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  • Even after classes have commenced, course descriptions and online syllabus information may be subject to change according to the size of each class and the students' comprehension level.

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Course Information

Year 2018  School School of Political Science and Economics
Course Title
Advanced Topics in Political Science: Polimetrics - Applied Scaling & Classification Techniques in Political Science

Instructor CURINI, Luigi
Term/Day/Period fall quarter  01:Wed.1/02:Wed.5
Category Non-Seminar Eligible Year 3rd year and above Credits 2
Classroom 01:3-903 (SPSE PC Room)/02:3-903 (SPSE PC Room) Campus waseda
Course Key 11G100SA05 Course Class Code 01
Main Language English
  Course Code POLX301L
First Academic disciplines Political Science
Second Academic disciplines Political Science
Third Academic disciplines Political Science
Level Advanced, practical and specialized Types of lesson Lecture

Syllabus Information

Latest Update:2018/09/13 18:42:21

Course Outline The recent years have witnessed a dramatic increase in the interest towards the analysis of texts in social sciences. This is largely due to the development of new methods that facilitate substantively important inferences about politics from large text collections. This course aims to provide an introductory guide to this exciting new area of research, while also offering guidelines on how to effectively use text methods for social scientific research. The attention will be devoted to three main areas: 1) automated content methods that allow to estimate the location of actors in policy space (and the differences between these methods and the human-coded ones); 2) automated content methods that allow to organize texts into a set of pre-defined categories (with a particular emphasis on social media texts); 3) automated content methods that allow to discover new ways of organizing texts into a set of unknown categories. Lab sessions are a crucial part of the course: they are offered for “hands-on” experiences to learn the techniques discussed during the morning class. An elementary knowledge of R, plus a curiosity towards applied statistics , are good prerequisites for the lab sessions. All the datasets and replication files of the lab sessions will be made available at a dedicated URL.
Objectives Students will learn how to employ some widely discussed methods advanced in the literature to analyze political texts and to extract from them useful information for texting their own theories.
Course Schedule 1.Morning class (Theory): An introduction to textual analysis methods
2.Afternoon class (Lab): How to analyze texts with the Quanteda package
 
3.Morning class (Theory): From words to positions (Wordscores)
4.Afternoon class (Lab): How to implement the Wordscores algorithm using the Quanteda package
 
5.Morning class (Theory): From words to positions (Wordfish)
6.Afternoon class (Lab): How to implement the Wordfish algorithm using the Quanteda package
 
7.Morning class (Theory): From Manifestoes to positions: The Comparative Manifesto Project
8.Afternoon class (Lab): How to extract party positions from party Manifestoes
 
9.Morning class (Theory): From words to issues: The structural topic Model
10. Afternoon class (Lab): How to implement the stm package
 
11. Morning class (Lab): How to retrieve information from social media (part 1)
12. Afternoon class (Lab): How to retrieve information from social media (part 2)
 
13.Morning class (Theory): Dictionaries and Supervised classification methods
14.Afternoon class (Lab): How to extract information from social media (part 1)
 
15.Morning class (Theory): Supervised aggregated classification methods
16.Afternoon class (Lab): How to extract information from social media (part 2)
Textbooks Please see below for the references.
Reference Classes 1 & 2: Grimmer, Justin, and Stewart, Brandon M. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts”. Political Analysis, 21(3): 267-297.

Classes 3 & 4: Laver, Michael, Kenneth Benoit, John Garry (2003). Extracting Policy Positions from political texts using words as data. American Political Science Review, 97(02), 311-331
Martin, Lanny W., and Georg Vanberg. 2008. A robust transformation procedure for interpreting political text. Political Analysis, 16: 93-100
Laver, Michael, and Kenneth Benoit. 2008. Compared to What? A Comment on “A Robust Transformation Procedure for Interpreting Political Text”, Political Analysis, 16: 101-111

Classes 5 & 6: Proksch, Sven-Oliber, and Slapin, Jonathan B. 2008. “A Scaling Model for Estimating Time-Series Party Positions from Texts”. American Journal of Political Science, 52(3): 705-722.
Proksch, Sven-Oliber, and Slapin, Jonathan B. 2009. “How to Avoid Pitfalls in Statistical Analysis of Political Texts: The Case of Germany”. German Politics, 18(3): 323-344.
Curini, Luigi, Hino, Airo, and Atsushi Osaki. 2018. Intensity of government–opposition divide as measured through legislative speeches and what we can learn from it. Analyses of Japanese parliamentary debates, 1953–2013 (with Airo Hino & Atsushi Osaka), Government and Opposition

Classes 7 & 8: Lowe, W., Benoit, K., Mikhaylov, S., & Laver, M. (2011). Scaling Policy Preferences From Coded Political Texts. Legislative Studies Quarterly, 26(1), 123-155.
Benoit, K., M. Laver, & S. Mikhaylov (2009). Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions. American Journal of Political Science, 53(2), 495-513
Huber, John and Matthew Gabel. (2000). Putting Parties in their Place: Inferring Party Left-Right Ideological Positions from Manifestos Data. American Journal of Political Science, 44 (1), 94-103

Classes 9 & 10: Robert, Margaret E., Brandon M. Stewart, Dustin Tingley, Christopher Luca, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, David G. Rand (2014). Structural Topic Models for Open-Ended Survey Response, American Journal of Political Science, 58(4), 1064-1082
Roberts, Margaret E., Brandon M. Stewart, Dustin Tingley(2014). STM: R Package for Structural Topic Models, Journal of Statistical Software, https://cran.r-project.org/web/packages/stm/vignettes/stmVignette.pdf

Classes 13 & 14: Grimmer, Justin, and Stewart, Brandon M. (2013). “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts”. Political Analysis, 21(3): 267-297
Curini Luigi, Andrea Ceron, and Stefano M. Iacus (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London: Routledge, 2017, Chapter 2

Classes 15 & 16: Curini Luigi, Andrea Ceron, and Stefano M. Iacus. “iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content”, Information Sciences, 367–368 (1), 2016, 105–124
Curini Luigi, Andrea Ceron, and Stefano M. Iacus (2018). ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters", Sage Open
Evaluation
Rate Evaluation Criteria
Papers: 85% Course grades will be based on home-assignments and class-participation.
Class Participation: 15% Course grades will be based on home-assignments and class-participation.
Note / URL http://www.luigicurini.com/applied-scaling--classification-techniques-in-political-science.html

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