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Center For telecomm research workshop 2018

A brief overview of the work done across the three years of my Ph.D at King's College

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Center For telecomm research workshop 2018

  1. 1. Quantifying the intangible from Data. Sagar Joglekar, Ph.D. Candidate, Computer Science @SagarJoglekar 1
  2. 2. What is the intangible? ● Drives human cognitive processes. (aesthetics) ● Subject to a deeper perception (Safety, beauty) ● Grounded in context, culture and community. (Support) 2
  3. 3. What “Intangible” do you “Quantify”? ● Online: ○ How do we share and seek help online ? ● Offline ○ Can we quantify how citizens perceive spaces around them? ○ Can we use this knowledge to create better cities ? 3
  4. 4. Our online lives Part I 4
  5. 5. Online life is stream of gushing information 5
  6. 6. 6
  7. 7. Internet Anxiety Depression Support Comfort 7
  8. 8. Why online support? 8
  9. 9. Data: • Online support communities for Mental distresses and respiratory distress • More than 2 million posts over a period of 5 years • More than 100,000 active users between the two. 9
  10. 10. Key Question: • Can we operationalize offline theories of Social support, online? • Can we quantify these processes? 10
  11. 11. Technical challenges Find established theories to draw parallels from Find effective operationalization of theories Drawling Causal links* 11 Technical challenges Find established theories to draw parallels from Find effective operationalization of theories Drawling Causal links*
  12. 12. User Graphs: • Node => Users • Edges => Who replies to whom • Edge weights => Topical alignment between user pairs 12
  13. 13. Topic Modelling • Modeling corpus of text as a mixture of topics • Each topic is a mixture of words. • Represent text as a vector of topics. • Easier understanding of salient phrases
  14. 14. Taking offline support theories, online Social Capital* -> Embeddedness of help seekers (Topology of community) Critical consciousness *-> Topical relevance of responses (LDA topical coherence of posts) Community capacity* -> Urgency, Mobilization, Involvement (temporal aspects of activity) *Community organizing and community building for health– Meredith Minkler et.a 1997 15
  15. 15. How embedded are the support seekers ? Life sucks, I want to kill myself 16
  16. 16. How embedded are the support seekers ? Support seekers are Very Central 17
  17. 17. Topical Alignment: Life sucks, I want to kill myself Don’t say that, you can make your life better. Life is all about creating experiences. Where: 18
  18. 18. How topically aligned are the conversations? Responses are Topically aligned 19
  19. 19. Propensity to Answer: Z-score Questions = 1/3 Answers = 2/3 20 Supportive agents develop over time
  20. 20. Triads and cohesion. 21
  21. 21. Supportive agents add cohesion to the conversations 22
  22. 22. Key Takeaways • Online support communities shows characteristics of offline support communities • Online supportive conversations show characteristics of offline supportive conversations • People evolve and take over the mantle as they stay on 23
  23. 23. Why is this important ? Finding supportive agents in an online community Learn language traits in online support. Fill gaps in the care provider’s lexicon. 24
  24. 24. Our offline Lives Part II 25
  25. 25. Spaces we occupy, influence our mental well being. 26 So what if A.I. could help us design them ?
  26. 26. Why Urban design? Well Being City Planning Profit!! Beauty, Safety, Security 27
  27. 27. The challenge A.I. decisions are often ``Black Box’’ in nature Decisions need to be explained in the language of users (Architects, designers, planners) Framework needs to be quickly adaptable and scalable 28
  28. 28. The Data 29
  29. 29. How do we do it? http://facelift.datadissonance.org/ 30
  30. 30. Transformation Example: Original Template Transform Beautified 31
  31. 31. Do humans find results actually beautified? Original - Transformed Mturk comparison At least 3 votes per comparison. Overall agreement 78% 32
  32. 32. The Explainability Metrics from Urban planning: •Walkability •Openness •Complexity •Green Spaces 33
  33. 33. 34
  34. 34. 35
  35. 35. Expert insights and feedback 36
  36. 36. 37
  37. 37. Still not perfect Generative models are very tricky to train No control over how much to maximize to produce realistic image Now way as of now to incorporate zoning laws 38
  38. 38. Why is this relevant ? 39
  39. 39. Milestones: • Make human conditions and human processes part of the learning function • Domain specific transparency in A.I. Systems 40
  40. 40. ML NLP REC SYSCURATION OTHERS ……………. INTERNET “Learning” From Humans 41
  41. 41. ML NLP REC SYSCURATION OTHERS INTERNET “Learning” For Humans 42
  42. 42. 43 Nishanth Sastry (Primary Advisor) Miram Redi, Wikimedia research Daniele Quercia, Bell Labs UK Luca Aiello, Bell Labs UK Anna Simoni, Blizzard Institute, QMUL Pietro Panzarasa, QMUL
  43. 43. 44 Thank You!! https://sagarjoglekar.github.io

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