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1
Harith Alani
Knowledge Media institute,
The Open University, UK
Workshop on Social Data on the Web (SDoW)
ISWC, Shanghai...
Sensor & Social Networks
2
www.nabaztag.com
www.withings.com
The Canine Twitterer
“Having my daily workout.
Already did 15...
Tag-Along Marketing
The New York Times,
November 6, 2010
“Everything is in place for location-based social
networking to b...
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
- ...
7
SocioPatterns platform
7
Sociopatter deployments
8
Science Gallery,
Dublin
2 months, ~30K
people
25C3 conference
“nothing to hide”
Berlin
3 days, ~...
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
...
Offline + online social networking
11ESWC2010
Where
should I go?
Where have I
met this guy?
Anyone I
know here?
Who should...
12
Social Web Communities
Sept. 2008
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tagora.ecs.soton.ac.uk/schemas/
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xmlns:rdf="http://www.w3.org/
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Components of LSS
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
intere...
SW sources
15
proceedingschair
chair
author
CoP
conference
Social networking systems
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,network...
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 anal...
20
Tag Filtering Service
21
Tag Disambiguation
•  Term vector similarity
•  Term vector from tag co-occurrence
•  Term vector for each suggested Db...
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...
24
From raw tags and social relations
to Structured Data
User raw
data
Structured
data
Collective
intelligence
ontologies
...
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
interests
Delicious
Flic...
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	
  
XMLSch...
triple store
Profile
builder
Tag disambiguation
service
Tag to URI service
ontology
tags,networks
interests
Delicious
Flic...
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
•  Networki...
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
•  ...
Characteristics of F2F contact events
Contact
characteristics
ESWC 2009 HT 2009 ESWC 2010
Number of
contact events
16258 9...
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...
Average seniority of neighbours in F2F networks
0 5 10
seniority (number of papers)
0
1
2
3
4
5
Averageseniorityofneighbor...
Presence	
  of	
  A<endees	
  HT2009	
  
Importance	
  of	
  the	
  bar?	
  	
  
Popularity	
  of	
  sessions?	
  	
  par0...
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 s...
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Scientific seniority v...
Conference Chairs
all
participants
2009
chairs
2009
all
participants
2010
chairs
2010
average degree
average strength
55
8...
Networking with online and offline ‘friends’
Characteristics all users coauthors Facebook
friends
Twitter
followers
averag...
Twitterers vs Non-Twitterers
•  Time spent in conference rooms
–  Twitter users spent on average 11.4% more time in the
co...
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 pu...
49
•  How many do you
recognise? Use?
•  Which ones still exist?
•  Which are well and healthy,
which are weakening and
co...
•  Problem of managing the health of online communities using real-time
analysis of huge community data sets
–  Current so...
Thanks to
References:
•  Barrat, A., et al. (2010) Social dynamics in conferences: analyses of data from the Live Social S...
SDoW2010 keynote
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  1. 1. 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
  2. 2. Sensor & Social Networks 2 www.nabaztag.com www.withings.com The Canine Twitterer “Having my daily workout. Already did 15 leg lifts!”
  3. 3. 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. 4. 4
  5. 5. Localised social networking with Facebook 5 200M FB mobile users. Visit FB twice as much
  6. 6. 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. 7. 7 SocioPatterns platform 7
  8. 8. 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!
  9. 9. Offline social networks 9 by Ciro Cattuto From a small conference at ISI, Turin
  10. 10. 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
  11. 11. 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. 12. 12 Social Web Communities Sept. 2008
  13. 13. <?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
  14. 14. 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 Communities of Practice Social Tagging Social Networks Contacts m Profile BuilderPublications Aggregator RDFcache RFID Badges Web interfaceLinked data Visualization URIs tags social semantics contacts data data.semanticweb.org rkbexplorer.com publications, co-authorship networks !" #" $" %" &" '" (" 14
  15. 15. SW sources 15 proceedingschair chair author CoP conference
  16. 16. 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 Visualization Web Interface Linked Data Local Server D 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 D 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 Communities of Practice Social Tagging Social Networks Contacts m Profile BuilderPublications Aggregator RDFcache RFID Badges Web interfaceLinked data Visualization URIs tags social semantics contacts data data.semanticweb.org rkbexplorer.com publications, co-authorship networks !" #" $" %" &" '" (" 16
  17. 17. 17 Social and information networks 17
  18. 18. 18 Merging social networks 18 FOAF
  19. 19. 19 Tag Filtering Service Semantic modeling Semantic analysis Collective intelligence Statistical analysis Syntactical analysis
  20. 20. 20 Tag Filtering Service
  21. 21. 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/
  22. 22. 22 From Tags to Semantics 22
  23. 23. 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. 24. 24 From raw tags and social relations to Structured Data User raw data Structured data Collective intelligence ontologies Semantic data
  25. 25. 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 Communities of Practice Social Tagging Social Networks Contacts m Profile BuilderPublications Aggregator RDFcache RFID Badges Web interfaceLinked data Visualization URIs tags social semantics contacts data data.semanticweb.org rkbexplorer.com publications, co-authorship networks !" #" $" %" &" '" (" 25
  26. 26. 26 RFIDs for tracking social contact 26
  27. 27. 27 Convergence with online social networks 27
  28. 28. People contact  RFID  RDF Triples 28 F2FContact hasContact   contactWith   contactDate   contactDura0on   XMLSchema#date   XMLSchema#0me   contactPlace   Place foaf#Person1 foaf#Person2
  29. 29. 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 Communities of Practice Social Tagging Social Networks Contacts m Profile BuilderPublications Aggregator RDFcache RFID Badges Web interfaceLinked data Visualization URIs tags social semantics contacts data data.semanticweb.org rkbexplorer.com publications, co-authorship networks !" #" $" %" &" '" (" 29
  30. 30. 30
  31. 31. 31
  32. 32. 32 Real-time F2F networks with SNS links http://www.vimeo.com/6590604
  33. 33. 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
  34. 34. Analysis of LSS Results The New Yorker 2/11/2008 34
  35. 35. 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%
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. Presence  of  A<endees  HT2009   Importance  of  the  bar?     Popularity  of  sessions?    par0cular  talks?  
  40. 40. Number  of  cliques  HT2009  
  41. 41. 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
  42. 42. !" !#$" !#%" !#&" !#'" (" (#$" (" &" ((" (&" $(" $&" )(" )&" %(" *+,-./"01221+./3" 45678.9" *+..:3" 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
  43. 43. 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
  44. 44. 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
  45. 45. 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. 46. 46 What about the individuals?
  47. 47. Behaviour of individuals – micro level analysis 47 !" !#$" !#%" !#&" !#'" (" (#$" (" )" *" (+" (," $(" $)" $*" ++" +," %(" %)" -./0123" 4$4"526722" 4$4"8972069:" :2;<9:=">?@20AB?"C" >D?@;<"E7DB<2>#"F72G" ?:;@7>HIJ>" @0"K88"92;L" 6DD1">?@20AB?M" ;01">D?@;<">@60;<>"" >:=" >?@20A>9N" DO9>@127M" :@6:" E7DB<2" 89O1209>M"PQM"12R2<DE27>#" S:DT>"9:2"0239">9;7"72>2;7?:27N"
  48. 48. •  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. 49. 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?
  50. 50. •  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
  51. 51. 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!
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