1. CI Labs - Interactive Master Class
suresh.sood@uts.edu.au
linkedin.com/in/sureshsood
@soody
http://www.slideshare.net/ssood/bigdatahuman
7 August 2014
Big Data Perspective on Human Centricity:
Methods of Naturalistic Observation and Behavior

2. Areas for Discussion
1. Background studies – Datafication
2. How to put the human context into business?
3. The toolbox
4. Twitter
5. LIWC and RID psychological dictionaries
6. The predictive empathetic organisation
7. Internet of things and human tuning method
8. Signals of joy study (work in progress)

3. http://datafication.com.au/instagram/

5. MX , 19 July 2011 5

7. Theory and Research on Consumers’ Reports of Interactions with Brands and Experiencing Primal Forces, Suresh
Sood, 2010
7

8. Social CRM integrates social data
8

9. …Blogs are like conversations with friends. You share what you feel
and what excites you about certain things. It's almost as good as
being there. The fact that others can Google your topic and read is
like tuning into a television station.
We all want to know what's out there. Who's doing what,
shopping where and what products help others. Blogs are just
another way to share all the great things, not so great things and
just a part of who we are. An outlet if you will. The blogisphere
community is all connect and we make contacts in many ways.
Through posts, through twitter conversations, through smaller nit
community's, live web casts, and through conferences that we met
in person. We make many friends and help each other with lot of
topics. Many of us are Mom bloggers who stay at home and have
no way of making new friends or communicating with others until
we found blogging. Blogging creates friendships and that's what
makes us real and connected.
40 year old Mom blogger “nightowlmama” (#260)9

10. Datafication
“Datafication refers to the fact that we’re looking at more
aspects of life that we never actually understood as being
informational before…So what we’re seeing with social media
companies is they’re actually datafying aspects of the life that
we never really saw that could be datafied. So for example
Facebook datafies our friendships. Twitter datafies our whispers
or maybe our stray thoughts. And LinkedIn datafies our
professional contacts…what big data means is we are able to
learn things about ourselves at the population level, at a huge
scale, that we never could in the past. So lots of different
disciplines, in one case sociology, totally gets upended. Because
in the past you ran small studies on small groups, now you’re
looking at it in population scale size.
Kenneth Cukier, 2014, “Birth of Datafication”, http://bigthink.com/videos/the-birth-of-datafication

11. Datafication 2 : First National Study of Twitter Usage in Australia
Australians send an average of 234 million tweets per month and 5,000 tweets per minute, a new Twitter
study by advertising agency The Works has found. Aussie females are more likely to retweet than males
and most retweets occur on Mondays, according to the agency's 'datafication' research project. Douglas
Nicol, creative partner and director at The Works, said the study was designed to help marketers talk to
consumers more effectively. “There’s a lot of hype around social media. Using research from datafication,
we are able to equip Australian marketers with no nonsense practical advice,” Nicol said.“This in turn will
help marketers appeal directly to an audience. We believe that in turn, this will boost the way people view
and talk about a brand or product online.”
Lovers, carers and jesters were identified as the top three archetypical personalities on Twitter.
According to the study marketers can talk most effectively to lovers by being passionate, carers by being
gentle and jesters by being mischievous.“If you understand what drives the motivations behind Australians
you will be in a better position to connect with them,” Nicol said. Almost 11% of the Australian population
is on Twitter and of those users 46% are male and 54% are females.
The study also found that Sydney hosted the largest population of Twitter users while Hobart is
responsible for the most tweets per capita.
'Datafication', which was supported by the University of Technology Sydney (UTS), analysed the most
popular words used in Twitter over an eight week period to rank motivations and behaviours on the
social site.
Software created by Dr Suresh Sood, a social media expert at UTS, then analysed the data to produce
the insights into what individuals are doing on Twitter.
'Datafication' is set to launch as a real-time service for the agency’s clients early next year.

