INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCELECTURE 1, 1.9.2015

INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCE (CSS01)LAURI ELORANTA

@LAURIELORANTA

DATA MINING

DATA AND SOCIETY

BIG DATA

PREDICTIVE ANALYSIS

DIGITAL METHODS

DIGITAL HUMANITIES

SOCIAL NETWORK ANALYSIS

PROGRAMMING IN SOCIAL SCIENCE

IT IS A JUNGLE OUT THERECOMPLEX SYSTEMS

DATA SCIENCE

HADOOP/MAP REDUCE

REACTIVE PROGRAMMING

PERSONAL DATA

MY DATA

OPEN DATA

IOT / WEARABLES

BUZZ

HYPE

BUZZ

HYPE

BUZZ

HYPE

THE BACKGROUND IMAGE “JUNGLE” BY LUKE JONESIS UNDER CREATIVE COMMONS LICENSE.

SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

NOT THAT MUCH TALKING ANDEVEN LESS DOINGONLY A FEW PIONEERS IN THE DESERTED CSS SCENE IN FINLAND

THE BACKGROUND IMAGE “DESERT” BY MOYAN BRENNIS UNDER CREATIVE COMMONS LICENSE.

SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

• Practicalities• What is computational social science?• Areas of Computational Social Science• (Big) Data & automated information extraction• Social Networks • Social Complexity• Simulation

• Research examples• Lecture 1 Reading

LECTURE 1OVERVIEW

PRACTICALITIES

• The slides and all materials will be online at http://blogs.helsinki.fi/computationalsocialscience/

• Course consists of • 8 Lectures• A Research Plan Assignment (required, if you want study

credits, 5op)

• Any questions? • Contact lecturer Lauri Eloranta at firstname dot lastname

@helsinki.fi

PRACTICALITIESGENERAL

• LECTURE 1: Introduction to Computational Social Science [TODAY]• Tuesday 01.09. 16:00 – 18:00, U35, Seminar room114

• LECTURE 2: Basics of Computation and Modeling• Wednesday 02.09. 16:00 – 18:00, U35, Seminar room 113

• LECTURE 3: Big Data and Information Extraction• Monday 07.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 4: Network Analysis• Monday 14.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 5: Complex Systems• Tuesday 15.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 6: Simulation in Social Science • Wednesday 16.09. 16:00 – 18:00, U35, Seminar room 113

• LECTURE 7: Ethical and Legal issues in CSS• Monday 21.09. 16:00 – 18:00, U35, Seminar room 114

• LECTURE 8: Summary• Tuesday 22.09. 17:00 – 19:00, U35, Seminar room 114

LECTURESSCHEDULE

• Course Book• Cioffi-Revilla, Claudio (2014).

Introduction to Computational Social Science. Springer-Verlag, London.

• Further Reading:

LITERATURECOURSE BOOK

• The full eBook is available via Helsinki University Library:

https://helka.linneanet.fi/cgi-bin/Pwebrecon.cgi?BBID=2753081

LITERATURECOURSE BOOK

LITERATUREADDITIONAL READING

• There will be additional reading given for each lecture

• Research articles on the topic at hand, some will be given for “homework reading”

• The full list of articles can be found at: http://blogs.helsinki.fi/computationalsocialscience

• 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 • Focus on research method <-> research data <-> research

problem

• How to write a research plan, general instructions:• http://www.uta.fi/cmt/en/doctoralstudies/apply/Tutkimussuunni

telmaohjeet_EN%5B1%5D.pdf• https://into.aalto.fi/display/endoctoraltaik/Research+Plan

ASSIGNMENTGENERAL

• Assignment DL is Friday 2.10.2015 at EOD/Midnight.• 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 name, student ID and contact details

• Assignments are returned to the lecturer Lauri Eloranta via email: firstname dot lastname @ helsinki.fi

• Grading is done in one month’s time, and you will receive the study credits on or before 30.10.2015.

ASSIGNMENTHOW TO RETURN THE ASSIGNMENT

• Contains six course, covering different aspects of computational social science• Full stydy block 25-30 op.• Basic courses (mandatory)• Introduction to Computational Social Science (5 op) (I period)• Introduction to Programming in Social Science (5 op) (II

period)• Special courses• Data extraction (5 op) (IV period)• Network Analysis (5 op) (in 2016 – 2017)• Complex Systems (5 op) (III period)• Simulation (5 op) (in 2016 – 2017)

COMPUTATIONAL SOCIAL SCIENCE STUDY BLOCK

WHAT IS COMPUTATIONAL SOCIAL SCIENCE?

“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.

• “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, D. et al. 2009. Computational Social Science. Science. 6 February 2009: Vol. 323, no. 5915, pp. 721-723.

LAZER ET AL. 2009

• “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. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. (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.

CSS MANIFESTO(CONTE ET AL. 2012)

• “Computational social science refers to the academic sub-disciplines concerned with computational approaches to the social sciences. Fields include computational economics and computational sociology.

