課程資訊
課程名稱
大數據與政治觀測專題
Seminar on Big Data and Political Observation 
開課學期
106-1 
授課對象
社會科學院  政治學系  
授課教師
周韻采 
課號
PS5684 
課程識別碼
322 U2020 
班次
 
學分
2.0 
全/半年
半年 
必/選修
選修 
上課時間
星期二3,4(10:20~12:10) 
上課地點
社科研605 
備註
總人數上限:35人
外系人數限制:5人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1061PS5684_bigdata 
課程簡介影片
 
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課程概述

本課程首先介紹大數據(主要為社群媒體)的內涵與性質。其次,課程討論社群媒體分析中最重要的部分—情緒分析。課程接著以實作方式介紹大數據分析的各個步驟,從資料篩檢、處理、整併到預測。最後,本課程以討論大數據分析目前應用的限制及未來方向總結。
This course begins with the introduction of concepts and texture of big data, and specifically, social media. Then, it discusses the critical part of doing social listening, sentiment analysis. It continues to illustrate the big data analysis step by step, from data verification to predication. Finally, we conclude the course with the discussion of the current limits of big data analytics in social sciences and the path should be paved in the future.

三、每週進度及教學內容簡述
01. 09/12 – introduction(概論)
[No. 1]
02. 09/19 –World Congress of Girl Scouts and Guiding (NO CLASS)
03. 09/26 – big data types and generation(大數據種類與原理)
[No. 2] [No. 3]
{case study: location based data}
{case study: consumer purchase data}
04. 10/03 – social media as the opinion platform(社群媒體作為觀測工具)
[No. 4] [No. 5] [No. 6] [No. 7]
{case study: Facebook}
{case study: twitter}
05. 10/10 – Double Tenth Day (NO CLASS)
06. 10/17 – sentiment analysis(情緒分析 I)
[No. 8] [No. 9] [No. 10]
07. 10/24 – two-dimensional sentiment analysis II(二維情緒分析暨實作)
[No. 11] [No. 12]
{case study: CVAW}
08. 10/31 – aggregate anonymous data and privacy protection(去識別化資料與隱私保護)
{case study: facebook topic data}
{case study: google trend}
09. 11/07 -- data filtering and accuracy verification(資料篩檢與確認)
{case study: OTT}
{case study: 桃機淹水}
10. 11/14 -- data processing and integration(資料處理與來源整併)
{case study: VoIP}
{case study: game industry}
11. 11/21 – Midterm Assignment Due (NO CLASS)
12. 11/28 – modeling and predication(模型與預測)
[No. 13] [No. 14] [No. 15]
13. 12/05 -- campaign predication and practice(選舉預測暨實作)
[No. 16] [No. 17]
{case study: the 2016 general election}
{case study: KMT chairman election 2017}
14. 12/12 – cacophony: algorithms and keyword setting(影響大數據分析效度的原因)
[No. 18] [No. 19]
{case study: machine interactions}
{case study: fake sources}
15. 12/19 – the up-to-date domestic research (國內政治學界研究現況)
[No. 20]
 

課程目標
本課程介紹大數據分析的方法及應用於政治事件的可行性。課程同時以理論及實證面向,引領同學學習。本課程修畢後,同學可以本課程教授方法分析其他政治事件。
This course introduces the methods of big data analytics and probable applications of big data in analyzing politics/policy related events. It covers both theoretical and empirical dimensions in using big data analytics. The students should be able to apply the methods to the study of real-world events by taking this course.
 
