Final lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/). Lecturer: Lauri Eloranta Questions & Comments: https://twitter.com/laurieloranta

1. ASUMMARY
OFCOMPUTATIONALSOCIALSCIENCE
LECTURE 8, 22.9.2015
INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCE (CSS01)
LAURI ELORANTA

2. • LECTURE 1: Introduction to Computational Social Science [DONE]
• Tuesday 01.09. 16:00 – 18:00, U35, Seminar room114
• LECTURE 2: Basics of Computation and Modeling [DONE]
• Wednesday 02.09. 16:00 – 18:00, U35, Seminar room 113
• LECTURE 3: Big Data and Information Extraction [DONE]
• Monday 07.09. 16:00 – 18:00, U35, Seminar room 114
• LECTURE 4: Network Analysis [DONE]
• Monday 14.09. 16:00 – 18:00, U35, Seminar room 114
• LECTURE 5: Complex Systems [DONE]
• Tuesday 15.09. 16:00 – 18:00, U35, Seminar room 114
• LECTURE 6: Simulation in Social Science [DONE]
• Wednesday 16.09. 16:00 – 18:00, U35, Seminar room 113
• LECTURE 7: Ethical and Legal issues in CSS [DONE]
• Monday 21.09. 16:00 – 18:00, U35, Seminar room 114
• LECTURE 8: Summary [TODAY]
• Tuesday 22.09. 17:00 – 18:00, U35, Seminar room 114
LECTURESSCHEDULE

3. • PART 1: Final assignment practicalities
• PART 2: Summary: main takeaways from the course
• PART 3: The future of computational social science
• PART 4: What’s next?
LECTURE 8OVERVIEW

4. FINAL ASSIGNMENT
PRACTICALITIES

5. • Write a short research plan where you apply a computational social
science method to a research problem
• Length: 8 pages for Master’s students, 10 pages for PhD students
• Line-spacing 1,5
• Language: You can write in English or in Finnish
• Focus on research method <-> research data <-> research problem
• How to write a research plan, general instructions:
• http://www.uta.fi/cmt/en/doctoralstudies/apply/Tutkimussuunnitelmaohjeet_
EN%5B1%5D.pdf
• https://into.aalto.fi/display/endoctoraltaik/Research+Plan
FINALASSIGNMENTGENERAL

6. • Select one computational social science related research method
• Focus on (1) the research problem, (2) the CSS research method of your
selection and (3) research data of your selection
• Especially important is the relationship between the three: how does
the method, data and problem relate to each other
• Describe your research method based on literature
• The research question and data can be also described in relation to
previous research literature
• Remember to discuss the reliability issues of your study, and what
problems there might be in the research design
• Also remember to evaluate the potential ethical issues of the research
RESEARCH PLAN
CONTENTS

7. • The research plan, as any scientific text, should contain properly marked
references and a reference list in the end of the document
• In Helsinki University / Faculty of Social Sciences the reference notation
typically follows the APA 6th referencing style (American Psychological
Association, 6th edition).
• http://www.muhlenberg.edu/library/reshelp/apa_example.pdf
• The most important thing is that you use the notation style you have
selected in a concise manner
USING REFERENCES

8. • Final Assignment DL is Friday 9.10.2015 at EOD/Midnight. Late returns will not be
graded.
• All assignments are returned in PDF-format
• How to save my work in pdf-format ?  You can ”Save as PDF” or ”Print to PDF” in MS Word
• Include your details:
• Include your name, student ID and email information
• Final Assignment is returned via email:
• Assignments are returned to the lecturer Lauri Eloranta via email:
firstname dot lastname @ helsinki.fi
• The subject of the email should be: CSS – Assignment – Your Name
• Grading is done in one month’s time, and you will receive the study credits on or
before 30.10.2015.
• Final Grading is done in Helsinki University standard manner: 0-5.
RETURNING
THEASSIGNMENT

9. SUMMARYOF
COMPUTATIONAL
SOCIALSCIENCE

10. “In short, a computational social science is
emerging [field] that leverages the capacity
to collect and analyze data with an
unprecedented breadth and depth and
scale.” (Lazer et al. 2009.)
Lazer, D. et al. 2009. Computational Social Science. Science. 6 February 2009: Vol. 323, no. 5915, pp. 721-723.

