SDoW2010 keynote

1
Harith Alani
Knowledge Media institute,
The Open University, UK
Workshop on Social Data on the Web (SDoW)
ISWC, Shanghai, 2010
http://twitter.com/halani
http://delicious.com/halani
http://www.linkedin.com/pub/harith-alani/9/739/534
I know what you did last conference
Tracking and analysis of social networks

Sensor & Social Networks
2
www.nabaztag.com
www.withings.com
The Canine Twitterer
“Having my daily workout.
Already did 15 leg lifts!”

Tag-Along Marketing
The New York Times,
November 6, 2010
“Everything is in place for location-based social
networking to be the next big thing. Tech
companies are building the platforms, venture
capitalists are providing the cash and marketers
are eager to develop advertising. “
Location Sensors & Social Networking
3

Localised social networking with Facebook
5
200M FB mobile users.
Visit FB twice as much

Tracking of F2F contact networks
6
TraceEncounters - 2004
Sociometer, MIT, 2002
-  F2F and productivity
-  F2F dynamics
-  Who are key players?
-  F2F and office distance

Sociopatter deployments
8
Science Gallery,
Dublin
2 months, ~30K
people
25C3 conference
“nothing to hide”
Berlin
3 days, ~600 people
Italy, 10+ startups
5 weeks,
~250 people
hospital in Italy,
12 days, ~250 people &
~50 hand-washing sinks!

Offline social networks
9
by Ciro Cattuto
From a small conference
at ISI, Turin

10
•  Similarity
features
–  Country of
origin
–  Seniority
–  .. Age? Role?
Projects?
Interests?
SR
SR
students
students
JR•  What other
info can we
get to help us
understand
these network
dynamics?
Offline social networks

Offline + online social networking
11ESWC2010
Where
should I go?
Where have I
met this guy?
Anyone I
know here?
Who should
I talk to?

12
Social Web Communities
Sept. 2008

<?xml version="1.0"?>!
<rdf:RDF!
xmlns="http://
tagora.ecs.soton.ac.uk/schemas/
tagging#"!
xmlns:rdf="http://www.w3.org/
1999/02/22-rdf-syntax-ns#"!
xmlns:xsd="http://www.w3.org/2001/
XMLSchema#"!
xmlns:rdfs="http://www.w3.org/
2000/01/rdf-schema#"!
xmlns:owl="http://www.w3.org/
2002/07/owl#"!
xml:base="http://
tagora.ecs.soton.ac.uk/schemas/
tagging">!
<owl:Ontology rdf:about=""/>!
<owl:Class rdf:ID="Post"/>!
<owl:Class rdf:ID="TagInfo"/>!
<owl:Class
rdf:ID="GlobalCooccurrenceInfo"/>!
<owl:Class
rdf:ID="DomainCooccurrenceInfo"/>!
<owl:Class rdf:ID="UserTag"/>!
<owl:Class
rdf:ID="UserCooccurrenceInfo"/>!
<owl:Class rdf:ID="Resource"/>!
<owl:Class rdf:ID="GlobalTag"/>!
<owl:Class rdf:ID="Tagger"/>!
<owl:Class rdf:ID="DomainTag"/>!
<owl:ObjectProperty
rdf:ID="hasPostTag">!
<rdfs:domain
rdf:resource="#TagInfo"/>!
</owl:ObjectProperty>!
<owl:ObjectProperty
rdf:ID="hasDomainTag">!
<rdfs:domain
rdf:resource="#UserTag"/>!
<owl:ObjectProperty
rdf:ID="isFilteredTo">!
<rdfs:range
rdf:resource="#GlobalTag"/>!
<rdfs:domain
rdf:resource="#GlobalTag"/>!
<owl:ObjectProperty
rdf:ID="hasResource">!
<rdfs:domain rdf:resource="#Post"/>!
<rdfs:range =…!
Live Social Semantics (LSS):
RFIDs + Social Web + Semantic Web
•  Integration of physical presence and online information
•  Semantic user profile generation
•  Logging of face-to-face contact
•  Social network browsing
•  Analysis of online vs offline social networks