課程要求
課堂出席 15%
課堂發言表現 10%
口頭報告 20%
期中作業 20%
期末報告 35%
 
預期每週課後學習時數
 
Office Hours
另約時間 備註: 以email約時間ychoutotochu@gmail.com 
參考書目
待補 
指定閱讀
**每週閱讀範圍對照如上欄的「三、每週進度與教學內容簡述」
指定閱讀:
1. McKinsey Global Institute (2011). Big data: The next frontier for innovation, competition, and productivity
2. Bohannon, John (2017). The pulse of the people: Can internet data outdo costly and unreliable polls in predicting election outcomes. Science 355 (6324), 470-472.
3. Monroe, Burt L. et al (2015). No! Formal theory, causal inference, and big data are not contradictory trends in political science. APSA Symposium.
4. Bollen et al (2013). Twitter mode predicts stock mood.
5. Monaco, Nicholas J. (2017). Computational propaganda in Taiwan: Where digital democracy meets automate autocracy, working paper No. 2017.2
6. Wen, Wei-Chun (2014). Facebook political communication in Taiwan: 1.0/2.0 messages and election/ post-election messages. Chinese Journal of Communication, 7:1, 19-39.
7. Teta Stamati, Thanos Papadopoulos & Dimosthenis Anagnostopoulos (2015). Social media for openness and accountability in the public sector: Cases in the Greek context. Government Information Quarterly, 32, 12–29.
8. Kim, S. M.; Hovy, E. H. (2006). Identifying and analyzing judgment opinions. Proceedings of the Human Language Technology/North American Association of Computational Linguistics conference (HLT-NAACL 2006).
9. Pang, Bo & Lillian Lee (2008). Opinion Mining and Sentiment Analysis. Now Publishers Inc.
10. Liu, Bing (2010). Sentiment analysis and subjectivity. In Indurkhya, N.; Damerau, F. J. Handbook of Natural Language Processing.
11. Cambria, Erik, Björn Schuller, Yunqing Xia & Catherine Havasi (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems 28 (2): 15–21.
12. Yu, Liang-Chih et al (2016) Building Chinese affective resources in valence-arousal dimensions. Proceedings of NAACL-HLT 2016, pages 540–545.
13. Hal R. Varian (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2):3–28.
14. David W. Nickerson and Todd Rogers (2014). Political campaigns and big data. Journal of Economic Perspectives, 28(2):51–74.
15. Einav, Liran & Jonathan D. Levin (2013). The data revolution and economic analysis, NBER Working Paper 19035.
16. Bond, Robert & Solomon Messing (2015). Quantifying social media’s political space: estimating ideology from publicly revealed preferences on Facebook. American Political Science Review, 109(1): 62-78.
17. Sheng Yu and Subhash Kak (2015) A Survey of Prediction Using Social Media
18. Napoli, P.M. (2015). Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers. Telecommunications Policy, http://dx.doi.org/10.1016/j.telpol.2014.12.003i
19. Lazer, D., R. Kennedy, G. King, & A. Vespignani (2014). The parable of Google Flu: Traps in big data analysis. Science, 343 (6176): 1203–1205.
20. 電子治理研究中心 (2017) Web 2.0時代的民意探勘: 政府部門網路輿情分析的概念與實務. 台北: 國家發展委員會
21. Anjali Kaushik & Aparna Raman (2015) The new data-driven enterprise architecture for e-healthcare: Lessons from the Indian public sector. Government Information Quarterly, 32, 63–74.
22. Grimmer, Justin (2015). We are all social scientists now: How big data, machine learning, and causal inference work together. APSA Symposium.
23. Zeynep Tufekci (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media
24. supplementary material (available on the virtual classroom)

延伸閱讀:
1. Noveck, Beth S. (2015) Smart Citizens, Smarter State: The Technologies of Expertise and the Future of Governing. Boston: Harvard University Press
2. Soares, Sunil (2012) Big Data Governance: An Emerging Imperative. Boise: MC Press Online
3.
參考資料網站:
Kaggle: https://www.kaggle.com/


 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
9/12  introduction (概論) 
第2週
9/19  international conference (NO CLASS) 
第3週
9/26  big data types and generation(大數據種類與原理) 
第4週
10/03  social media as the opinion platform(社群媒體作為觀測工具) 
第5週
10/10  雙十節 (NO CLASS)
 
第6週
10/17  sentiment analysis(情緒分析 I) 
第7週
10/24  two-dimensional sentiment analysis II(二維情緒分析暨實作) 
第8週
10/31  aggregate anonymous data and privacy protection(去識別化資料與隱私保護) 
第9週
11/07  CASBAA conference (NO CLASS)
(W1~W8 PPT) 
第10週
11/14  data filtering and accuracy verification(資料篩檢與確認) 
第11週
11/21  data processing and integration(資料處理與來源整併) 
第12週
11/28  modeling and predication(模型與預測)
MIDTERM ASSIGNMENT DUE 
第13週
12/05  election predication and practice(選舉預測暨實作)
W1~W13 ppt 
第14週
12/12  measuring the effects of policy marketing(政策行銷效果分析) 
第15週
12/19  the up-to-date domestic research (國內政治學界研究現況) 
第16週
12/26  cacophony: algorithms and keyword setting(影響大數據分析效度的原因) 
第17週
1/02  limits and future of big data analytics(侷限與無限) 
第18週
1/9  TERM PROJECT due