Lecture 7: How to STUDY the Social Web? (2014)

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Lecture 7: How to STUDY the Social Web? (2014)

  1. 1. Social Web 2014, Lora Aroyo! Lecture VI: How can we STUDY the Social Web? (based on slides from Les Carr, Nigel Shadbolt, Harith Alani Lora Aroyo The Network Institute VU University Amsterdam Social Web 2014
  2. 2. The Web the most used and one of the most transformative applications in the history of computing, e.g. how the Social Web has transformed the world's communication ! approximately 10 more than 10 Social Web 2014, Lora Aroyo!
  3. 3. The Web Great success as a technology, it’s built on significant computing infrastructure, but as an entity surprisingly unstudied Social Web 2014, Lora Aroyo!
  4. 4. • physical science: analytic discipline to find laws that generate or explain observed phenomena • CS is mainly synthetic: formalisms & algorithms are created to support specific desired behaviors • Web Science: web needs to be studied & understood as a phenomenon but also to be engineered for future growth and capabilities Social Web 2014, Lora Aroyo! Science & Engineering
  5. 5. Web is NOT a Thing • it’s not a verb, or a noun • it’s a performance, not an object • co-constructed with society • activity of individuals who create interlinked content that reflect & reinforce the interlinkedness of society & social interaction ... and a record of that performance Social Web 2014, Lora Aroyo!
  6. 6. Slide from Harith Alani
  7. 7. eScience: Analysis of Data Social Web 2014, Lora Aroyo! • the automated or semi-automated extraction of knowledge from massive volumes of data — it is a lot, but it is not just a matter of volume • 3 Vs of Big Data • Volume: #of rows / object / bytes • Variety: # of columns / dimensions / sources • Velocity: # columns / bytes per unit time • more Vs — Veracity: Can we trust this data?
  8. 8. Simple micro rules give rise to complex macro phenomena Social Web 2014, Lora Aroyo! • at microscale an infrastructure of artificial languages and protocols: a piece of engineering • however, interaction of people creating, linking and consuming information generates web's behavior as emergent properties at macroscale • properties require new analytic methods to be understood • some properties are desirable and are to be engineered in, others are undesirable and if possible engineered out
  9. 9. • software applications designed based on appropriate technology (algorithm, design) and with envisioned 'social' construct • usually tested in the small, testing microscale properties • a macrosystem evolving from people using the microsystem and interacting in often unpredicted ways, is far more interesting and must be analyzed in different ways • macrosystems exhibit challenges that do not exist at microscale Social Web 2014, Lora Aroyo! A new way of software development
  10. 10. Example: Evolution of Search Engines 1: techniques designed to rank documents 2: people were gaming to influence algorithms & improve their search rank 3: adapt search technologies to defeat this influence Social Web 2014, Lora Aroyo!
  11. 11. The Web Graph • to understand the web, in good CS tradition, we look at the graph • nodes are web pages (HTML) • edges are hypertext links between nodes • first analysis shows that in-degree and out-degree follow power law distribution => holds for large samples • this gave insight into the growth of the web Social Web 2014, Lora Aroyo!
  12. 12. The (Search) Algorithms • the Web graph also as basis of algorithms for search engines: • PageRank and others assume that inserting a hyperlink symbolizes an endorsement of authority of the page linked to Social Web 2014, Lora Aroyo!
  13. 13. According to Google each day 20-25% of searches have not been seen before, i.e. generate a new identifier thus a new node in the graph more than 20 million new links per day, 200 per second ! do they follow the same power laws & growth models? Social Web 2014, Lora Aroyo!
  14. 14. According to Google each day 20-25% of searches have not been seen before, i.e. generate a new identifier thus a new node in the graph more than 20 million new links per day, 200 per second ! do they follow the same power laws & growth models? validating such models is hard exponential growth of content changes in number & power of servers increasing diversity in users Social Web 2014, Lora Aroyo!
  15. 15. Social Web 2014, Lora Aroyo!
  16. 16. it’s relationships, stupid! not attributes May, 2007 April, 2002 All the world's a net by David Cohen Social Web 2014, Lora Aroyo!
  17. 17. Leveraging recent advances in: • Theories: about social motivations for creating, maintaining, dissolving & re-creating links in multidimensional networks & about emergence of macro-structures • Data: Semantic Web provides technological capability to capture, store, merge & query relational metadata to more effectively understand & enable communities • Methods: qualitative & quantitative for theoretically-grounded network predictions • Computational infrastructure: Cloud computing & petascale applications are critical to face the computational challenges in analyzing the data Social Web 2014, Lora Aroyo!
  18. 18. Network Analysis • is about linking social actors, e.g. systematically understanding and identifying connections • by using empirical data • draws on graphic imagery • relies on mathematical/ computational models • Jacob Moreno - one of the founders of social network analysis; some of the earliest graphical depictions of social networks (1933) Social Web 2014, Lora Aroyo!
  19. 19. Think Networks! • everything is connected to everything else • networks are pervasive - from the human brain to the Internet to the economy to our group of friends • following underlying order and follow simple laws • "new cartographers" are mapping networks in a wide range of scientific disciplines • social networks, corporations, and cells are more similar than they are different • new insights into the interconnected world • new insights on robustness of the Internet, spread of fads and viruses, even the future of democracy. Albert-László Barabási: Linked:The New Science of Networks April, 2002 Social Web 2014, Lora Aroyo!
