Big data analytics

A.Y. 2020/2021
9
Max ECTS
60
Overall hours
SSD
SPS/04
Language
English
Learning objectives
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.
Expected learning outcomes
By the end of the course students will learn how - effectively use statistical methods on texts for social scientific research; - discuss the advantages, but also the limits, of each approach. Course evaluation aims to verify the expected learning outcomes in relation to these topics.
Course syllabus and organization

Single session

Responsible
Lesson period
Second trimester
If necessary, on-line lessons will be held on the Microsoft Teams platform and can be followed synchronously on the basis of the second quarter timetable.
Course syllabus
Big data are those labeled, for strange reasons, with the capitalized "Big". Nevertheless, they are still "data", altough with some specific characteristics: large volume, high frequency and, most notably, unpredictability - data come in the many different forms, they are raw, messy, unstructured, not ready for processing, and so on. Still, these data convey a lot of information to social scientists and good statistical techniques are required in order to extract meaningful results from them. In this workshop we will focus on a specific type of Big data, namely digital texts, both from social media as well as other sources (such as legislative speeches or electoral programs). The aim is to provide an introductory guide to this exciting new area of research, while also offering guidelines on how to effectively use statistical methods on texts for social scientific research by discussing the advantages, but also the limits, of each approach. The attention will be devoted to three main areas: 1) scaling methods that allow to estimate the location of actors in some policy space; 2) supervised classification methods, including machine learning algorithms, that allow to organize texts into a set of pre-defined categories; 3) unsupervised classification that allow to discover new ways of organizing texts into a set of unknown categories.
Prerequisites for admission
An elementary knowledge of R, plus a curiosity towards applied statistics, are good prerequisites for the lab sessions.
Teaching methods
Lab sessions are a crucial part of the course: they are offered for "hands-on" experiences to learn the techniques and the statistical methods discussed during classes. All the datasets, replication files of the lab sessions and reference texts will be made available at a dedicated URL before the beginning of the course. Enrolled students should bring their own laptop with R, RStudio and the relevant packages previously installed and functioning (instructions will be circulated beforehand).
Teaching Resources
Benoit, Kenneth. 2020. Text as Data: An Overview. In: Luigi Curini and Robert Franzese, Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, 461-497

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.

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

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.

Curini, Luigi, Hino, Airo, and Atsushi Osaki. 2020. Intensity of government-opposition divide as measured through legislative speeches and what we can learn from it. Analyses of Japanese parliamentary debates, 1953-2013, Government and Opposition, Volume 55, pp. 184-201

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

Curini, Luigi, and Robert Fahey. 2020. Sentiment Analysis. In: Luigi Curini and Robert Franzese, Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, 534-551

Barberá, Pablo and C. Steinert-Threlkeld Zachary. How to Use Social Media Data for Political Science Research. In: Luigi Curini and Robert Franzese, Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, 404-423

Further readings can be suggested during the course. Please check regularly the home-page of the course on Ariel.
Assessment methods and Criteria
For enrolled students, course grades will be based on home-assignments and class-participation. Instructions for not-enrolled students will be circulated later.
SPS/04 - POLITICAL SCIENCE - University credits: 9
Lessons: 60 hours
Professor(s)
Reception:
Wednesday 14:00-17:00
room 319 - via Conservatorio 7, Department of Social and Political Sciences. The office-hours will be held on-line. Plz write directly to the teacher to make an appointment