12. Datafication 3- First Australian Instagram Study Conducted
www.datafication.com.au

13. Analytic Insights from Millions of Instagram Images
• Sunday at 5pm is the peak usage for Instagram in Australia while on
weekdays 8pm is the most popular posting time
• The average Aussie Instagram user posts 2.3 times a week with around 10
posts being made a month
• Sydney, Brisbane and the Gold Coast are the ‘selfie’ capitals of Australia,
with more pictures of people taking photos of themselves posted than any
other category
• In Melbourne images of food are the most popular Instagram subject, while
in Perth its portrait piccies and in Adelaide it’s more artistic shots.
• Brand recognition on Instagram is low. The most popular hashtag is
#instagood with more than 1.6 million references, however brands such as
McDonald’s, Nike and Holden have been hashtagged less than 15,000 times.

14. Experience Recommender
Data
collection
Individual(Group) Analysis
Find Preference and Behavior
pattern(including Trajectory pattern)
Recommendation
Recommend right experience to
right person ( or group) at right
time and place
Manual Automatic Recommndation

15. Driving decisions from big data has potential of
dehumanizing interactions but balances with
deep understanding of people (customers) to
help and entertain them!

16. How to put the human context into the Business?
• Behavior data  Links human emotions to business -> Analyse footprints left behind.
• What really does customer satisfaction mean ? Is the person actually happy?
• How do we take the emotional dimension into account for customer experience?
• How do we recognize someone is dissatisfied?
• How do we recognize a “distressed” person?
• Do we use text and voice? Will sleeping patterns and eating habits help?
• would you act differently if someone is happy?
• How do you coach employees to see how someone sounds in emotional terms?
• Understanding when distress exists and when a customer needs enhanced service
• Behavior data reveals attitude and intent. This is more predictive of future
opportunities and risk versus historical data

17. Honest Signals

19. “I've learned that people will forget
what you said, people will forget what
you did, but people will never forget
how you made them feel.”
Maya Angelou

20. Challenge Today : Moving from Transactions
Alone to Relationships and Empathy
Current State
= Transactions $$$
We do this stuff well e.g.
Collect payments …
Future State
= Human Empathy (relationships)
We don’t do this really e.g. User
generated content, ratings, reviews, 1:1
dialogue, Distress Signals, Geolocation
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21. Approach
Combine design thinking with
physiological frameworks to build
and develop marketing activities with
purpose and sympathetic of humans.

22. The toolbox
Regressive Imagery
Dictionary (RID)
Twitter
Linguistic Inquiry and
Word Count (LIWC)
Ethnography
Scenarios
Storytelling

23. Evil Plans: Having Fun on the Road to World Domination
by
Hugh MacLeod (Kindle Edition - Feb 17, 2011)

24. Twitter – “Found data and stray thoughts”
• Twitter.com/barackobama,…/theellenshow
• Search.twitter.com
– Near: “taj mahal” within:1mi :)
– Near: “taj mahal” within:1mi :(
– Lang:pa near:”taj mahal” within:15mi
– From:soody, to:soody and citations:@soody
Mass opinion- Find questions people are asking by
viewing tweets only with “?” and no links
Keyword ? –filter:links lang:en

25. 25

26. Instagram Deception (Suspects outside of -20 & +20)
Vine Deception (Suspects outside of -5 and +5)

27. The Newman Model of Deception (Pennebaker et al)
Key word categories for deception mapping:
1. Self words e.g. “I” and “me” – decrease when someone distances
themselves from content
1. Exclusive words e.g. “but” and “or” decrease with fabricated
content owing to complexity of maintaining deception
1. Negative emotion words e.g. “hate” increase in word usage owing
to shame or guilty feeling
1. Motion verbs e.g. “go” or “move” increase as exclusive words go
down to keep the story on track

28. LIWC (analyzewords.com)
• Reveal personality from word usage
• Uses LIWC classification of words

29. TweetPsych (tweetpsych.com/)
• Linguisitic analysis using:
–RID
–LIWC
Note: TweetPsych is not without critics:
http://psychcentral.com/blog/archives/2009/06/18/putting-cool-ahead-of-science-tweetpsych/

30. Photos with Faces
(Bakhshi et al 2014)
• Photos with faces
– 38% more likely to be liked
– 32% more likely to be commented
– Age and gender does not drive engagement!