Components of LSS
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
interests
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense
Repository
JXT Triple Store
Extractor
Daemon
Connect API
Visualization Web Interface Linked Data
Local
Server
ID
aders
Real-World
Contact Data
Social
Semantics
Communities of Practice
Social Tagging
Social Networks
Contacts
mbid -> dbpedia uri
tag -> dbpedia uri
Proﬁle BuilderPublications
Aggregator
RDFcache
ID
dges
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
T
JXT Triple Store
Extractor
Daemon
Connect API
Web-basedSystemsRealWorld
Visualization Web Interface
Local
Server
RFID
Readers
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
m
Aggregator
RDFcache
RFID
Badges
Web interfaceLinked data Visualization
URIs
tags
social semantics
contacts data
data.semanticweb.org
rkbexplorer.com
publications, co-authorship networks
!"
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14

SW sources
15
proceedingschair
chair
author
CoP
conference

Social networking systems
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
interests
Delicious
Flickr
LastFM
acebook
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense
Repository
JXT Triple Store
Extractor
Daemon
Connect API
Local
Server
D
aders
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
mbid -> dbpedia uri
tag -> dbpedia uri
Aggregator
RDFcache
D
dges
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
T
JXT Triple Store
Extractor
Daemon
Connect API
Local
Server
RFID
Readers
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
m
Aggregator
RDFcache
RFID
Badges
URIs
tags
social semantics
contacts data
rkbexplorer.com
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16

17
Social and information networks
17

18
Merging social networks
18
FOAF

19
Tag Filtering Service
Semantic modeling
Semantic analysis
Collective intelligence
Statistical analysis
Syntactical analysis

21
Tag Disambiguation
•  Term vector similarity
•  Term vector from tag co-occurrence
•  Term vector for each suggested Dbpedia disambiguation page
21
apple, tree, fruit, ..
apple,film,1980,..
Co-occurring
tags in the
whole
folksonomy
User tags
regardless of
the resource
(Period of
Time)
co-occurring
tags in the
same resource
User Tags co -
occurring in the
same resource
http://grafias.dia.fi.upm.es:8080/Sem4Tags/

23
Tags to User Interests
•  Based on 72 POIs verified by users
23
Phd candidates?
Global Delicious Flickr lastFM
Concepts
generated
2114 1615 456 43
Concepts
removed
449(21%) 307(19%) 133(29%) 9(21%)
Based on 11 users
who edited their
POIs at HT09

24
From raw tags and social relations
to Structured Data
User raw
data
Structured
data
Collective
intelligence
ontologies
Semantic
data

triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
interests
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense
Repository
JXT Triple Store
Extractor
Daemon
Connect API
Local
Server
ID
aders
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
mbid -> dbpedia uri
tag -> dbpedia uri
Aggregator
RDFcache
ID
dges
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
T
JXT Triple Store
Extractor
Daemon
Connect API
Local
Server
RFID
Readers
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
m
Aggregator
RDFcache
RFID
Badges
URIs
tags
social semantics
contacts data
rkbexplorer.com
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25

26
RFIDs for tracking social contact
26

27
Convergence with online social networks
27

People contact  RFID  RDF Triples
28
F2FContact
hasContact

contactWith

contactDate
contactDura0on

XMLSchema#date

XMLSchema#0me

contactPlace

Place
foaf#Person1
foaf#Person2

triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
interests
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense
Repository
JXT Triple Store
Extractor
Daemon
Connect API
Local
Server
ID
aders
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
mbid -> dbpedia uri
tag -> dbpedia uri
Aggregator
RDFcache
ID
dges
Delicious
Flickr
LastFM
Facebook
semanticweb.org
rkbexplorer.com
T
JXT Triple Store
Extractor
Daemon
Connect API
Local
Server
RFID
Readers
Real-World
Contact Data
Social
Semantics
Social Tagging
Social Networks
Contacts
m
Aggregator
RDFcache
RFID
Badges
URIs
tags
social semantics
contacts data
rkbexplorer.com
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29

