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課程名稱 |
社群媒體與社會網絡分析
Social Media and Social Network Analysis |
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開課學期 |
108-2 |
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授課對象 |
社會科學院 新聞研究所 |
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授課教師 |
劉好迪 |
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課號 |
JOUR7094 |
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課程識別碼 |
342EM3100 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期三7,8,9(14:20~17:20) |
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上課地點 |
新聞401 |
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備註 |
本課程以英語授課。
限碩士班以上
總人數上限：20人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
The course gives an introduction to the analysis of social media data with a particular focus on social networking analysis. In the course, students learn how to use the R programming language to collect, process and analyze digital trace data. The course focuses on practical examples that can also be used in data-driven journalism. The course starts with a general introduction to R. In a second block, students learn how 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 or Facebook automatically via R. Students are specially prepared for the challenging work with texts (for example, regular expression). In a fourth block, the students plan their own project. At the end of the seminar, some state-of-the-art methods are presented in the form of an outlook. |
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課程目標 |
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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課程要求 |
Propose questions for discussion (10%)
- Readings: read the literature for every class
- Send 1 day before class 1 question for discussion – explain in 5 sentences why the question is important.
2. Proposal research project (20%)
- Use the template
- Find additional literature
- 2 Pages
3. Presentation (30%)
- Organizational information: Begin your preparations immediately after choosing a topic. The literature is available for download on the learning platform.
- - Content: Please include additional literature. Think about what you want to convey. Do not present anything you did not understand. Find links to other topics in the seminar and present some empirical examples.
- - Didactic notes: Think about the time. Speak freely. Decide which didactic instruments you want to use. Use interactive elements. Look for examples of the subject and discuss them after the presentation. If possible, find examples from Taiwan.
- Discuss your ideas with me. In any case, discuss your plan with me at least one week before the presentation.
- Timing: 20 minutes presentation / 10 minutes discussion examples / 10 minutes Q&A
4. Paper/Own Study (40%)
- approx. 10 pages per person, print and PDF version (PDF without copy protection)
- Please discuss the subject with me in advance. - must comply with scientific standards (incorporate relevant literature and critically summarize, independently search further, especially current literature also from academic journals; established citation style, for example, APA style) |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
指定閱讀
Arlt, D., Rauchfleisch, A., & Schafer, M.S. (forthcoming): Polarization or dialogue? Political debate on Twitter in the wake of the Swiss referendum on the Nuclear Withdrawal Initiative. Environmental Communication.
Ausserhofer, J., & Maireder, A. (2013). NATIONAL POLITICS ON TWITTER. Information, Communication & Society, 16(3), 291–314. doi:10.1080/1369118X.2012.756050
Chang, W. (2013). R graphics cookbook (First edition). Beijing, Cambridge, Farnham, Koln, Sebastopol, Tokyo: O'Reilly.
Easley, D., & Kleinberg, J. (2010). Networks, crowds and markets: Reasoning about a highly connected world. Cambridge: Cambridge Univ. Press.
Kaiser, J., Rhomberg, M., Maireder, A., & Schlogl, S. (2016). Energiewende?s Lone Warriors: A Hyperlink Network Analysis of the German Energy Transition Discourse. Media and Communication, 4(4), 18. doi:10.17645/mac.v4i4.554
Maireder, A., & Schlogl, S. (2014). 24 hours of an #outcry: The networked publics of a sociopolitical debate. European Journal of Communication, 29(6), 687–702. doi:10.1177/0267323114545710
Maireder, A., Weeks, B. E., Gil de Zuniga, Homero, & Schlogl, S. (2016). Big Data and Political Social Networks. Social Science Computer Review, 35(1), 126–141. doi:10.1177/0894439315617262
Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, tidy, transform, visualize, and model data. s.l.: O'Reilly UK Ltd.
延伸閱讀 |
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評量方式
(僅供參考) |
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