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

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4

Localised social networking with Facebook

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200M FB mobile users. Visit FB twice as much

Tracking of F2F contact networks

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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

Facebook

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

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



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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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

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



Aggre

gato

r

RD

F c

ache

RFID Badges

Delicious

Flickr

LastFM

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



Aggre

gato

r

RD

F c

ache

RFID Badges


URIs

tag

s

social semantics

contacts data


rkbexplorer.com


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16

17

Social and information networks

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18

Merging social networks

18 FOAF

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Tag Filtering Service

Semantic modeling Semantic analysis Collective intelligence Statistical analysis Syntactical analysis

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Tag Filtering Service

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Tag Disambiguation •  Term vector similarity

•  Term vector from tag co-occurrence

•  Term vector for each suggested Dbpedia disambiguation page

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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/

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From Tags to Semantics

22

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Tags to User Interests

•  Based on 72 POIs verified by users

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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

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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

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



Aggre

gato

r

RD

F c

ache

RFID Badges

Delicious

Flickr

LastFM

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



Aggre

gato

r

RD

F c

ache

RFID Badges


URIs

tag

s

social semantics

contacts data


rkbexplorer.com


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RFIDs for tracking social contact

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27

Convergence with online social networks

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People contact RFID RDF Triples

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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

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



Aggre

gato

r

RD

F c

ache

RFID Badges

Delicious

Flickr

LastFM

Facebook

semanticweb.org

rkbexplorer.com

dbpedia.org

dbtune.org


JXT Triple Store

Extractor Daemon

Connect API

Web-b

ased S

yste

ms

Real W

orld


Local Server

RFID Readers


SocialSemantics



Contacts



Aggre

gato

r

RD

F c

ache

RFID Badges


URIs

tag

s

social semantics

contacts data


rkbexplorer.com


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29

30

31

32

Real-time F2F networks with SNS links

http://www.vimeo.com/6590604

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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

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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

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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

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•  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

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•  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

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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

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•  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!

Documents

SDoW2010 keynote