It is a multi-disciplinary and integrated approach to social survey focusing on information processing by means of advanced information technology. The computational tasks include the analysis of social networks and social geographic systems.” • (Wikipedia 2015,

http://en.wikipedia.org/wiki/Computational_social_science)

WIKIPEDIA

• “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, 2014

Cioffi-Revilla, Claudio (2014). Introduction to Computational Social Science. Springer-Verlag, London.

INCREASINGLY COMPLEX SOCIETY

THE BACKGROUND IMAGE “POINT AND LINE TO (MULTIPLE) PLANE(S).” RODRIGO CARVALHOIS UNDER NON COMMERCIAL CREATIVE COMMONS LICENSE. SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

INSTRUMENTAL REVOLUTION

THE BACKGROUND IMAGE “TATEL TELESCOPE” BY EP_JHUIS UNDER NON COMMERCIAL CREATIVE COMMONS LICENSE.

SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

IT IS FOREMOST AN

COMPUTER SCIENCE

SOCIAL SCIENCE

STATISTICS

COMPUTATIONAL SOCIAL SCIENCE

Time

More

Less

• Speed and performance of IT (CPU, RAM, Network)

• Access to IT / Internet

• Amount of data generated

• Cost of IT

FUNDAMENTAL CHANGES IN RESEARCH SETUPTHE BACKGROUND IMAGE “HOME VISIT” BY NICOLAS NOVA

IS UNDER CREATIVE COMMONS LICENSE. SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

MAJOR QUESTIONS REGARDING RESEARCH ETHICS THE BACKGROUND IMAGE “CAMÉRA DE SURVEILLANCE” BY TRISTAN NITOTIS UNDER CREATIVE COMMONS LICENSE.

SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

NOT A SILVER BULLET

COMPUTATIONAL SOCIAL SCIENCE IS

THE BACKGROUND IMAGE “9MM BULLET BW” BY AN NGUYENIS UNDER CREATIVE COMMONS LICENSE.

SEE ORIGINAL IMAGE HERE. SEE LICENSE TERMS HERE.

Computational Social Science proposes revolutionary opportunities for the social sciences, but it has still some challenges in relation to methods, interdisciplinary cooperation and research ethics.

1. Solving increasingly complex problems: The problems of global world are complex: computational methods might be able to solve these complex issues

2. The rise of data: The amounts of data has exploded during the 21st century

3. IT and Instrumental revolution: all the new tools and possibilities

4. Complex systems: modeling our dynamic organisations and societies

5. Social networks: modeling human behavior as networks6. Making predictions and simulations: predicting future

from the past7. Interdisciplinary field: (social sciences, math, computer

science…)8. Many problems and challenges, especially regarding

research ethics

CSS COMPONENTS

• Information processing paradigm has two aspects in relation to CSS:

1. Information processing is substantive to the complex systems of society that CSS researches: This means that information processing is takes part in forming and evolution of complex systems.

2. Information processing is methodological in the sense that it serves as the core instrument of CSS

COMPUTATIONAL PARADIGM OF SOCIETY

(Cioffi-Revilla, 2014.)

BIG DATA & AUTOMATED INFROMATION EXTRACTION

SOCIAL NETWORK ANALYSIS

COMPLEX SYSTEMS & MODELING

SIMULATION

12

34

THE MAIN AREAS OF CSS

• Areas of Computational Social Science1. (Big) Data & automated data extraction• Generate, retrieve, sort, modify, transform, … data

2. Social Networks• Network analysis and social networks

3. Social Complexity• Social complexity, complex adaptive systems, complex systems modeling

4. Simulation

FOUR MAIN AREAS OF CSS

(Cioffi-Revilla, 2014.)

• Data and automated information extraction can be seen as foundation for the other areas of CSS

• Raw data can be used as:1. Data for its own sake: as research data -> data is the

subject of research2. Data for modeling or validating other phenomena via. e.g.

network analysis, complex systems analysis or simulation

• Data is generated, retrieved, modified, transformed,… for research purposes via computational automation

BIG DATA & AUTOMATED INFORMATION EXTRACTION

(Cioffi-Revilla, 2014.)

• A long tradition in network analysis (much older field than CSS)• Social Networks (Facebook, Twitter, etc.) just one part of

network analysis• Many other social interactions can be modeled as networks ->

thus social networks are not technology dependent as such• -> e.g. modeling family as network• -> e.g. modeling a project as network

SOCIAL NETWORKS

(Cioffi-Revilla, 2014.)

• Society seen as a complex adaptive system:• Phase transitions• Adaptation (multi stage process)• Need -> intent -> capacity -> implementation• Goal

• Information processing in many parts of Complex adaptive systems• To help adaptation, allocating resources, coordination, …

• Family as and complex adaptive system:• Development, hardships, births, deaths, successes, failures• Adaptation over decades

SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

• Three types of systems1. Natural systems2. Human systems3. Artificial systems

• Artificial systems (or artifacts) exist because they have a function: they serve as adaptive buffers between humans and nature

• Humans pursue the strategy of building artifacts to achieve goals• Two kinds of artificial systems working in synergy• Tanglible (e.g. roads, buildings)• Intanglibe ( e.g. organisations, social structures)

SIMON’S THEORY OF ARTIFACTS AND SOCIAL COMPLEXITY

(Cioffi-Revilla, 2014.)

