Introduction to Social Data Science

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# Introduction to Social Data Science
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.author[
### David Garcia <br> <em>ETH Zurich</em>
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.date[
### Social Data Science
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<div class="my-footer"><span>David Garcia - Social Data Science - ETH Zurich, Chair of Systems Design</span></div>

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# What is the aim of Social Data Science?

> The aim of Social Data Science is   
**the Quantitative Understanding of Human Behavior**.

- **Quantitative:** As opposed to qualitative or descriptive, we aim for robust findings grounded in strong evidence that can be quantified.

- **Understanding:** Not just predicting, we want to be able to generalize and combine knowledge, and even to motivate interventions or policies.

- **Human:**  We will not study particles or objects. Measurement validity and ethics will be a challenge.

- **Behavior:** Observable changes, structures, dynamics, and patterns; not just stories or theories

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#  How are we going to do it?
**Retrieving, processing, analyzing, and interpreting Digital traces.**  
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![:scale 80%](FBMap.png)
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# Another example of digital traces
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**Pair up and guess together:**

- What kind of traces are show in the figure on the right?

- How were they collected?

- What can we learn about human behavior with this kind of data?

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.pull-right[.center[![:scale 100%](Net.png)]]

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# What is data?

> **Data:** Facts in the form of stored and transmittable information.

- *Data* is the plural of the Latin word *datum*. *Data* means ”given (things)”.  
- Data is given to us, it is not fabricated nor simulated.
![:scale 85%](http://www.phdcomics.com/comics/archive/phd080715s.gif)

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# What is Social Data Science?

**Data Science** is the application of methods from Computer Science and Statistics to empirical questions and practical problems.

**Social Data Science** is the application of Data Science to study human behavior and social interaction.

- Importance of combining methods, synthesis, and communication
- Gathering digital traces with Computer Science techniques
- Analysis interpretation with respect to what is known in the relevant Social Sciences
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# Why digital traces?

Data from digital traces have six properties that can complement other traditional data sources in the social sciences:

- **Big Data:** Observing large amounts of humans across demographics

- **Fast Data:** Quantifying aspects of human behavior in real time

- **Long Data:** Retrieving longitudinal data and at various timescales

- **Deep Data:** Gathering persistent information on individuals

- **Mixed Data:** Combining heterogeneous datasources and unstructured data

- **Strange Data:** Locating small subcommunities or deviant behavior

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# What are the limitations of digital traces?

With the great potential of digital traces, aslo come great challenges:

- **Platform biases:** caused by their design, algorithms, social bots, etc

- **Data gatekeepers:** Not everyone can access some data sources

- **Performative behavior:** Talking online is not the same as offline.

- **Representativity issues:** Not everyone leaves digital traces.

- **Observational data:** Testing causal mechanisms is not straightforward.

- **The data deluge:** Too much data enables black-box predictions that can be useful but limit our understanding.

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## The Hype Cycle of Social Data Science
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<img src="Hype1.svg" width="900" />

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## The Hype Cycle of Social Data Science
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<img src="Hype3.svg" width="900" />

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### The importance of questions in Social Data Science

![:scale 100%](DeepThought.jpeg)  
Deep Thought, from the movie version of The Hitchhiker's Guide to the Galaxy.

> **Understanding our questions is a prerequisite to understanding their answers.**