TENTAMEN
Lecture 1: Introduction to Computational Methods in Communication Science
There is an increasing amount of data available online.
Much of what we know about human behavior is based on what people tell us:
- in self-report measures in surveys
- in responses in experimental research
- in qualitative interviews
Although valuable, such measurements can be biased.
But it is important to keep in mind that a lot of (mass) communication looks for example like
newspapers, news articles (New York Times, de Volkskrant) or is based on user-generated
content (TikTok, Instagram).
How can we analyze large amounts of texts? → what this course is about.
What is computational social science? And why should we care?
Computational social science
● Field of social science that uses algorithmic tools and large/unstructured data to
understand human and social behavior
● Complements rather than replaces traditional methodologies: methods are not the
goal, but contribute to data generation
[ChatGPT: the statement highlights a pragmatic approach: the choice of computational
methods should serve the larger purpose of generating data and answering substantive
questions about human and social behavior].
● Includes methods such as, e.g.,:
- Data mining (e.g., scraping and gathering of large data sets)
- Software development for social science experiments
- Automated text analysis (e.g., sentiment analysis, keyword extraction,
dictionary approaches)
- Image classification (e.g., face recognition, visual topic modeling)
- Machine learning approaches (e.g., for classification, prediction, topic
modeling)
- Actor-based modeling (e.g., simulation of social behavior, spreading of
information)
Why is this important now?
, ● 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
● Large-scale records (big data) of persons or businesses are created constantly
● Powerful and comparatively cheap processing power, and easy to use computing
infrastructure for processing these data
● Improved tools to analyze this data, including network analysis methods and
automatic text analysis methods such as supervised text classification, topic
modeling, word embeddings, as well as large language models
Big data: refers to extremely large and complex datasets that cannot be easily managed,
processed or analyzed using traditional data processing tools.
10 characteristics of big data
1. Big - the scale or volume of some current data sets is often impressive. However, big
data sets 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 confounded - behavior in big data systems is not natural; it is
driven by the engineering goals of the systems.
[ChatGPT: people’s actions in digital systems (e.g., social media, e-commerce
platforms) are often influenced by how these systems are designed. For example: on
social media, algorithms prioritize certain types of content, which can influence what
people post, share or engage with].
9. Dirty - big data often includes a lot of noise (e.g., junk, spam, spurious data points,
etc.).
10.Sensitive - some of the information that companies and governments have is
sensitive.
Pros and cons of computational methods
Opportunities Pitfalls
We can study actual behavior instead of Techniques are often (rather) complicated
, simply self-reports
We can study human beings in their social Data is often proprietary (not shared
context instead of in an artificial lab setting openly)
We can increase our N (which means Samples are often biased
higher power)
Potential to uncover patterns and insights Often, data have only insufficient metadata
that we could not investigate before
[ChatGPT: metadata provides descriptive
details about a dataset, such as source,
collection method, timeframe, format].
Risks of no longer understanding the
models we use (black box)
Computational communication science - why computational methods are important for
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).
Typical research areas
Computational communication science studies thus usually involve:
1. Large and complex data sets
2. Consisting of digital traces and other “naturally occurring” data
3. Requiring algorithmic solutions to analyze (e.g., machine learning, LLMs)
4. Allowing the study of human communication by applying and testing communication
theory
Examples of areas in which computational communication science is used:
- Political communication (hate speech)
- Social media use (tracking of actual social media use / spreading of behavior,
information or emotions)
- Health communication (prevalence of health information online)
- (Online) journalism (news coverage across decades / gender equality)
Example 1: analyzing news coverage
● Jacobi et al. (2016) analyzed the coverage
of nuclear technology from 1945 to 2014 in
the New York Times
● Analysis of 51,528 news stories (headline
and lead) → way too much for human
coding
● Used “LDA topic modeling” to extract latent
topics and analyzed their occurrence over
time
, Introduction to automated text analysis (core topic of this course)
A “new” kind of data
● A lot of communication is encoded in texts
● But text does not look like data we can easily analyze
Traditional text analysis
● Choosing the texts that contain the
content and one wants to analyze (1)
● Define the units and categories of
analysis (2) & (3)
● Describe categories and develop a
set of rules for the manual coding
process (4)
● Coding the text according to the rules
(5), which usually requires a lot of
manual work
● Make sense of codes (6) and rework
the codes and rules (7) and redo the
analysis
● Analyze frequencies, relationships,
differences, similarities between
units/codes
Problem: requires a lot of work and there are
always more texts than humans can possibly
code manually.
Definition
Text analysis is “a research technique for making replicable and valid inferences from texts
(or other meaningful matter) to the contexts of their use” (Krippendorff, 2004).
[ChatGPT: refers to text analysis as a systematic method for interpreting and understanding
texts (or other meaningful content) in a way that allows researchers to draw conclusions
about the broader context in which those texts are created or used].
What is text?
Symbols and meaning
● Text consist of symbols
● Symbols by themselves do not have meaning
● A symbol itself is a mark, sign, or word that indicates, signifies, or is understood as
representing an idea, object, or relationship
● Symbols thereby allow people to go beyond what is known or seen by creating
linkages between otherwise very different concepts and experiences
● Text (a collection of symbols) only attains meaning when interpreted (in its context)
Lecture 1: Introduction to Computational Methods in Communication Science
There is an increasing amount of data available online.