  20. 20. NYT, 26 Feb 2007
  21. 21. Networks: another perspective :-) • Social Networks: It’s not what you know, it’s who you know • Cognitive Social Networks: It’s not who you know, it’s who they think you know. • Knowledge Networks: It’s not what you know, it’s what they think you know Social Web 2014, Lora Aroyo!
  22. 22. Social Web 2014, Lora Aroyo!
  23. 23. http://webscience.ecs.soton.ac.uk/ L.A. Carr, C.J. Pope,W. Hall,N.R. Shadbolt Social Web 2014, Lora Aroyo!
  24. 24. Web Science is about additionality not the union of disciplines, but intersection Social Web 2014, Lora Aroyo!
  25. 25. Society is Diverse different parts of society have different objectives and hence incompatible Web requirements, e.g. openness, security, transparency, privacy Social Web 2014, Lora Aroyo!
  26. 26. • POWER DISTANCE:The extent to which power is distributed equally within a society and the degree that society accepts this distribution. • UNCERTAINTY AVOIDANCE:The degree to which individuals require set boundaries and clear structures • INDIVIDUALISM vs COLLECTIVISM:The degree to which individuals base their actions on self- interest versus the interests of the group. • MASCULINITY vs FEMININITY:A measure of a society's goal orientation • TIME ORIENTATION:The degree to which a society does or does not value long-term commitments and respect for tradition. Social Web 2014, Lora Aroyo! Understanding the Socio-Cultural
  27. 27. Understanding variations Social Web 2014, Lora Aroyo! • Ecology of theWeb - structure of the environment, producers and consumers • Populations (individuals and species), traits/characteristics, heredity, genotypes and phenotypes • Mechanisms - variation (mutation, migration, genetic drift), selection • Outcomes - adaption, co- evolution, competition, co- operation, speciation, extinction
  28. 28. Social Web 2014, Lora Aroyo! Understanding variations • Ecology of theWeb - structure of the environment, producers and consumers • Populations (individuals and species), traits/characteristics, heredity, genotypes and phenotypes • Mechanisms - variation (mutation, migration, genetic drift), selection • Outcomes - adaption, co- evolution, competition, co- operation, speciation, extinction
  29. 29. Social Web 2014, Lora Aroyo! Understanding variations • Ecology of theWeb - structure of the environment, producers and consumers • Populations (individuals and species), traits/characteristics, heredity, genotypes and phenotypes • Mechanisms - variation (mutation, migration, genetic drift), selection • Outcomes - adaption, co- evolution, competition, co- operation, speciation, extinction
  30. 30. Social Web 2014, Lora Aroyo! Understanding variations • Ecology of theWeb - structure of the environment, producers and consumers • Populations (individuals and species), traits/characteristics, heredity, genotypes and phenotypes • Mechanisms - variation (mutation, migration, genetic drift), selection • Outcomes - adaption, co- evolution, competition, co- operation, speciation, extinction
  31. 31. but How to do the Science? Social Web 2014, Lora Aroyo!
  32. 32. Big Data Owners Who can do macro analysis? • Google, Bing,Yahoo!, Baidu • Large scale, comprehensive data • New forms of research alliance ! ! How Billions ofTrivial Data Points can Lead to Understanding Social Web 2014, Lora Aroyo!
  33. 33. Social Web 2014, Lora Aroyo!
  34. 34. Social Web 2014, Lora Aroyo! The Age of OPEN Data
  35. 35. Social Web 2014, Lora Aroyo! The Age of OPEN Data TRANSPARENCY VALUE ENGAGEMENT
  36. 36. Social Web 2014, Lora Aroyo! The Age of OPEN Data TRANSPARENCY VALUE ENGAGEMENT • common standards for release of public data • common terms for data where necessary • licenses - CC variants • exploitation & publication of distributed, decentralised information assets
  37. 37. Social Web 2014, Lora Aroyo!
  38. 38. Social Web 2014, Lora Aroyo!
  39. 39. Social Web 2014, Lora Aroyo!
  40. 40. Social Web 2014, Lora Aroyo!
  41. 41. Web Observatory Social Web 2014, Lora Aroyo!
  42. 42. slides from: david de roure
  43. 43. slides from: david de roure
  44. 44. Web Science Reflections Is the Web changing faster than our ability to observe it? How to measure or instrument the Web? How to identify behaviors and patterns? How to analyze the changing structure of the Web? Social Web 2014, Lora Aroyo!
  45. 45. Big Bang: Web Information • the assumption of open exchange of information is being imposed on the society • is the Web, and its open access, open data, scientific & creative commons offer a beneficial opportunity or dangerous cul-de-sac? Social Web 2014, Lora Aroyo!
  46. 46. Open Questions • How is the world changing as other parts of society impose their requirements on the Web?, e.g. current examples with SOTA/PIPA,ACTA requirements for security and policing taking over free exchange of information, unrestricted transfer of knowledge • Are the public and open aspects of the Web a fundamental change in society’s information processes, or just a temporary glitch?, e.g. are open source, open access, open science & creative commons efficient alternatives to free-based knowledge transfer? Social Web 2014, Lora Aroyo!
  47. 47. Social Web 2014, Lora Aroyo! Open Questions • do we take Web for granted as provider of a free & unrestricted information exchange? • is Web Science the response to the pressure for the Web to change - to respond to the issues of security, commerce, criminality & privacy? • what is the challenge for Web science in explaining how the Web impacts society?
  48. 48. What can you do as a Computer Scientist? specifically for the SocialWeb Social Web 2014, Lora Aroyo!
  49. 49. image source: http://www.flickr.com/photos/bionicteaching/1375254387/Social Web 2014, Lora Aroyo! Hands-on Teaser • Present your final assignment (social web app)

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