32
Real-time F2F networks with SNS links
http://www.vimeo.com/6590604

33
Deployed at:
Live Social Semantics
Data analysis
•  Face-to-face interactions across scientific conferences
•  Networking behaviour of frequent users
•  Correlations between scientific seniority and social networking
•  Comparison of F2F contact network with Twitter and Facebook
•  Social networking with online and offline friends

Analysis of LSS Results
The New Yorker 2/11/2008
34

Characteristics of F2F contact network
•  Degree is number of people with whom the person had at least one F2F
contact
•  Strength is the time spent in a F2F contact
•  Edge weight is total time spent by a pair of users in F2F contact
35
Network
characteristics
ESWC 2009 HT 2009 ESWC 2010
Number of users 175 113 158
Average degree 54 39 55
Avg. strength (mn) 143 123 130
Avg. weight (mn) 2.65 3.15 2.35
Weights ≤ 1 mn 70% 67% 74%
Weights ≤ 5 mn 90% 89% 93%
Weights ≤ 10 mn 95% 94% 96%

Characteristics of F2F contact events
Contact
characteristics
ESWC 2009 HT 2009 ESWC 2010
Number of
contact events
16258 9875 14671
Average contact
length (s)
46 42 42
Contacts ≤ 1mn 87% 89% 88%
Contacts ≤ 2mn 94% 96% 95%
Contacts ≤ 5mn 99% 99% 99%
Contacts ≤ 10mn 99.8% 99.8% 99.8%
F2F contact pattern is very similar for all three conferences

F2F contacts of returning users
10
1
10
210
1
10
2
10
3
10
4
10
510
3
10
4
ESWC2010
10
1
10
2
10
3
10
4
10
5
ESWC2009
10
1
10
2
10
3
10
4
Degree
Total interaction time
Links’ weights
37
•  Degree: number of other
participants with whom an attendee
has interacted
•  Total time: total time spent in
interaction by an attendee
•  Link weight: total time spent in F2F
interaction by a pair of returning
attendees in 2010, versus the same
quantity measured in 2009
Time spent on F2F networking by frequent
users is stable, even when the list of
people they networked with changed
ESWC 2009 &
ESWC 2010
Pearson Correlation
Degree 0.37
Total F2F
interaction time
0.76
Link weight 0.75

Average seniority of neighbours in F2F networks
0 5 10
seniority (number of papers)
0
1
2
3
4
5
Averageseniorityofneighbors
senn
senn,w
senn,max
38
•  No clear pattern is observed
if the unweighted average
over all neighbours in the
aggregated network is
considered
•  A correlation is observed
when each neighbour is
weighted by the time spent
with the main person
•  The correlation becomes
much stronger when
considering for each
individual only the neighbour
with whom the most time was
spent
Avg seniority of the neighbours
with weighted averages
Seniority of user with strongest link
Conference attendees tend to networks with others of similar
levels of scientific seniority

Presence
of
A<endees
HT2009

Importance
of
the
bar?

Popularity
of
sessions?

par0cular
talks?

Number
of
cliques
HT2009

Offline networking vs online networking
41
•  people who have a large number of friends on Twitter and/or Facebook don’t seem to
be the most socially active in the offline world in comparison to other SNS users
Users with Facebook and Twitter accounts in ESWC 2010
Twitterers Pearsons Correlation
Tweets – F2F Degree -0.14
Tweets – F2F Strength -0.11
Twitter Followees – F2F Degree -0.12
No strong correlation between amount of F2F
contact activity and size of online social networks
users

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Scientific seniority vs Twitter followers
42
•  Comparison between people’s scientific seniority and the number of people following
them on Twitter
People who have the highest number of Twitter followers are not
necessarily the most scientifically senior, although they do have high
visibility and experience
users

Conference Chairs
all
participants
2009
chairs
2009
all
participants
2010
chairs
2010
average degree
average strength
55
8590
77.7
19590
54
7807
77.6
22520
average weight
average number of
events per edge
159
3.44
500
8
141
3.37
674
12
•  Conf chairs interact with more distinct people (larger average degree)
•  Conf chairs spend more time in F2F interaction (almost three times as much
as a random participant)
Conference chairs meet more people and spend 3 times
as much time in F2F networking than other users

