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Course title |
Social Media and Social Network Analysis |
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Semester |
111-1 |
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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: 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/aUahUNn9GXPrdqKC9
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 |
Introduction to R
Data analysis and visualization of digital trace data
Twitter and Facebook 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 |
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Grading |
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