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Course title |
Social Media and Social Network Analysis |
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Semester |
110-1 |
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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 |
Friday 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: 20. |
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Ceiba Web Server |
http://ceiba.ntu.edu.tw/1101socne |
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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/7DnAcohdJKcGyYct5
IMPORTANT: YOU WILL DIRECTLY VIA EMAIL RECEIVE THE URL TO THE VIDEO CALL ON FRIDAY 24 SEPTEMBER
You can also directly contact me: adrian.rauchfleisch@gmail.com
The course introduces the analysis of social media data with a particular focus on social networking analysis. Students learn how to use the R programming language to collect, process, and analyze digital trace data in the course. The course focuses on practical examples that can also be used in data-driven journalism or business analytics. The course starts with a general introduction to R. In the second block, students learn to read data, perform statistical procedures, and visualize results in high-quality plots. In the third block, students learn how to collect data from Twitter automatically via R. Students are specially prepared for the challenging work with texts (for example, regular expression). In the fourth block, the students plan their own projects. At the end of the course, some state-of-the-art methods are presented in the form of an outlook. |
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Course Objective |
-learn basic coding skills in R
-learn the basics of network analysis
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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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Week |
Date |
Topic |
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Week 1 |
9/24 |
Introduction |
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Week 2 |
10/01 |
Basics in R & What are social media? |
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Week 3 |
10/08 |
Ethical challenges & Twitter AP |
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Week 4 |
10/15 |
Visualize data |
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Week 5 |
10/22 |
Process digital trace data |
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Week 6 |
10/29 |
Presentations and research designs |
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Week 7 |
11/05 |
Introduction social network analysis |
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Week 8 |
11/12 |
Twitter network analysis and work with Gephi |
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Week 9 |
11/19 |
Advanced network analysis |
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Week 10 |
11/26 |
Digital methods and text mining |
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Week 11 |
12/03 |
Brainstorming and elevator pitches |
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Week 12 |
12/10 |
Presentations proposals |
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Week 13 |
12/17 |
Guest speaker |
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Week 14 |
12/24 |
Scraping online data |
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Week 15 |
12/31 |
Holiday |
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Week 16 |
1/07 |
Final presentations |