This document discusses social data and how it can provide insights beyond what traditional social media monitoring allows. It notes that social data allows us to learn about relationships between consumers and how brands fit into those interactions. Social data provides a microscopic view of individual interactions and a macroscopic view of interactions at a network level. Currently, most social media monitoring focuses only on content, keywords, and channels. To fully understand the context and behaviors revealed in social data, new research frameworks and data mining tools are needed.

1. Social Data !
Beyond Keywords
Francesco D’Orazio @abc3d
www.facegroup.com
Big Data Congress, London, November 7 2012

2. We have been
asking the
wrong question

3. “We talk of the relationships
consumers have with our brands as if
they were primary but the data points
to things being otherwise.
Consumers’ most valuable
relationships are not with brands but
with other consumers”
Mark Earls

5. Social data allow us to learn about
the relationships and interactions
between consumers and how brands
can fit into that equation
Cosmic 140 © Information Architects, Inc. 2010

6. Social data allows us to see how interactions play
out at individual level (microscope)

7. Social data allow us to see how
interactions play out at network level
(the macroscope)

8. The code
behind a
tweet

9. Currently we look at social media data !
like a butcher looks at a carcass
Cutting by
keywords
Aggregating
by channels
Weighting
by influence

10. © Alexandre Farto aka Vhils 2010
Most Social Media Monitoring
platforms focus on just Content

11. © Alexandre Farto aka Vhils 2010
But we also wanted to understand
Context and Behaviour

12. © Thomas Doyle 2010
So we designed new research frameworks
and social data mining tools

13. Social Media vs Social Data
Social media is the set
of applications and
platforms allowing Social data is the
people to participate in collective information
online social activities
produced by millions of
people as they actively
participate in online
social activities.
13

14. Social Media vs Social Data
14

15. Social Media vs Social Data
15

16. Dimensions of Social Data
Content
Demographics
Behaviours
Social Graph
Interest Graph
16

17. What makes social data ‘big’?
Large volumes
Real-time collection
Real-time analysis
Interactivity
Full-Text
Weavrs Emotion Map

18. 1!
TOPIC/BRAND tracking
By keywords
Share of Voice

19. 2!
AUDIENCE mapping > brand graph
By keywords
By audience
Share of Voice
Share of Mind

21. 3!
CLUSTER tracking > real-time
segmentation
Dynamic
Segments
Real-time Audience
Insights

22. 4!
CONTENT diffusion > where, how
and who is sharing the content

23. 5!
INFLUENCE mapping > hubs &
connectors by volumes, visibility
and engagement
Influence by Volume
Influence by Visibility
Influence by Engagement

24. 6!
SOCIAL CRM > efficient
workflow with automatic
labeling and predictive modeling

25. 7!
Augmentation > layering social
data on a Panel or a Community

26. 8! SOCIAL SIMULATIONS
20 participants
20 alpha-bots
designed by them
20 beta-bots
tested and fine-
tuned by us
20 beta-bots cloned 20 times each (400 beta-clones active
over a 2 weeks period)

27. 9!
REALITY MINING

30. Social science
+
Computational Social data
Social Science +
Computation

31. CONTINUOUS
ITERATION: play with a
wider range of
hypothesis to solve a
problem.
Pulsar Social Data Mining platform
31

32. CONTINUOUS ITERATION: starting with 0 hypothesis
> finding out what I don’t know I don’t know
Is Big Data producing a new epistemology?
32

33. Thank you
francesco d’orazio, @abc3d!
www.facegroup.com