A typology of sns users

28 Int. J. Web Based Communities, Vol. 7, No. 1, 2011

A typology of social networking sites users

Petter Bae Brandtzæg*
University of Oslo,
and
SINTEF ICT,
Forskningsvn. 1, 0314 Oslo, Norway
Fax: +47-22067350
E-mail: pbb@sintef.no
*Corresponding author

Jan Heim
SINTEF ICT,
Forskningsvn. 1, 0314 Oslo, Norway
Fax: +47-22067350
E-mail: jheim@sintef.no

Abstract: Social networking sites (SNSs) are said to be new important means
of participating, communicating, and gaining social capital. Thus, increasingly
fragmented user population and user behaviours in SNSs make it important to
achieve more knowledge about SNS users and their participation inequality.
This article proposes a user typology for SNSs, which identifies and describes
the variety of ways in which people use SNSs. An analysis of the survey data
from 5,233 respondents in four major Norwegian SNSs showed five distinct
user types:
1 sporadics
2 lurkers
3 socialisers
4 debaters
5 actives.
Both quantitative and qualitative analysis validates the typology. The SNS user
typology contributes to a more nuanced and precise measure of how future
research should identify and predict SNS use and better understand
participation inequality in SNSs. The identification of various user types
indicates a 50-30-20 rule for participation in small and locally bounded online
communities compared to the existing 90-9-1 rule. Finally, the results could
help the design of SNSs in tailoring them to user type.

Keywords: social networking sites; SNSs; online communities; participation;
users; user typology; personality types; Norway.

Reference to this paper should be made as follows: Brandtzæg, P.B. and
Heim, J. (2011) ‘A typology of social networking sites users’, Int. J. Web
Based Communities, Vol. 7, No. 1, pp.28–51.

Biographical notes: Petter Bae Brandtzæg is a Research Scientist at SINTEF
ICT and a PhD candidate at the University of Oslo in the RECORD project. His
research interests include social media, user behaviour, social capital and

Copyright © 2011 Inderscience Enterprises Ltd.

A typology of social networking sites users 29

privacy issues. He received his MS in Psychology from the Norwegian
University of Science and Technology, Trondheim, Norway in 2000. He is a
member of ECREA and the ACM. He has recently been a Guest Editor for
Computers in Human Behavior.

Jan Heim is a Chief Scientist at SINTEF. He has previously been an Associate
Professor and Head of the Department of Psychology at University of
Trondheim. He is at present working on human-computer interaction with a
focus on user requirements and psychological aspects of mediated
communication and has done so in various European research projects
(Telecommunity, TASC, USER, INUSE, RESPECT, Vis-à-vis, Eye-2-Eye,
CITIZEN MEDIA). He is the author or co-author of several international
papers.

1 Introduction

The popularity of social networking sites (SNSs) such as Facebook, Orkut, LinkedIn, and
MySpace has boosted social interactions and user engagement online among several
hundred million users worldwide. In Norway, where this study takes place, 53% of the
Norwegian online population between 15 and 74 years are users of SNSs (Brandtzæg and
Lüders, 2008).
According to Bishop (2007), SNSs have changed the nature of online user
participation into more democratising forums where people can communicate and add
their user-generated content (UGC). SNSs are said to represent an important mechanism
for knowledge exchange and sharing (Shen and Khalifa, 2009). The change from passive
consumption to more active use indicates more varied use and forms of participation
(Preece and Shneiderman, 2009) and a new digital divide between actives and
non-actives (Brandtzæg, 2010). Participation inequality in SNSs might, therefore, affect
people’s ability to gain social capital (Ellison et al., 2007; Brandtzæg et al., 2010),
discuss, and become involved in new forms of information and civic engagement
(Brandtzæg and Lüders, 2008).
Despite the explosive interest in and use of SNSs worldwide, little is known about the
various ways in which users engage in SNSs. According to a review by Boyd and Ellison
(2007), scholars have a limited understanding of who is using these sites, why, and for
what purposes, especially outside the USA. Therefore, this article explores the patterns of
adoption and use of SNSs by developing a typology of the users of these sites. The
development of a classification scheme of SNS users’ patterns identified through an
empirically-based typology could reveal the mix of underlying activities and participation
inequality of such sites (e.g., Bunn, 1993). Such a typology could also help researchers,
designers, and managers understand what motivates technology-mediated social
participation. This will enable them to improve the interface design and social support
for such sites (e.g., Brandtzæg et al., 2010; Preece and Shneiderman, 2009; Shen and
Khalifa, 2009).
More specifically, the main objective of this article is to outline an empirically-based
SNS typology that can be used to describe and identify distinct user types characterised
based on the ways in which SNS users behave in terms of participation level and
participation objective. There were two sub-goals to achieve this:

30 P.B. Brandtzæg and J. Heim

• to empirically establish a user typology of SNS users based on questionnaire data on
user behaviour from 5,233 SNS users in Norway
• to empirically validate the SNS user typology.
Validation of the cluster solution of the different user types is carried out by comparing
the different user types’ responses to other survey questions reflecting a diverse user
participation. In addition, different user types are validated by analysing typical
qualitative responses from distinct user types.
It should be noted that the approach in this study is exploratory rather than one of
testing theoretical hypotheses. This is because no firm body of empirically-based
theoretical knowledge exists about users of SNS, especially not about typologies.

