Tim Budden talks at Digital Henley #4 on Wednesday 4th May regarding the Expanding Digital Universe, Privacy by Design and automated processing to unlock consumer information.

1. Unlocking insights
from social data
Tim Budden
VP Data Science at DataSift

2. Drew Conway’s Data Science Venn Diagram
2

3. Expanding data universe
Agenda
1
2
3
4
Evolution of social data
Social data analytics
Privacy by design
5 Examples

4. Expanding digital universe1

5. 5
Expanding digital universe
Expanding
human data
universe

6. Evolution of social data2

7. The evolution of social data
From public to non-public spaces:
Public Walled 1 to 1 Image-based

8. Public
Where brands and consumers most commonly engage
directly. This is where customer support and brand
perception can be addressed directly by a brand.

9. Walled garden
Users engage each other in a non-public but large network. This
is where users are more candid about their aspirations and
attitudes toward brands.

10. 1 to 1
Users engage each other directly on a one-to-one or small
group basis. Thus far this space has been considered largely
off limits to brands.

11. Image-based
Public spaces where people showcase their best
visual content.

12. 12

13. Social data analytics3

14. 14
Business applications of social media

15. 15
Volume and velocity
Natural Language
Privacy
2.1B People Globally
on Social Networks
Challenges to extracting
insights from data
Unlocking Insights from 2.1B People on Social Networks

16. Example analytics project: Run on the banks?
16
Bank of England experimented with trying to predict a bank run in the days preceding the Scottish independence referendum
Observed spike on 15 September of tweets mentioning “RBS” and “run”
Scottish
independence
referendum

17. 17
Run on the banks?
“Great run there! Arm tackles don’t bring down good RBs”

18. Ambiguity in natural language
18

19. Synonymity in natural language
19

20. word2vec
20
king - man
=
queen - woman
Berlin - Germany
=
Paris - France
https://spacy.io/demos/sense2vec?NFL

21. Privacy by design4

22. How can information useful to business be extracted
from non-public spaces, while wholeheartedly
respecting people’s privacy?

23. Think in terms of audiences and demographics not individuals
23
Djokovic
Federer
female male
Come on
Djokovic! Come on
Roger!
Go for it
Novak!
Great shot
Federer!
Henman Hill at Wimbledon

24. Think in terms of topics and attitudes not verbatim
Sumptuous
interior!
Beautiful
lines!
Lots of
storage

25. PYLON: Anonymised and Aggregated insights
25
Text available to algorithms
but not output
Aggregated results
Audience sizes are
quantised:
minimum bucket
size and intervals
Anonymised: all
Personally
Identifiable
Information
(PII) is dropped
A
P
I
DS

26. CONTENT
Gender: Male
Age Range: 35-44
Region: California, USA
CONTENT
Negative
Neutral
Positive
DEMOGRAPHICS
SENTIMENT
Automatic classification
of related topics
e.g. Star Wars VII (Film)
TOPIC ANALYSIS
CONTENT
LINKS
Analyze
URLs shared
across Facebook
Engagement and Demographics
around Likes, Comments and Shares
ENGAGEMENT
Can’t wait to take the kids to watch Star Wars VII
CONTENT
Privacy-safe
aggregate analysis of
text
TEXT ANALYSIS
Topic Data is Multi-Dimensional.
Build Insights into Content, Engagement, Audiences

27. Examples5

28. Analysing and visualising automotive
28
websequencediagrams.com

29. Writing the script with Facebook topic data
29

30. 30
Volume and velocity
Natural Language
Privacy
2.1B People Globally
on Social Networks
Challenges to extracting
insights from data
Unlocking Insights from 2.1B People on Social Networks

31. THANK YOU