Networking with online and offline ‘friends’
Characteristics all users coauthors Facebook
friends
Twitter
followers
average contact
duration (s)
42 75 63 72
average edge weight
(s)
141 4470 830 1010
average number of
events per edge
3.37 60 13 14
•  Individuals sharing an online or professional social link meet much more
often than other individuals
•  Average number of encounters, and total time spent in interaction, is highest
for co-authors
F2F contacts with Facebook & Twitter friends were respectively %50 and
%71 longer, and %286 and %315 more frequent than with others
They spent %79 more time in F2F contacts with their co-authors, and they
met them %1680 more times than they met non co-authors

Twitterers vs Non-Twitterers
•  Time spent in conference rooms
–  Twitter users spent on average 11.4% more time in the
conf rooms than non-twitter users
•  Number of people met F2F during the conference
–  Twitter users met on average 9% more people F2F
•  Duration of F2F contacts
–  Twitter users spent on average 63% more time in F2F
contact than non twitter users
45

46
What about the
individuals?

Behaviour of individuals – micro level analysis
47
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•  WeGov is producing tools, platforms and methodologies for policy makers to
interact directly and indirectly with the public using SNS
–  Monitor and analyse discussions and opinions on SNS
–  Semantically model and analyse SNS users activities
–  Inject information and link relevant info on separate SNS
–  ‘what, when, where, how’ when using SNS
–  Produces for privacy, legal, and ethical issues
48http://www.wegov-project.eu/
eParticipation is about reconnecting ordinary people with
politics and policy-making [….] Governments and the EU
institutions working with citizens to identify and test ways of
giving them more of a stake in the policy-shaping process,
such as through public consultations on new legislation
•  Problem is that people don’t use government portals, minister blogs, opinion
collecting web sites
•  Instead, they use social media

49
•  How many do you
recognise? Use?
•  Which ones still exist?
•  Which are well and healthy,
which are weakening and
collapsing?
•  How to do analysis on
huge scale? real-time?
•  How can we predict their
future evolution?
•  Which ones are good/bad
ROI?

•  Problem of managing the health of online communities using real-time
analysis of huge community data sets
–  Current solutions fail to meet challenges of scale and growth
–  Lack of support for understanding and managing the business, social and economic objectives
of users, providers and hosts
•  ROBUST will combine community analysis, risk management, and
community forecasting in large scale to benefit individual users and
businesses
•  Create models and methods for describing, understanding and managing the
users, groups, behaviours and needs of online communities
•  Large scale simulation for predicting impact of user behaviour and policies
on community evolution and the risks and opportunities for online business
•  Scalable real time tools and algorithms for community analysis including
dynamics and interactions
50

Thanks to
References:
•  Barrat, A., et al. (2010) Social dynamics in conferences: analyses of data from the Live Social Semantics
application. In 9th International Semantic Web Conference (ISWC), China.
•  Szomszor, M., et al. (2010) Semantics, Sensors, and the Social Web: The Live Social Semantics experiments.
Extended Semantic Web Conference (ESWC), Crete.
•  Broeck, W., et al. (2010) The Live Social Semantics application: a platform for integrating face-to-face presence
with on-line social networking, Workshop on Communication, Collaboration and Social Networking in Pervasive
Computing Environments (PerCol), IEEE PerCom, Mannheim.
•  Alani, H., et al. (2009) Live Social Semantics. In 8th International Semantic Web Conference (ISWC), US.
51
Alain Barrat
CPT Marseille & ISI
Martin Szomszor
CeRC, City University, UK
Wouter van Den Broeck
ISI, Turin
Ciro Cattuto
ISI, Turin
SocioPatterns.org
rkbexplorer.org
Gianluca Correndo
Ivan Cantador
Andrés Garcia
Organisers of HT 2009,
ESWC 2009, ESWC 2010
All LSS participants!

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