31. Twitter and Marketing Predictions
• Tweets is “found data” without asking questions
• More meaning than typical search engine query
•
• Large numbers of passive participants in natural settings
• Twitter can predict the stock market (Lisa Grossman, Wired, Oct 19 2010)
• Predict movie success in first few weekends of release
– “…it also raises an interesting new question for advertisers and marketing
executives. Can they change the demand for their film, product or service buy
directly influencing the rate at which people tweet about it? In other words,
can they change the future that tweeters predict?”
Tech Review, http://www.technologyreview.com/blog/arxiv/25000/
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32. Roadmap – Evolution from Existing Operations to Predictive Empathetic
Rigid Flexible Connected
What if conversations continue?
(Adapted from Solis, 2012 and Davenport 2007)
Themes
Silo, rigid
Hoarding info
Vs. collaboration
Freely share info and
Knowledge on internal basis
acting social with customers
2 –way communications
Connected internal and
External. Listening and
Learning. Internal and
external engagement
Shared via hub and
Spoke. Employees
Connected directly to
Customers.
Adaptive
Agile, integrate customer
Experiences and feedback
Loops. Listening and
Learning now become
analyse and insights
Makes sense of data
And transforms into
Intelligence.
Respond in Real time
Predictive
Shift from reactive to
Proactive and predictive
Business uses social
media heavily and is
flexible, connected,
adaptive and predictive
in terms of customer
experiences, distresses
needs and new
opportunities. Predict
scenarios before they
occur maximise
opportunity and limit risk
How can we help lead conversations
and recognise the distress signals?
(predictive recommendation with human focus)
What conversations are next?
Why are these conversations occurring?
What actions are required?
What are the sentiment of conversations?
When and where are conversations taking place?
What conversations are taking place?
Business
Intelligence
32

33. Beacon
Active Card
Shelf Shelf Shelf
Gateway
ServerMonitor
Internet
Supermarket control room

35. Smart Social Card System
Reader/Wifi Gateway and Active Card

36. Smart Sandbag System
smart-dove.com
The first 3 columns are x, y, z axis of gyroscope, then x, y, z
axis of accelerator. These are raw data of 40 repetitions of
shoulder press exercise. Standard Deviation and moving
average algorithm to build the chart and HMM to extract
features and build model of exercise. All models are put into
cloud for trainee exercise scoring.

37. Putting the Human into the Tuning (Method)
1. Get human insights (field observations) of trainer and trainee behavior and
synchronise to output from system
2. Use data mining to develop models enhanced with human judgments versus using
only log files
3. Sync log data to field observations
4. Distill meaningful data features for exercise environment based on qualitative study
of output, experiences of field observers and past experience with other data sets
5. Develop automated detector using classification algorithm
6. Validate detector for new trainees

38. Method avoids limitations
“our work is purely quantitative and based on
observations we had from data…”

39. Signals of Joy Study (June 2014)
• First Australian study of baby feeding experiences
• Unpacks “Mother knows best” at feeding time
• Naturalistic feeding videos (31 hours & 34 mums)
• Exploratory versus Scientific hypothesis method
• Basic drives at feeding time
• Mother/care-giver generated video
• Educators, parents and marketers
• Paucity of research infant/toddler feeding in
naturalistic settings
• Signals babies “give off”
near feeding time -> during –> after

40. Typical Signals (before,during and after)
Crying
Hug
Grin
Disorderly bite
Nurse
Play
Stirring
Stroke
Suckle
Mouth opening
Turninghead
With open arms

41. Signals as Percentage

42. Signal Distribution by Period
Before feeding the baby follows signals of open mouth, cry, open arms, nurse and conversation
During feeding the signals follow stir, turn head, stretch, increase movement, nurse and
conversation.
After feeding the baby is standoffish.

43. Signals by Household
The signals vary by household as some parents or caregivers prefer to nurse or enter into
more conversations with the infant relative to other parents.

44. Typical Signal Sequence
(Behavior Pattern)
Analysis of the signal sequence shows once a baby exhibits abnormal action or
emotion the caregiver provides nursing to calm the baby.

45. Opportunities
1. Working theory/framework of feeding
Include strategies for promoting communication and
language of toddlers
2. Predictive Recommender System
3. Video Feeding Community (white label)
4. Smart Tin
5. Archetype Child and Parents

46. Video Analysis Methodology
Video Exploration via Nvivio for Analysis and Mining of Video

47. The future is impossible to predict. However one
thing is certain :
The company that can excite it’s customers
dreams is out ahead in the race to business
success
Selling Dreams, Gian Luigi Longinotti
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