11. “The increasing integration of technology into our
lives has created unprecedented volumes of data on
society’s everyday behaviour. Such data opens up
exciting new opportunities to work towards a
quantitative understanding of our complex social
systems, within the realms of a new discipline known
as Computational Social Science. “
(Conte et al. 2012)
Conte, R. 2012. Manifesto of Computational Social Science. The European Physical Journal Special Topics.
November 2012: Vol. 214, Issue 1, pp. 325-346.

12. “The new field of Computational Social Science
can be defined as the interdisciplinary
investigation of the social universe of many
scales, ranging from individual actors to the
largest groupings, through the medium of
computation.” (Cioffi-Revilla, 2014.)
Cioffi-Revilla, Claudio (2014). Introduction to Computational Social Science. Springer-Verlag, London.

13. COMPUTER
SCIENCE
SOCIAL
SCIENCE
STATISTICS
COMPUTATIONAL
SOCIALSCIENCE

14. INCREASINGLY
COMPLEX
SOCIETY
THE BACKGROUNDIMAGE “POINTAND LINE TO (MULTIPLE)PLANE(S).”RODRIGO CARVALHO
IS UNDERNON COMMERCIALCREATIVECOMMONS LICENSE.
SEE ORIGINALIMAGEHERE. SEE LICENSE TERMS HERE.

15. INSTRUMENTAL
REVOLUTION
THE BACKGROUNDIMAGE “TATELTELESCOPE”BY EP_JHU
IS UNDERNON COMMERCIALCREATIVECOMMONS LICENSE.
SEE ORIGINALIMAGEHERE. SEE LICENSE TERMS HERE.
ITISFOREMOSTAN

16. Time
More
Less
• Speed and
performance of IT
(CPU, RAM,
Network)
• Access to IT /
Internet
• Amount of data
generated
• Cost of IT

17. 1. Solving increasingly complex problems
2. Instrumental revolution with the rise of
data and IT
3. An Interdisciplinary field
4. Contains many problems and
challenges, especially regarding
research ethics
COMPONENTSOF
COMPUTATIONALSOCIALSCIENCE

18. NOTA
SILVER
BULLET
COMPUTATIONAL
SOCIALSCIENCEIS
THE BACKGROUNDIMAGE “9MM BULLET BW”BY AN NGUYEN
IS UNDERCREATIVECOMMONS LICENSE.
SEE ORIGINALIMAGE HERE. SEE LICENSE TERMS HERE.

19. EVERYONE IS
ALREADYA
RESEARCH
SUBJECT

20. BIG DATA&
AUTOMATED
INFROMATION
EXTRACTION
SOCIAL
NETWORK
ANALYSIS
COMPLEX
SYSTEMS &
MODELING
SIMULATION
1
2
3
4

21. Image by IBM, 2014. The Four V’s of Big Data. http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

22. • Characteristics of social networks and social networks as analogy of
some parts of the society are quite common in all major social science
fields (economics, sociology, anthropology, political science,
psychology).
• Social Network Analysis is a paradigmatic viewpoint of society: it
contains the belief, that social universe is formed of and can be modeled
with networks.
• Not just a collection of methods, but also a strong theoretical perspective
SOCIALNETWORKASA
VIEWPOINT
(Cioffi-Revilla 2014.)

23. ADJACENCYMATRIX
REPRESENTATION
Anna
Jack
Jane
Ellen
Anna Ellen Jack Jane
Anna 0 0 1 1
Ellen 0 0 0 1
Jack 1 0 0 1
Jane 1 1 1 0

24. • Complexity is a debated concept: 1. what can be considered
complex? 2. how to model and research complexity?
• No agreed universal definition of complexity or complex system
• Parts versus the whole (micro vs. macro): i.e. can you research
complexity by researching the parts of the complex system only?
• Structure versus agency: i.e. can you research complexity by
researching the structure only, and what is the relationship between
structure and agency?
• Deep ontological and epistemological debates/problems when
discussing about modeling complexity or simulating complexity
• Positivism/Empiricism vs. critical realism vs. complex realism
• Some authors don’t consider big parts of agent based simulation of
complex systems to be science at all.
COMPLEXITYIS COMPLEX
(Byrne & Callaghan, 2014)

25. • Large (and old) research field
• Two main areas of simulation
1. Variable-Oriented Models
• System Dynamics Models (e.g. modeling a nuclear plant)
• Queuing Models (e.g modeling how a box office line behaves)
2. Object-Oriented Models
• Cellular automate (e.g. Game of life: http://en.wikipedia.org/wiki/Conway%27s_Game_of_Life,
http://pmav.eu/stuff/javascript-game-of-life-v3.1.1/)
• Agent based models (eg. Modeling the communication of a project
organisation of many individuals)
SIMULATION
(Cioffi-Revilla, 2014.)

