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A Summary of Computational Social Science - Lecture 8 in Introduction to Computational Social Science

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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

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A Summary of Computational Social Science - Lecture 8 in Introduction to Computational Social Science

  1. 1. ASUMMARY OFCOMPUTATIONALSOCIALSCIENCE LECTURE 8, 22.9.2015 INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCE (CSS01) LAURI ELORANTA
  2. 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. 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. 4. FINAL ASSIGNMENT PRACTICALITIES
  5. 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. 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. 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. 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. 9. SUMMARYOF COMPUTATIONAL SOCIALSCIENCE
  10. 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. 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. 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. 13. COMPUTER SCIENCE SOCIAL SCIENCE STATISTICS COMPUTATIONAL SOCIALSCIENCE
  14. 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. 15. INSTRUMENTAL REVOLUTION THE BACKGROUNDIMAGE “TATELTELESCOPE”BY EP_JHU IS UNDERNON COMMERCIALCREATIVECOMMONS LICENSE. SEE ORIGINALIMAGEHERE. SEE LICENSE TERMS HERE. ITISFOREMOSTAN
  16. 16. Time More Less • Speed and performance of IT (CPU, RAM, Network) • Access to IT / Internet • Amount of data generated • Cost of IT
  17. 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. 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. 19. EVERYONE IS ALREADYA RESEARCH SUBJECT
  20. 20. BIG DATA& AUTOMATED INFROMATION EXTRACTION SOCIAL NETWORK ANALYSIS COMPLEX SYSTEMS & MODELING SIMULATION 1 2 3 4
  21. 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. 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. 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. 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. 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. 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. 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. 28. THEFUTUREOF COMPUTATIONAL SOCIALSCIENCE
  29. 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. 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. 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. 32. WHAT’SNEXT?
  33. 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. 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. 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. 36. Thank You! Questions and comments? twitter: @laurieloranta

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