Lecture 1: Introduction
Content of this lecture
1. What is computational social science?
2. Computational communication science
3. Formalities of the course
4. Learning ‘R’ for automated text analysis
What is computational social science? … and why should we care?
Example: surprising sources of information
Example: wealth in Rwanda
Example: crime prediction
Computational social science
- Field of Social Science that uses algorithmic tools and large/unstructured data to understand
human and social behavior
- Computational methods as “microscope”: Methods are not the goal, but contribute to
theoretical development and/or data generation
- Complements rather than replaces traditional methodologies
- Includes methods such as, e.g.;
o Advanced data wrangling/data science
o Combining of different data sets
o Automated Text Analysis
o Machine Learning (supervised and unsupervised)
o Actor-based modelling
o Simulations
o …
1
,Typical workflow
Why is this important now?
- In the past, collecting data was expensive (surveys, observations…)
- In the digital age, the behaviors of billions of people are recorded, stored and therefore
analyzable.
- Every time you click on a website, make a call on your mobile phone, or pay for something
with your credit card, a digital record of your behavior is created and stored.
- Because (meta-)data are a byproduct of people’s everyday actions, they are often called
digital traces.
- Large-scale records of persons or business are often called big data.
10 characteristics of big data
Characteristic Description
1 Big The scale or volume of some current datasets is often impressive.
However, big datasets are not an end in themselves, but they can enable
certain kinds of research including the study of rare events, the
estimation of heterogeneity, and the detection of small differences.
2 Always-on Many big data systems are constantly collecting data and thus enable to
study unexpected events and allow for real-time measurement.
3 Nonreactive Participants are generally not aware that their data are being captured
or they have become so accustomed to this data collection that it no
longer changes their behavior.
4 Incomplete Most big data sources are incomplete, in the sense that they don’t have
the information that you will want for your research. This is a common
feature of data that were created for purposes other than research.
5 Inaccessible Data held by companies and governments are difficult for researchers to
access.
6 Nonrepresentative Most big datasets are nonetheless not representative of certain
populations. Out-of-sample generalizations are hence difficult or
impossible.
7 Drifting Many big data systems are changing constantly, thus making it difficult
to study long-term trends.
8 Algorithmically Behavior in big data systems is not natural; it is driven by the
confounded engineering goals of the systems.
9 Dirty Big data often includes as lot of noise (e.g., junk, spam, spurious data
points...)
10 Sensitive Some of the information that companies and governments have is
sensitive.
2
,Example data
Typical computational research strategies
1. Counting things
o In the age of big data, researcher can “count” more than ever
▪ How often do people use their smartphone per day?
▪ About which topics do news websites write most often?
2. Forecasting and nowcasting
o Big data allow for more accurate predictions both in the present and in the future
▪ Investigate when people disclose themselves in computer-mediated
communication
▪ Crime prediction
3. Approximating experiments
o Computational methods provide opportunities to conduct “natural experiments”
▪ Compare smartphone log data of people who use their smartphone naturally
vs. those who abstain from certain apps (e.g., social media apps)
▪ Investigate the potential of nudges to make users select certain news
Advantages and disadvantages
- Advantages of Computational Methods
o Actual behavior vs. self-report
o Social context vs. lab setting
o Small N to large N
- Disadvantage of Computational Methods
o Techniques often complicated
o Data often proprietary
o Samples often biased
o Insufficient metadata
Computational Communication Science: why computational methods are important for (future)
communication research…
Definition
“Computational Communication Science (CCS) is the label applied to the emerging subfield that
investigates the use of computational algorithms to gather and analyze big and often semi- or
unstructured data sets to develop and test communication science theories” (Van Atteveldt & Peng,
2018).
3
, Promises
The recent acceleration in the use of computational methods for communication science is primarily
fueled by the confluence of at least three developments:
- Vast amounts of digitally available data, ranging from social media messages and other
digital traces to web archives and newly digitized newspaper and other historical archives
- Improved tools to analyze this data, including network analysis methods and automatic text
analysis methods such as supervised text classification, topic modeling, word embeddings,
and syntactic methods.
- Powerful and cheap processing power, and easy to use competing infrastructure for
processing these data, including scientific and commercial cloud computing, sharing
platforms such as Github and Dataverse, and crowd coding platforms such as Amazon Turk
and Crowdflower
Example 1: simulating search queries
Example 2: analyze news coverage
4
Content of this lecture
1. What is computational social science?
2. Computational communication science
3. Formalities of the course
4. Learning ‘R’ for automated text analysis
What is computational social science? … and why should we care?
