The document provides an overview of social media monitoring. It discusses collecting data from various social media platforms, mining the data to extract relevant information and discard irrelevant data, and correlating the data to obtain metrics. The mining process involves collecting posts, extracting data, and filtering out irrelevant information. It notes that monitoring requires understanding language and meaning rather than just web analytics. The document presents examples of metrics and monitoring services and discusses issues like precision, recall, filtering, and geolocation.

1. Social
Media
Monitoring
(a
quick
overview)
Hugo
Zaragoza
Director
of
Websays.
March
19th
2013,
hugo.zaragoza@websays.com
@hugo_zara
ESADE
(Barcelona).
@websays

2. Social
Media
Monitoring:
(a
quick
overview)
• The
Problem
What
we
want
to
do,
and
Why
• The
Data
It
all
starts
with
data…
• The
Mining
which
is
collected
and
dis@lled…
• The
Metrics
to
obtain
those
precious
metrics!

3. Before…

4. AIer…

5. What
we
want
to
do:
– Understand
your
clients
problems
– Understand
your
clients
needs
&
wishes
– Detect
crisis
early
and
plan
the
counter-­‐aNack
– Measure
what
you
do!
• Overall
reputaQon
ATE!
CCUR VE!
• Overall
campaign
efforts
• Impact
of
specific
campaigns
• A ECTI
• Influencers
• EFF !
• FAST !
EAP
• C H

6. (Modern)
ReputaQon
Management

7. Example:
ReputaQon
Management
From
a
relaxed
yearly
Public
RelaQons
affair…
to
a
24h.
data-­‐crunching
speed-­‐of-­‐light
business.

8. Why?
(ROI?
RONI?)
From:
Online
ReputaQon:
Get
ready
for
war
hNp://www.slideshare.net/gregfromparis/online-­‐reputaQon-­‐the-­‐art-­‐of-­‐war-­‐2011

9. (Modern)
ReputaQon
Management
• Social
media
is
also
an
opportunity
to
turn
reputaQon
risks
into
successful
markeQng
campaigns
– Public
has
become
skepQcal,
hungry
for
genuine
content
and
emoQon
– Public
is
the
media.
• Increasing
need
to
an=cipate,
detect,
diagnose,
monitor...
• Big
Data
playing
an
increasing
role!

10. Social
Media
Monitoring:
(a
quick
overview)
• The
Problem
What
we
want
to
do,
and
Why
• The
Data
It
all
starts
with
data…
• The
Mining
which
is
collected
and
dis@lled…
• The
Metrics
to
obtain
those
precious
metrics!

11. From:
Online
ReputaQon:
Get
ready
for
war
hNp://www.slideshare.net/gregfromparis/online-­‐reputaQon-­‐the-­‐art-­‐of-­‐war-­‐2011

13. But
mostly…
• TwiNer
• Facebook
• LinkedIn
• Youtube
• Blogs
&
Forums
• Web
Pages
• Online
news

14. Example:
a
Bank

15. Example:
a
Bank

16. Example:
Small
Airline
Company

17. Example:
Small
Airline
Company

18. Example:
Italian
ElecQons
2013
hNp://noQzie.Qscali.it/poliQca/elezioni_2013/netsenQment/

19. Example:
Italian
ElecQons
2013
hNp://noQzie.Qscali.it/poliQca/elezioni_2013/netsenQment/

20. It’s
not
“clippings”..
it’s
complex
heavily
Interconnected
Data

21. It’s
not
“clippings”,
it’s
complex
heavily
Interconnected
Data

22. It’s
not
“clippings”,
it’s
complex
heavily
Interconnected
Data
Hugo
Zaragoza
Center
for
Glob
al
Developmen
t
Sw iss
Re

23. Social
Media
Monitoring:
(a
quick
overview)
• The
Problem
What
we
want
to
do,
and
Why
• The
Data
It
all
starts
with
data…
• The
Mining
which
needs
to
be
collected
and
dis@lled…
• The
Metrics
to
obtain
those
precious
metrics!

24. Mining
Process:
Collect
posts
Extract
data
Discard
the
irrelevant
Correlate
• This
is
NOT
web
analy=cs:
• Understand
the
message
• Deal
with
the
ambigüi=es
of
language
and
meaning

25. Some
Monitoring
Services
(Radian6)
(Based
in
Spain
:)
• There
are
dozens
“monitoring
companies”…
but
they
offerings
are
quite
different!

27. “Desambigüación”
• Sólo el 15% de clippings con la palabra “Camper” son realmente
de y no de
…
• Sólo el 25% de clippings con la palabra “Mango” son realmente
de y no de
…

28. “Precision” and “Recall”
Follow the concept “Barcelona”:
1. Remove all the other Barcelonas (PRECISION)
2. Collect also each street, park, monument, etc. (RECALL)

29. Basic solution:
BOOLEAN queries
PROXIMITY queries
Camper
Camper
–camping
Camper
AND
(shoes
OR
shop)
–camping
(Camper
AND
(shoes
OR
shop))~10
–camping

30. Beyond queries: Statistical Filtering

31. Other issues to discuss
• Banning of sites, authors, locations…
• GeoLocation (& Issues)
(example diagram)

32. Social
Media
Monitoring:
(a
quick
overview)
• The
Problem
What
we
want
to
do,
and
Why
• The
Data
It
all
starts
with
data…
• The
Mining
which
needs
to
be
collected
and
dis@lled…
• The
Metrics
to
obtain
those
precious
metrics!

33. Metrics
(1)
• Example
1:
demo
some
of
the
Websays
Dashboard
metrics
à
• Example
2:
opinion
leader
metrics
• Exmple
3:
amplicficaQon
measures
– hNp://www.slideshare.net/susanhallam/social-­‐media-­‐markeQng-­‐measurement-­‐
susan-­‐hallam
• Example
4:
Websays
PoliQcal
ElecQon
Monitoring

34. Other
presentaQons
on
the
topic:
• Online
ReputaQon:
Get
ready
for
war
– Basics
hNp://www.slideshare.net/gregfromparis/online-­‐reputaQon-­‐the-­‐art-­‐of-­‐war-­‐2011
• Measuring
Your
Success:
Social
AnalyQcs.
– Applause,
AmplificaQon,
etc.
hNp://www.slideshare.net/susanhallam/social-­‐media-­‐markeQng-­‐measurement-­‐
susan-­‐hallam
• Social
AnalyQcs
– Approaches,
metrics.
hNp://www.slideshare.net/equevedo/social-­‐analyQcs

35. Further
Reading
www.kaushik.net/avinash
pages.lithium.com/science-­‐of-­‐social
blog.websays.com/