26. SIMULATION OVERVIEW
Empirical data
Referent / target
system in real
world
Conceptual
model of
target system
Formal model
Simulation
model
Simulation system
(software)
Observation
Abstraction
Formalization Computational
implementation
Testable
predictions
Feedback
(Cioffi-Revilla, 2014.)
“The Model” “The Simulation”
“The Real World”

27. • Focusing solely on computational social science has some potential
pitfalls:
• Digital methods are only as good as their fit for the research question at
hand
• Don’t let the method be on the driver seat
• Base all decisions back to the research question
EVERYTHINGSTARTSWITHA
RESEARCHQUESTIONS

28. THEFUTUREOF
COMPUTATIONAL
SOCIALSCIENCE

29. • Still many problems in relation to methods, tools, ethics and privacy
• Computational Social Science tends to be either computer science focused or social
science focused
•  Needs more integration between different fields
• Wallach (2015) suggests that we should focus on
1. Improving the interdisciplinary cooperation between CS and social sciences  For
example attending conferences of different fields
2. Explicitly managing research publication expectations by acknowledging the fact that
publishing interdisciplinary research can be slower than publishing single- discipline
research
3. Focus on providing educational trajectories for future computational social scientists
CSS FIELD IS STILL
EVOLVING
(Wallach 2015.)

30. • Creating a “social super collider”
• Solving complex social questions is nowadays quite hard or
impossible, because one needs to combine many different sources of
(typically unaccessible) data
• What about the privacy then?
• Expanding virtual labs
• Providing infrastructure for large macrosociology studies
• For example, Amazon Mechanical Turk
• Putting the social back into computational social science
• Many research papers are heavily computer science focused, and
have limited relevance in the field of social science
• More interdisciplinary cooperation needed!
FUTURE OPPORTUNITIES
AND CHALLENGES
(Watts 2013.)

31. • Computational social science is an instrumental revolution based on new possibilities,
new methods and new data
• The similar change, that is happening in social sciences, has already happened in
computational biology and in computational physics
• As we are in the middle of this change, it is today important to define what
“computational social science” is in relation to social science
• In the long term these computational methods will be part of the standard research
method tools of social science, side by side with the traditional method set
• Thus, after the “revolution”, there will be no computational social science, just
social science.
THEWORD“COMPUTATIONAL”
WILLEVENTUALLYDISAPPEAR

32. WHAT’SNEXT?

33. • Helsinki University / Faculty of Social Sciences & Centre for Research
Methods is providing a study program in computational social science:
• http://blogs.helsinki.fi/computationalsocialscience
• The program forms of six courses:
• CSS01: Introduction to Computational Social Science (this course)
• CSS02: Programming in Social Sciences (held in II period)
• CSS03: Automated information extraction (held in IV period)
• CSS04: Network analysis
• CSS05: Complex Systems and Modeling (held in III period)
• CSS06: Simulation in Social Sciences
COMPUTATIONALSOCIAL
SCIENCESTUDYPROGRAM

34. • Wallach, H. (2015). Computational social science: Toward a collaborative
future. In R. Alvarez, editor, Computational Social Science: Discovery
and Prediction. Cambridge University Press, forthcoming.
• Watts, D. J. (2013). Computational social science: Exciting progress and
future directions. The Bridge on Frontiers of Engineering, 43(4), 5-10.
LECTURE 8 READING

35. • Cioffi-Revilla, C. 2014. Introduction to Computational Social Science.
Springer-Verlag, London
• Byrne, D.; Callaghan, G. 2014. Complexity Theory and The Social
Sciences. Routledge, New York.
• IBM, 2014. The Four V’s of Big Data.
http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-
big-data.jpg
• Wallach, H. (2015). Computational social science: Toward a collaborative
future. In R. Alvarez, editor, Computational Social Science: Discovery
and Prediction. Cambridge University Press, forthcoming.
• Watts, D. J. (2013). Computational social science: Exciting progress and
future directions. The Bridge on Frontiers of Engineering, 43(4), 5-10.
REFERENCES

36. Thank You!
Questions and comments?
twitter: @laurieloranta