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
4
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
7
SocioPatterns platform
7
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
11 ESWC2010
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>! <owl:ObjectProperty rdf:ID="isFilteredTo">! <rdfs:range rdf:resource="#GlobalTag"/>! <rdfs:domain rdf:resource="#GlobalTag"/>! </owl:ObjectProperty>! <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
tag
s,
ne
two
rks
interests
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Web interface Linked data Visualization
URIs
tag
s
social semantics
contacts data
data.semanticweb.org
rkbexplorer.com
publications, co-authorship networks
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14
SW sources
15
proceedings chair
chair author
CoP
conference
Social networking systems
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tag
s,
ne
two
rks
interests
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Web interface Linked data Visualization
URIs
tag
s
social semantics
contacts data
data.semanticweb.org
rkbexplorer.com
publications, co-authorship networks
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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
20
Tag Filtering Service
21
Tag Disambiguation • Term vector similarity
• Term vector from tag co-occurrence
• Term vector for each suggested Dbpedia disambiguation page
21
apple, tree, fruit, ..
appl
e, fi
lm, 1
980,
.. 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/
22
From Tags to Semantics
22
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
tag
s,
ne
two
rks
interests
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Web interface Linked data Visualization
URIs
tag
s
social semantics
contacts data
data.semanticweb.org
rkbexplorer.com
publications, co-authorship networks
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$"
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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
tag
s,
ne
two
rks
interests
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Delicious
Flickr
LastFM
semanticweb.org
rkbexplorer.com
dbpedia.org
dbtune.org
TAGora Sense Repository
JXT Triple Store
Extractor Daemon
Connect API
Web-b
ased S
yste
ms
Real W
orld
Visualization Web Interface Linked Data
Local Server
RFID Readers
Real-WorldContact Data
SocialSemantics
Communities of Practice
Social TaggingSocial Networks
Contacts
mbid -> dbpedia uritag -> dbpedia uri
Profile BuilderPublications
Aggre
gato
r
RD
F c
ache
RFID Badges
Web interface Linked data Visualization
URIs
tag
s
social semantics
contacts data
data.semanticweb.org
rkbexplorer.com
publications, co-authorship networks
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#"
$"
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&"
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29
30
31
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
101 102101
102
103 104 105103
104
ESW
C20
10101 102 103 104 105
ESWC2009101102103104
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 10seniority (number of papers)
0
1
2
3
4
5
Ave
rage
seni
ority
of n
eigh
bors
sennsenn,wsenn,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
48 http://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
data.semanticweb.org
Gianluca Correndo
Ivan Cantador
Andrés Garcia
Organisers of HT 2009, ESWC 2009, ESWC 2010
All LSS participants!