Trondheim bigdata Talk

Geo-Temporal-(Social?) Data?!
Social Events in Social Media!
!

Massimiliano Ruocco!!
@ruoccoma!
ruoccoma dot gmail dot com!
Telenor Digital (SWEng), NTNU (PhD)!
Who am I?

Digital
Footprint
Social
Geographical
Temporal

Scenario 1
User visiting a touristic spot.
Takes a picture of it.
Posts it (+ comments) on FB/Flickr/Twitter.

Scenario 2
User watching a football match at the stadium.
Takes a picture of the match (+ comments).
Posts it on FB/Flickr/Twitter

Scenario 3
User reading newspaper.
Comments some trending facts (i.e.: crisis in
Middle East).
Posts it Twitter.

Scenario 1
User visiting a touristic spot.
Takes a picture of it.
Posts it (+ comments) on FB/Flickr/Twitter.
Scenario 2
User watching a football match at the stadium.
Takes a picture of the match (+ comments).
Posts it on FB/Flickr/Twitter
Scenario 3
User reading newspaper.
Comments some trending facts
Comments some trending facts (i.e.: crisis
in Middle East).
Posts it Twitter.
Event!
<<my trip in Naples>>
Event!
<<semifinal CL>>
Event!
<<crisis in middle east>>

Events in Social Media
From raw data to events

Flickr as data source
+250M geotagged
3.5M uploaded/day
87M users
6.000M pics

POI-related Tag Extraction
Tag Point Pattern
Geo distribution of pictures tagged with a certain term
Point Process Theory Extended
rigorous statistic

Point Pattern Analysis Objective
Determine
If
a
given
set
of
spa1al
points
(Spa1al
Point
Pa6ern)
exhibits

clustering,
regularity
or
are
randomly
distributed
within
an
area
A

Ripley’s K-function
Summarizing
a
spa1al
point
pa6ern
over
a
scale
h

CSR Test
-‐  K(h)
>πh2
clustering
at
scale
h

-‐  K(h)
<πh2
dispersion
at
scale
h

Ripley’s K-function
Summarizing
a
spa1al
point
pa6ern
over
a
scale
h

CSR Test
-‐  D(h)
>h
clustering
at
scale
h

-‐  D(h)
<h
dispersion
at
scale
h

Ripley’s Cross-K-function
Summarizing
a
spa1al
correla1on
over
two
tag
point
pa6ern
over
a
scale
h

Spa1al
distribu1on
of
the
Tag
Point
Pa6erns
related
to
the
tag
Old Naval College

and
the
tag
University of Greenwich at
two
diﬀerent
zooming

Summarizing
a
spa1al
correla1on
over
two
tag
point
pa6ern
over
a
scale
h

CSR Test
-‐  L12(h)
>0
a6rac1on
at
scale
h

-‐  L12(h)
<0
repulsion
at
scale
h

Spa1al
distribu1on
of
the
Tag
Point
Pa6erns
related
to
the
tag

Old Naval College
and
the
tag
University of Greenwich !

Summarizing
a
spa1al
correla1on
over
two
tag
point
pa6ern
over
a
scale
h

CSR Test
-‐  K12(h)
>πh2
a6rac1on
at
scale
h

-‐  K12(h)
<πh2
repulsion
at
scale
h

Spa1al
distribu1on
of
the
Tag
Point
Pa6erns
related
to
the
tag

Old Naval College
and
the
tag
University of Greenwich !

Objective

Derive
indicators
es1ma1ng
clustering
tendency

of
Tag-‐point
pa6ern

Applications

1
-‐
Extrac2ng/Ranking
social
tags
indica1ng
geographical
POI

2
-‐Enhance
query
expansion
in
combina1on
with
other
metadata

Size
Inhomogeneity

Real Data: Challenges

Example of point pattern of the tag night !
Data Inhomogeneity

Data Inhomogeneity
Related underlying Picture Point Pattern !

Data Inhomogeneity
Example of point pattern of the tag night over the
underlying distribution!

Size
1
-‐
Subsampling 
2-‐
bigmatrix*
and
biganalytics** (R)

Inhomogeneity

Case-‐Control
Analysis

Real Data: Challenges
*Kane
M.,
Emerson
J.,
“The
R
Package
bigmemory:
Suppor2ng
Eﬃcient
Computa2on
and
Concurrent
Programming
with
Large

Data
Sets”
(2010).
Journal
of
Sta1s1cal
SoVware.

**Kane
M.
et
al.,
“Scalable
Strategies
for
Compu2ng
with
Massive
Data”
(2013),
Journal
of
Sta1sc1cal
SoVware.

2
-‐
Maximum
func1on
value
K(h)
over
the
scale

1
-‐
Area
underlying
K(h)
in
the
considered
scale

Derived Geo-Features
Set
1

Set
2

wwt! grdstreeteatportlan! britishlibrary! astoria!
Table - Top-5 tags extracted ranked by MaxValue and Area

Event-related Image Search
Geo(Temporal)-tagged resources supporting IR

Expansion terms selection over three dimensions

Text
Features

(baseline)

-‐  TF,
IDF,
DF

Time
Features

-‐  Kurtosis:
Peakdness

-‐  Autocorrela-on:
Randomness

-‐  Cross-‐Correla-on

Geo
Features

-‐  Good
expansion
=
spa1ally

correlated
with
qi

-‐  Calculated
for
each
1le
Tqi
,e

Derived
from
q-‐point
pa6er
&
e-‐point
pa6ern
&
(q+e)-‐point
pa6ern

Scalability?

Scalability?

Which Tile?
1 - Best tile + calculate confidence value
2 - Confidence values combination from different tiles:
Map Reduce fashion + Solr Search engine

Results

Table – Comparison of the classification performances. The best scores in each
column are type-set boldface.

Results

Fig – Comparison of MAP improvements as function of number of feedback docs

Yes! But…BigData?
•  Bigmatrix + Biganalytics in R
•  Subsampling
•  World map divided in tiles
•  Map-Reduce fashion algorithm

Cool stuff!
Increasing volume of Geo-Temporal Data from
Social Media ++
Amazing things!
–  Visualization
–  Location-based recommendation
–  Dicovering trends!

Thanks! Questions?
M.
Ruocco
and
H.
Ramampiaro,
(2014),
"Geo-‐Temporal
Distribu2on
of
Tag
Terms
for
Event-‐Related
Image

Retrieval".
In
Informa1on
Processing
&
Management
Journal
(IPM).
Elsevier.

M.
Ruocco
and
H.
Ramampiaro,
(2014),
"A
Scalable
Algorithm
for
Extrac2on
and
Clustering
of
Event-‐
Related
Pictures".
In
Mul1media
Tools
and
Applica1ons
Journal
(MTAP),
Springer.

M.
Ruocco
and
H.
Ramampiaro,
(2013),
"Exploring
Temporal
Proximity
and
Spa2al
Distribu2on
of
Terms

in
Web-‐based
Search
of
Event-‐Related
Images".
In
Proc.
of
the
24th
ACM
Conference
on
Hypertext
and

Social
Media
(HT
2013).
ACM
Press.

M.
Ruocco
and
H.
Ramampiaro,
(2012),
"Exploratory
Analysis
on
Heterogeneous
Tag-‐Point
PaQerns
for

Ranking
and
Extrac2ng
Hot-‐Spot
Related
Tags".
Proceedings
of
the
5th
ACM
SIGSPATIAL
Interna1onal

Workshop
on
Loca1on-‐Based
Social
Networks
(LBSN
2012).
ACM
Press.

ruoccoma@gmail.com!

Evaluation of top-100 extracted tags: P@n

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