriction: MA students and beyond
The upper limit of the number of students: 15.
The upper limit of the number of non-majors: 5. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
IMPORTANT: IF YOU COULD NOT BOOK THE CLASS - USE THIS FORM - WILL THEN GET IN TOUCH WITH YOU: https://forms.gle/4U1Fqb2S3Wnm2XW96
You can also directly contact me: adrian.rauchfleisch@gmail.com
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 Twitter using R and techniques for working with text data, such as using regular expressions. 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. |
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Course Objective |
Introduction to R
Data analysis and visualization of digital trace data
Twitter data can be collected automatically
Learn new methods
Text mining |
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Course Requirement |
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Student Workload (expected study time outside of class per week) |
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Office Hours |
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Designated reading |
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References |
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.
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Grading |
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