• Large (and old) research field• Two main areas of simulation1. 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)

• Also, Evolutionary Models

SIMULATION

(Cioffi-Revilla, 2014.)

• 4 main areas of Computational Social Science1. Big data and automatic information extraction2. Social networks3. Social complexity4. Simulation• Typically all of these working together

• CSS has a lot of problems, especially concerning privacy and ethics• CSS is not a silver bullet and it does not replace other social

science fields or methods: Instead, CSS complements other research fields and methods

SUMMARY

SOME RESEARCH EXAMPLES

• Tracking and predicting how flu or other contagious diseases spread• Based on network and social media analysis and modeling• Many different variations, one of the first: Google Flu Trends,

based on flu related search queries• For example:• Achrekar, H.; Gandhe, A.; Lazarus, R.; Ssu-Hsin Yu; Benyuan Liu, 2011.

Predicting Flu Trends using Twitter data. Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on , vol., no., pp.702,707, 10-15 April 2011

MODELING THE SPREAD OF DISEASESALREADY AN EPIDEMOLOGY CLASSIC

• http://www.google.org/flutrends/intl/en_us/

GOOGLE FLU TRENDS

• Leskovec, J.; Backstrom, L.; Kleinberg, J. 2009. Meme-tracking and the dynamics of the news cycle. Proceedings of the 15th ACM ACM SIGKDD international conference on Knowledge discovery and data mining, Pages 497-506 , 2009 - dl.acm.org

• Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events.

• We develop a framework for tracking short, distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis.

MODELING NEWS CYCLE DYNAMICS

• Athanasiadis, I. N.; Mentes, A. K.; Mitkas, P. A.; Mylopoulos, Y. A. 2005. A Hybrid Agent-Based Model for Estimating Residential Water Demand SIMULATION March 2005 81: 175-187, doi:10.1177/0037549705053172

• Picardi, C. and Saeed, K. 1979.The dynamics of water policy in southwestern Saudi Arabia Anthony. SIMULATION, October 1979; vol. 33, 4: pp. 109-118.

SUSTAINABLE WATER DEMAND MANAGEMENT MODELING

• Venturini, T.; Laffite, N. B.; Cointet, J-P.; Gray, I.; Zabban, V.; De Pryck, K. 2014.Three maps and three misunderstandings: A digital mapping of climate diplomacy. Big Data & Society July-December 2014 1: 2053951714543804, first published on August 5, 2014 doi:10.1177/2053951714543804

CLIMATE DIPLOMACY MAPPING

• Can electoral popularity be predicted using socially generated big data? Information Technology. Volume 56, Issue 5, Pages 246–253, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: 10.1515/itit-2014-1046, September 2014

• Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.

PREDICTING ELECTIONS?

• DIGIVAALIT 2015• http://www.hiit.fi/digivaalit-2015• Researching the parliamentary elections 2015 in Finland,

focusing on digital media data (Twitter, Facebook)• Trying to understand how media is used and how public agenda

is set

• CITIZEN MINDSCAPES• http://

challenge.helsinki.fi/blog/citizen-mindscapes-kansakunnan-mielentila

• Diving deep into the unscoped virtual territories of a nation’s collective consciousness may reveal something remarkable. The Finnish, hugely popular Suomi24 discussion forum has 1.9 million monthly visitors, who use the online town square to talk about anything and everything close to their hearts. If this data could be harnessed into research use, what amazing things could we learn about Finnish society? A team of media professionals at the forums owner company Aller and researchers at the National Consumer Research Center plan to make use of this immense database.

DIGIVAALIT 2015 & CITIZEN MINDSCAPES

• Listen the “The Trust Engineers” podcast by Radiolab• http://www.radiolab.org/story/trust-engineers/

• Think about and discuss different ethical research issues in relation to what you heard

ETHICS

• Lazer, D. et al. 2009. Computational Social Science. Science. 6 February 2009: Vol. 323, no. 5915, pp. 721-723.

• Conte, R. 2012. Manifesto of Computational Social Science. The European Physical Journal Special Topics. November 2012: Vol. 214, Issue 1, pp. 325-346.

• Anderson, C. 2008. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired. http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory

• Einav, L. and Levin, J. 2014. The Data Revolution and Economic Analysis. In Innovation Policy and the Economy edited by Josh Lerner and Scott Stern. http://web.stanford.edu/~leinav/pubs/IPE2014.pdf

• King, G. 2011. Ensuring the Data-Rich Future of the Social Sciences. Science. 11 February 2011: Vol. 331 no. 6018 pp. 719-721.

• Wallach, H. 2014. Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency. Medium.com. https://medium.com/@hannawallach/big-data-machine-learning-and-thesocial-sciences-927a8e20460d

LECTURE 1 READING

Thank You! Questions and comments?

twitter: @laurieloranta