Computational social science (CSS) encompasses computational methods applied to social sciences, addressing issues like micro-macro problems and the challenges of observing and experimenting with social phenomena. It utilizes data from various sources, including social media, big data, and simulations to understand social dynamics, such as residential segregation and public health issues. This document outlines key areas in CSS, including simulation, big data analytics, and social network analysis, along with examples illustrating these concepts.

1. Computational Social
Science
Kiarash Kiani

2. What Is CSS?
Computational social science refers to the academic
sub-disciplines concerned with computational
approaches to the social sciences.

3. Scientific Methods
Generate
Hypothesis
Design
Experiment
Measure
Things
Test
Hypothesis

4. Bigger Problems
• Social phenomena involve many individuals interacting to
produce collective entities
- Firms, markets, cultures, political parties, social movements,
audiences
- “Micro-Macro” problem (aka “Emergence”)
• Micro-Macro problems are hard to study empirically
- Difficult to collect observational data about individuals, networks,
and population at the same time
• Difficulty of manipulating large-scale social/organizations
experimentally

5. Solutions
• Web as a record of social
interactions
- Public web pages / discussions
- Twitter, Facebook, Instagram, Flickr,
blogs, Spotify, wikis
- Private emails, WhatsApp, Slack, Line
- Text, image, sound: speeches, news,
commercials
• Big Data
- GIS data: satellite, GPS
- Sensor data: video surveillance, smart
phones, wearables, mobile apps

6. The Main Areas of CSS
• Simulation
• Big Data
• Social Network Analysis
• Virtual Lab-Style Experiments

7. Simulation

8. Residential Segregation
Model
• Model based on Schelling’s
(1971) “Dynamic Models of
Segregation.”
• Good example of emergent
phenomena based on simple
rules at the neighborhood scale.
• Macro-level segregation does
not necessarily reflect micro-
level preferences.
• However there are criticisms of
it e.g. neighborhood tolerances.
Simulation

9. Extending Opinion Dynamics to
Model Public Health Problems
• Smoking is responsible for 18.1% of
total deaths in 2000
• Strong correlations between smoking
and social network relationships
• Youth psychosocial motivations:
- rebellion
- independence
- adulthood
Simulation
Thomas W. Moore, Patrick D. Finley, John M. Linebarger, Alexander V. Outkin, Stephen J. Verzi, Nancy S. Brodsky, Daniel
C. Cannon, Aldo A. Zagonel, and Robert J. Glass
70%
33%
10%
Wish to quit
Attempt to quit
Success

10. Big Data

11. Predicting Poverty and Wealth
From Mobile Phone Metadata
• Database of 1.5 million customers of Rwanda’s
largest mobile phone provider
• About 1,000 Rwandans received a call
• Had the complete call records for all 1.5 million
people
• Machine learning model to predict a person’s wealth
based on their call records
• Used this model to estimate the wealth of all 1.5
million customers
• Estimated the places of residence of all 1.5 million
customers using the geographic information
embedded in the call record
• Produce high-resolution maps of the geographic
distribution of wealth in Rwanda
Big Data

12. Social Network
Analysis

13. State of the Union
Addresses

14. Virtual Lab-Style
Experiments

15. Facebook “I Vote” Button
Direct messages to 61 million
Facebook users
1. Informational: 1% users
received
2. Social: 98%
3. Control group: 1% (no
message received)
Virtual Lab-Style Experiments
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person
experiment in social influence and political mobilization. Nature, 489(7415), 295–298. https://doi.org/10.1038/nature11421