Much of what we know about human behavior is based on what people tell us:
- in self-report measures in surveys
- in responses in experimental research
- in qualitative interviews
Although valuable, such measurements can be biased.
But it is important to keep in mind that a lot of (mass) communication looks for example like
newspapers, news articles (New York Times, de Volkskrant) or is based on user-generated
content (TikTok, Instagram).
How can we analyze large amounts of texts? → what this course is about.
What is computational social science? And why should we care?
Computational social science
● Field of social science that uses algorithmic tools and large/unstructured data to
understand human and social behavior
● Complements rather than replaces traditional methodologies: methods are not the
goal, but contribute to data generation
[ChatGPT: the statement highlights a pragmatic approach: the choice of computational
methods should serve the larger purpose of generating data and answering substantive
questions about human and social behavior].
● Includes methods such as, e.g.,:
- Data mining (e.g., scraping and gathering of large data sets)
- Software development for social science experiments
- Automated text analysis (e.g., sentiment analysis, keyword extraction,
dictionary approaches)
- Image classification (e.g., face recognition, visual topic modeling)
- Machine learning approaches (e.g., for classification, prediction, topic
modeling)
- Actor-based modeling (e.g., simulation of social behavior, spreading of
information)
Why is this important now?
, ● 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
● Large-scale records (big data) of persons or businesses are created constantly
● Powerful and comparatively cheap processing power, and easy to use computing
infrastructure for processing these data
● Improved tools to analyze this data, including network analysis methods and
automatic text analysis methods such as supervised text classification, topic
modeling, word embeddings, as well as large language models
Big data: refers to extremely large and complex datasets that cannot be easily managed,
processed or analyzed using traditional data processing tools.
10 characteristics of big data
1. Big - the scale or volume of some current data sets is often impressive. However, big
data sets 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 confounded - behavior in big data systems is not natural; it is
driven by the engineering goals of the systems.
[ChatGPT: people’s actions in digital systems (e.g., social media, e-commerce
platforms) are often influenced by how these systems are designed. For example: on
social media, algorithms prioritize certain types of content, which can influence what
people post, share or engage with].
9. Dirty - big data often includes a lot of noise (e.g., junk, spam, spurious data points,
etc.).
10.Sensitive - some of the information that companies and governments have is
sensitive.
Pros and cons of computational methods
Opportunities Pitfalls
We can study actual behavior instead of Techniques are often (rather) complicated
, simply self-reports
We can study human beings in their social Data is often proprietary (not shared
context instead of in an artificial lab setting openly)
We can increase our N (which means Samples are often biased
higher power)
Potential to uncover patterns and insights Often, data have only insufficient metadata
that we could not investigate before
[ChatGPT: metadata provides descriptive
details about a dataset, such as source,
collection method, timeframe, format].
Risks of no longer understanding the
models we use (black box)
Computational communication science - why computational methods are important for
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).
Typical research areas
Computational communication science studies thus usually involve:
1. Large and complex data sets
2. Consisting of digital traces and other “naturally occurring” data
3. Requiring algorithmic solutions to analyze (e.g., machine learning, LLMs)
4. Allowing the study of human communication by applying and testing communication
theory
Examples of areas in which computational communication science is used:
- Political communication (hate speech)
- Social media use (tracking of actual social media use / spreading of behavior,
information or emotions)
- Health communication (prevalence of health information online)
- (Online) journalism (news coverage across decades / gender equality)
Example 1: analyzing news coverage
● Jacobi et al. (2016) analyzed the coverage
of nuclear technology from 1945 to 2014 in
the New York Times
● Analysis of 51,528 news stories (headline
and lead) → way too much for human
coding
● Used “LDA topic modeling” to extract latent
topics and analyzed their occurrence over
time
, Introduction to automated text analysis (core topic of this course)
A “new” kind of data
● A lot of communication is encoded in texts
● But text does not look like data we can easily analyze
Traditional text analysis
● Choosing the texts that contain the
content and one wants to analyze (1)
● Define the units and categories of
analysis (2) & (3)
● Describe categories and develop a
set of rules for the manual coding
process (4)
● Coding the text according to the rules
(5), which usually requires a lot of
manual work
● Make sense of codes (6) and rework
the codes and rules (7) and redo the
analysis
● Analyze frequencies, relationships,
differences, similarities between
units/codes
Problem: requires a lot of work and there are
always more texts than humans can possibly
code manually.
Definition
Text analysis is “a research technique for making replicable and valid inferences from texts
(or other meaningful matter) to the contexts of their use” (Krippendorff, 2004).
[ChatGPT: refers to text analysis as a systematic method for interpreting and understanding
texts (or other meaningful content) in a way that allows researchers to draw conclusions
about the broader context in which those texts are created or used].
What is text?
Symbols and meaning
● Text consist of symbols
● Symbols by themselves do not have meaning
● A symbol itself is a mark, sign, or word that indicates, signifies, or is understood as
representing an idea, object, or relationship
● Symbols thereby allow people to go beyond what is known or seen by creating
linkages between otherwise very different concepts and experiences
● Text (a collection of symbols) only attains meaning when interpreted (in its context)
