Learning Materials

Welcome to Boot Camp

2:44

Installing R and RStudio

14:01

R Basics

17:36

Data “Wrangling”

22:45

Visualization

30:54

Basic Programming

28:32

Modeling

14:32

Communicate and Collaborate

23:16

Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions

1:22:38

Academic Research with the Twitter API v2

1:00:49

Tutorial 7: Analyzing Conversations in Python Using ConvoKit

57:22

Behind the Bot Study

46:06

Tutorial 5: BERT for Computational Social Scientists

1:00:15

Tutorial 4: Beyond the Bag of Words: Text Analysis with Contextualized Topic Models

59:45

Common Pitfalls in Quantitative Research

1:15:58

Tutorial 1: Comparing Word Embedding Models

1:00:41

Tutorial 3: Controlling for Text in Causal Inference with Double Machine Learning

1:02:06

Data Ethics Section 1: Data isn't just data, and ethical considerations

52:55

Data Ethics Section 2: Ethical Considerations and Case Study

52:03

Digital Trace Data Case Studies Using Social Media Advertising Data

1:04:52

Digital Trace Data Introduction

1:00:36

Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier

1:32:04

Experimental Possibilities of Generative Language Models

32:44

Computational Advances in Social Science Experiments

1:09:58

How to Get Grants in Computational Social Science

42:13

How to get a Job in Computational Social Science

55:07

How to Publish an Article in Computational Social Science

52:45

How to Publish a Book in Computational Social Science

32:40

How to Publish a Computational Social Science Paper?

1:31:15

Introduction to Computational Social Science

22:21

Measurement and Causal Inference Using Text as Data

1:03:47

Modeling Temporal Communication Networks and Dynamical Processes

1:26:56

Tutorial 6: Moving from words to phrases when doing NLP

57:46

Tutorial 8: Preprocessing Social Media Text

59:22

Randomized Field Experiments in Online Learning

1:38:46

Representing Text as Data

58:55

Social network analysis: research designs

1:08:20

Why SICSS? 2022

26:02

Introduction to Computational Social Science

27:18

Why SICSS?

25:33

Ethics: Part 1

33:33

Ethics: Part 2

34:21

Ethics Additions and Extensions

18:57

What is Digital Trace Data?

10:52

Strengths and Weakness of Digital Trace Data

31:40

Application Programming Interfaces

49:51

Screen Scraping

25:22

Building Apps and Bots for Social Science Research

27:50

An Introduction to Text Analysis

9:43

Dictionary-Based Text Analysis

19:25

Text Analysis Basics

28:07

Topic Models

26:38

Text Networks

21:01

Survey Research in the Digital Age

18:35

Probability and Non-Probability Sampling

19:35

Computer-Administered Interviews

9:18

Combining Surveys and Big Data

20:50

Additions and Extensions

36:20

Introduction to Mass Collaboration

6:04

Human Computation

18:23

Open Call

13:19

Distributed Data Collection

15:13

Fragile Families Challenge

28:33

What, Why, and Which Experiments?

12:46

Moving Beyond Simple Experiments

17:31

Four Strategies for Making Experiments Happen

12:46

Zero Variable Cost Data and Music Lab

25:43

3Rs

12:07

Bonus Lectures by Leaders in the Field

Tutorial 2: Extracting Information from Documents

58:19

Video as Data by Kaiping Chen at SICSS-NDSU 2022

45:05

Computation in Context by Saif Shahin at SICSS-NDSU 2022

44:33