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Preface
1
Introduction
1.1
Script & Material
1.2
About me
1.3
Who are you?
1.4
Content & Objectives
1.5
Overview of some readings
1.6
Tools and software we use
1.6.1
R: Why use it?
1.6.2
R: Where/how to study?
1.6.3
R: Installation and setup
1.6.4
Datacamp
1.6.5
Google Cloud
1.7
Descriptive inference, causal inference & prediction
1.7.1
Descriptive questions
1.7.2
Causal questions
1.7.3
Prediction
1.8
The digital revolution
1.9
How does the internet work? (+ access)
1.10
Technology adoption: United States
1.11
Platform usage (1): Social Media Adoption
1.12
Platform usage (2): Social Media Adoption (Barchart)
1.13
Platform usage (3): Social Networking Young
1.14
Platform usage (4): Daily hours digital media
1.15
What is Computational Social science (CSS)?
1.16
CSS: Challenges for Social Scientists
1.17
Exercise: What data can reveal about you…
1.18
Exercise: Documentaries by Deutsche Welle
1.19
X-Exercise: Farecast and Google Flu
1.20
X-Exercise: Big Data is not about the data
1.21
X-Exercise: Download your data!
1.22
Presentations: Example Barbera (2015)
1.23
Good practices in data analysis (X)
1.23.1
Reproducibility & Replicability
1.23.2
Reproducibility & Replicability
1.23.3
Why reproducability?
1.23.4
Reproducability: My current approach
1.23.5
Reproducability in practice
1.24
References
2
Big data & new data sources (1)
2.1
For a starter
2.2
What is Big Data?
2.3
Big data: Quotes for a start
2.4
Big data: Definitions
2.5
Big data: The Vs
2.6
Big data: Analog age vs. digital age (1)
2.7
Big data: Analog age vs. digital age (2)
2.8
Big data: Repurposing
2.9
Presentations
2.10
Exercise: Ten common characteristics of big data (Salganik 2017)
2.11
New forms of data: Overview
2.12
Where can we find big data sources data?
2.13
References
3
Big data & new data sources (2)
3.1
Presentations
3.2
Example: Salience of issues
3.3
Google trends: Caveats
3.4
Data security & ethics (1): What might happen?
3.5
Data security & ethics (2): Yes…
3.6
Data security & ethics (3): Protection
3.7
Data: Size & dimensions & creation
3.8
Data: How is it stored?
3.9
Data & Databases
3.10
R Database Packages
3.11
SQL: Intro
3.12
SQL: Components of a query
3.13
Lab: Working with a SQL database
3.13.1
Creating an SQL database
3.13.2
Querying an SQL database
3.13.3
Querying multiple SQL tables
3.13.4
Grouping and aggregating
3.14
Exercise: SQL database
3.15
SQL at scale: Strategy
3.16
SQL at scale: Google BigQuery
3.17
Lab (Skip!): Setting up GCP research credits
3.17.1
Google Translation API
3.18
Lab: Google Big Query
3.19
Exercise: Setting up & querying Google BigQuery
3.20
References
4
Data collection: APIs
4.1
Web APIs
4.2
API = Application Programming Interface
4.3
Why APIs?
4.4
Scraping: Decisions, decisions…
4.5
Types of APIs
4.6
Some APIs
4.7
R packages
4.8
(Reponse) Formats: JSON
4.9
(Reponse) Formats: XML
4.10
Authentication
4.11
Connect to API: Example
4.12
Lab: Scraping data from APIs
4.13
Exercise: Scraping data from APIs
4.13.1
Homework: APIs for social scientists
4.14
Extracting data from PDF files
4.15
X-Lab: Clarify API
4.16
X-Twitter’s APIs
4.17
X-Lab: Twitter’s streaming API
4.17.1
Authenticating
4.17.2
Collecting data from Twitter’s Streaming API
4.18
X-Exercise: Twitter’s streaming API
4.19
X-Lab: Twitter’s REST API
4.19.1
Searching recent tweets
4.19.2
Extracting users’ profile information
4.19.3
Building friend and follower networks
4.19.4
Estimating ideology based on Twitter networks
4.19.5
Other types of data
4.19.6
Checking for bots
4.20
X-Exercise: Twitter’s REST API
4.21
Extracting data from PDF files
5
Data collection: Web (screen) scraping
5.1
Web scraping: Basics
5.1.1
Scraping data from websites: Why?
5.1.2
Scraping the web: two approaches
5.1.3
The rules of the game
5.1.4
The art of web scraping
5.2
Screen (Web) scraping
5.2.1
Scenarios
5.2.2
HTML: a primer
5.2.3
HTML: a primer
5.2.4
Beyond HTML
5.2.5
Parsing HTML code
5.3
Lab: Scraping tables
5.4
Exercise: Scraping tables
5.5
Lab: Scraping (more) tables
5.6
Exercise: Scraping (more) tables
5.7
Lab: Scraping unstructured data
5.8
Exercise: Scraping unstructured data
5.9
Scrape dynamic webpages: Selenium
5.10
Lab: Scraping web data behind web forms
5.10.1
Using RSelenium
5.11
Exercise: Scraping web data behind web forms
5.12
RSS: Scraping newspaper websites
5.13
Lab: Scraping newspaper website
6
Machine learning: Introduction
6.1
Classical statistics vs. machine learning
6.2
Terminological differences
6.3
Mean as a model
6.4
Linear model (Equation)
6.5
Linear model (Visualization)
6.6
Estimation
6.7
Prediction
6.8
Prediction vs. classification
6.9
Exercise
6.10
Exercise: Discussion
6.11
What is an ML algorithm?
6.12
Prediction example: Dressel and Farid (2018)
6.13
Classifying ML algorithms
6.14
Supervised machine learning (SML)
6.15
Some SML techniques
6.16
SML social science applications
6.17
Unsupervised machine learning
6.18
SOME UML techniques
6.19
Machine Learning: New answers to old questions
6.20
FUTURE ISSUES
6.21
Lab: Simple examples of SML and UML
6.22
Dangers of ML
6.23
Lab 13: Sentiment analysis
Facebook
Twitter
LinkedIn
Weibo
Instapaper
A
A
Serif
Sans
White
Sepia
Night
Computational Social Science: Theory & Application
6.12
Prediction example: Dressel and Farid (2018)
Use table from introductory class with predicted values