Social Networking and On-Line Communities: Classification and Research Trends Maria Ioannidou, Eugenia Raptotasiou, Ioannis Anagnostopoulos.

Social Networking and On-Line Communities: Classification and Research Trends

Maria Ioannidou, Eugenia Raptotasiou, Ioannis Anagnostopoulos

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Evolution of the on-line social networks research field in WWW conf. series until 2005 the papers are

few (1 to 3 per year) increase of scientific

interest from 2006, when online social networks are recognized as an autonomous track

numbers keep rising: 28 in WWW2010

In WWW2011 out of 90 regular papers 12 contained the term “social” in their title

18 out of 89 posters were classified in “Social Systems and Graph Analysis”

Frequency of papers on social networks in WWW conferences in the period 2000-2010.

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Web Science Subject Categorization*

E. Web Society

E.2 Social Engagement and Social Science

E.2.1 Social networks

E.2.7 Virtual communities, groups and identity

E.6 Politics and Governance

E.6.2 Policy and Regulation

E.6.2.2 Privacy

E.6.2.3 Trust

E.6.2.4 Security

* http://webscience.org/2010/wssc.html

“The Web Science Subject Categorization (WSSC) system aims to facilitate communication and collaboration among scholars of the Web from various perspectives i.e. computational, mathematical, social, economic and legal”

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Our categorisation

Social Engagement and Social Science◦ Social networks

Web as a Complex System Systems, Social structures and processes Technologies used

◦ Virtual communities, groups and identity (Personalisation / Adaptation) Social interaction and behaviour Information propagation Social interest discovery, personalisation Community structure, evolution

Politics and Governance Policy and Regulation (Security, Privacy, Trust)

User anonymization Groups and mixed user profiles Collective privacy management User Reputation

How we worked… an example

In degree

Out degree

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Social Networks

See the Web as a Complex System:◦ Graphs

Systems, Social structures and processes◦ Network applications

Technologies used◦ Services, Games

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21

8797

1

110

4

2

5

7788104

115

126

89

6

78

7

7990

98105

111

8

80

91

99

106

112

9

81

12

10

13

82

92

100

107

113

116

118119

120

121

122

123

124

125

1483

15

84

93

101

16

85

94

102108

114

117

19

28

96

2086

95

103

109

22

23

24

2526

2729

30

31

32

3334

35

36

37

3839

4011

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

5758 59

60

61

62

63

64

65

6617

6768

69

70

71

72

73

74

75

76

18

Social Networks

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Virtual communities, groups and identity

Social interaction and behaviour- relation between a person’s social interactions and

personal behavior- Positive / Negative links

Information propagation- how quickly does information propagate- how widely does information propagate in the social

network- what is the role of word-of-mouth exchanges between

friends in the overall propagation of information in the network.

Social interest discovery, personalisationCommunity structure, evolution

- discovering and processing some of the community characteristics in order to predict their future evolution

Virtual communities, groups and identity

Profile / Group based

Behaviour basedCollaboration based

ImplicitExplicit

Hybrid

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Security, Privacy, Trust

User anonymizationhas to do with identifying security and privacy in on-line social networks and how this ensures, that the users are protected by malicious targeted attacks.Groups and mixed user profilesattacks that exploit the user groups with mixed profiles, in an effort to predict the users’ sensitive private attributes.Collective privacy managementdata that do not necessarily belong to the users that publish them.Privacy wizardSolutions and models that help users to describe their preferences and define their privacy settings automatically.

User Reputation

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Security, Privacy, Trust

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Relevancy between E.2.1/E.2.7 and E.6.2

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Graph Statistics

222 papers from which 104 and 31 concern categories E.2.1/E.2.7 and E.6.2

10% on the total papers are isolated nodeson average, each node is connected to two other

nodesgenerally, most nodes have low in/out degree there are links with papers from almost all other

tracks!high efficiency in information exchanging

(network’s efficiency value was 0.5)

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Generic Conclusions

Number of research efforts in social networks is still increasing

Existing problems become more intense and new arise as on-line users’ numbers increase

The topic of security and privacy seems to gain ground for now

Almost all other web topics are affected as well as other science fields

Semantic web research / semi-automatic information organisation can benefit from the study of social networks

Verify relations and weights between already established categorisations / semantic networks, or even exploit new ones (by graph clustering)

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Future workexplore in detail network’s structure and evolution

by including data from other sources / network graphs

examine the graph association of a specific topic with other topics (below an example from WWW conf. series)

Connections of E.6.2 with other topics:

black E.6.2grey other

Thank you …!!!

Questions ???

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Specific Conclusions

Social networking services fail to provide users the necessary protection and users tend to compromise their privacy neglecting necessary precautions

Community structure and activities within the communities have become more and more complicated but if studied can provide valuable information about human behavior and interaction

Similarity measurement among user profiles could be exploited in marketing methods and undergoing semantic web research