Example: surprising sources of information
Example: wealth in Rwanda
Example: crime prediction
Computational social science
- Field of Social Science that uses algorithmic tools and large/unstructured data to understand
human and social behavior
- Computational methods as “microscope”: Methods are not the goal, but contribute to
theoretical development and/or data generation
- Complements rather than replaces traditional methodologies
- Includes methods such as, e.g.;
o Advanced data wrangling/data science
o Combining of different data sets
o Automated Text Analysis
o Machine Learning (supervised and unsupervised)
o Actor-based modelling
o Simulations
o …
1
,Typical workflow
Why is this important now?
- In the past, collecting data was expensive (surveys, observations…)
- In the digital age, the behaviors of billions of people are recorded, stored and therefore
analyzable.
- Every time you click on a website, make a call on your mobile phone, or pay for something
with your credit card, a digital record of your behavior is created and stored.
- Because (meta-)data are a byproduct of people’s everyday actions, they are often called
digital traces.
- Large-scale records of persons or business are often called big data.
10 characteristics of big data
Characteristic Description
1 Big The scale or volume of some current datasets is often impressive.
However, big datasets are not an end in themselves, but they can enable
certain kinds of research including the study of rare events, the
estimation of heterogeneity, and the detection of small differences.
2 Always-on Many big data systems are constantly collecting data and thus enable to
study unexpected events and allow for real-time measurement.
3 Nonreactive Participants are generally not aware that their data are being captured
or they have become so accustomed to this data collection that it no
longer changes their behavior.
4 Incomplete Most big data sources are incomplete, in the sense that they don’t have
the information that you will want for your research. This is a common
feature of data that were created for purposes other than research.
5 Inaccessible Data held by companies and governments are difficult for researchers to
access.
6 Nonrepresentative Most big datasets are nonetheless not representative of certain
populations. Out-of-sample generalizations are hence difficult or
impossible.
7 Drifting Many big data systems are changing constantly, thus making it difficult
to study long-term trends.
8 Algorithmically Behavior in big data systems is not natural; it is driven by the
confounded engineering goals of the systems.
9 Dirty Big data often includes as lot of noise (e.g., junk, spam, spurious data
points...)
10 Sensitive Some of the information that companies and governments have is
sensitive.
2
,Example data
Typical computational research strategies
1. Counting things
o In the age of big data, researcher can “count” more than ever
▪ How often do people use their smartphone per day?
▪ About which topics do news websites write most often?
2. Forecasting and nowcasting
o Big data allow for more accurate predictions both in the present and in the future
▪ Investigate when people disclose themselves in computer-mediated
communication
▪ Crime prediction
3. Approximating experiments
o Computational methods provide opportunities to conduct “natural experiments”
▪ Compare smartphone log data of people who use their smartphone naturally
vs. those who abstain from certain apps (e.g., social media apps)
▪ Investigate the potential of nudges to make users select certain news
Advantages and disadvantages
- Advantages of Computational Methods
o Actual behavior vs. self-report
o Social context vs. lab setting
o Small N to large N
- Disadvantage of Computational Methods
o Techniques often complicated
o Data often proprietary
o Samples often biased
o Insufficient metadata
Computational Communication Science: why computational methods are important for (future)
communication research…
Definition
“Computational Communication Science (CCS) is the label applied to the emerging subfield that
investigates the use of computational algorithms to gather and analyze big and often semi- or
unstructured data sets to develop and test communication science theories” (Van Atteveldt & Peng,
2018).
3
, Promises
The recent acceleration in the use of computational methods for communication science is primarily
fueled by the confluence of at least three developments:
- Vast amounts of digitally available data, ranging from social media messages and other
digital traces to web archives and newly digitized newspaper and other historical archives
- Improved tools to analyze this data, including network analysis methods and automatic text
analysis methods such as supervised text classification, topic modeling, word embeddings,
and syntactic methods.
- Powerful and cheap processing power, and easy to use competing infrastructure for
processing these data, including scientific and commercial cloud computing, sharing
platforms such as Github and Dataverse, and crowd coding platforms such as Amazon Turk
and Crowdflower
Example 1: simulating search queries
Example 2: analyze news coverage
4