1.1 A user typology approach
The goal of a typology is to classify diversified behaviour into meaningful categories
(Barnes et al., 2007). In contrast, media behaviour is usually understood in terms of
frequency of use only (Heim et al., 2007) and lacks a framework to identify different
media usage more precisely. The challenge of the research described in this paper is,
therefore, to go beyond the traditional measures of time to include how people use SNSs,
to better understand participation inequality in terms of a typology.
In this paper, we conceptualise usage in terms of users’ level of participation
(intensity of use) and participation mode (objective and direction of participation) in the
SNS, as shown in Figure 1. Both participation level and participation mode are treated as
two equally important dimensions; at the same time, these dimensions cover a broad
range of SNS activities such as time-killing, debating, and UGC production. This
approach is similar to Shih and Venkatesh’s (2004) conceptualisation of user types in
terms of variety and rate, where variety referred to the different ways in which the
product could be used and usage rate referred to how often the product was used,
regardless of its application.

Figure 1 A conceptualising of user types in terms of different modes of and level of participation
in SNSs
High participation

Informational mode Recreational mode

Low


In this paper, user types in SNSs are conceptualised by
1 their participation objective
2 level of participation.
Users may be either high or low in terms of their participation, but some users may have
both a recreational mode and an informational mode of communication.

2 Previous research on user types in SNSs

2.1 User typologies
To generate the research questions (RQs) and validate our approach for this article, this
section provides a brief introduction to the study of user types that might relate to SNS
usage. First, it should be noted that the categorisation of people based on a typology is
not a new theoretical approach. In attempting to understand human nature, psychologists
have been developing personality typologies for many years.
The most influential theory in research on technology use and adoption that applies to
a typology is Rogers’ diffusion of innovations model (Rogers, 1962, 2003). This model
explains the process by which innovations are adopted and offers the following
categorisation of users, based on their rates of adoption of innovations over time:
1 innovators (around 2.5%)
2 early adopters (13.5%)
3 early majority (34%)
4 late majority (34%)
5 laggards (16%).
The theory’s weakness lies in the fact that it describes and classifies the innovation and
adoption rate in general, rather than the differences in actual media behaviour. Still, the
theory might prove useful in determining methods to describe, predict, and identify
different user types in an SNS, since SNSs are a kind of innovation.
Some media studies have applied a typology approach to understand differences in
media behaviour in general. Johnsson-Smaragdi (2001) identified four main user types
among children in nine European countries:
1 low media users
2 traditional media users
3 specialists
4 screen entertainment fans.
A similar approach was suggested by Heim et al. (2007) in their study of children’s
media usage in Norway, and by Ortega Egea et al. (2007) in their attempt to understand
the diffusion and usage patterns of internet services in the European Union among
citizens in general.


When it comes to online communities or social media sites such as SNSs, only a few
attempts have been made. One informal, but influential, study by Nielsen (2006)
suggested a 90-9-1 rule, which refers to the unbalanced nature of participation in social
media with UGC. An example of this rule is Wikipedia, where more than 99% of users
do not contribute and only consume. Wikipedia has 68,000 active contributors, who make
up 0.2% of the 32 million unique visitors to the site in the USA alone. A rough
explanation of the 90-9-1 rule is as follows:
1 Lurkers (90%) read or observe, but do not contribute. Kollock and Smith (1996)
described lurkers as free-riders, that is, non-contributing, resource-taking members.
This is also referred to as the free-rider problem.
2 Intermittent contributors (9%) contribute from time to time, but other priorities
dominate these contributors’ time.
3 Heavy contributors (1%) are active users who account for most of the contributions
and system activity.
Verifying Nielsen’s (2006) categories is only somewhat possible because no detailed
descriptions of these results or of the method exists, other than the defective information
published on Nielsen’s Alterbox, a blog site. However, the 90-9-1 rule has been
influential in the discussion on social media usage since the idea was launched, and it
should be interesting to see if we find the same pattern in the study presented in this
paper.
An online community user typology was suggested by Kozinets (1999). In a
theoretical paper, he described various types of community members according to two
factors: the degree of consumption activity and the intensity of relationships with other
members of the virtual community. These two factors enable four distinct types of
community members to be identified:
1 Tourists are users who simply drop by the community every now and then with only
superficial interest and few social ties
2 Minglers are users who maintain strong social ties while being marginally interested
in consumption activity
3 Devotees are users who maintain a strong interest in consumption but have few
social attachments
4 Insiders are users who have strong social ties and a strong interest in consumption
activity.
This typology is based not on empirical work but on theoretical assumptions. Kozinets
also stressed the importance of categorisation in making the fragmentation of online
community users more meaningful.
Recently, a report from OFCOM (2008) suggested a typology specific to SNS users,
based on in-depth interviews with 39 users. Five types were identified:
1 alpha socialisers are regular users who visit SNSs often, but only for short bursts to
flirt and meet new people
2 attention seekers are users who crave attention and comments from others, often by
posting photographs of themselves and friends


3 followers are users who join sites to keep up with what their peers are doing
4 faithfuls are users who rekindle old friendships, often from school or university
5 functionals are users who log on for a particular purpose such as looking for music
and bands.
The above user typologies related to SNSs are not based on quantitative information, but
rather on sparse qualitative and theoretical judgments. This means that we know little
about both user types and the distribution of user types in SNSs. Further, to the best of
our knowledge, OFCOM’s typology is the only one that addresses SNSs specifically.
Generally, the sparseness of empirical material related to SNSs and typologies of their
users (i.e., a single research report) suggests that further research is needed.
However, we should note that, although much research into online communities
(e.g., Preece, 2000) exists, SNSs are a new type of community that may play a role
different from that described in the early literature on virtual communities (Ellison et al.,
2007). This literature focused on how community participants who were geographically
dispersed and motivated by ‘meeting’ new people online (e.g., Wellman and Gulia,
1999). New SNSs, on the other hand, are an ‘all in one place solution’ that merges
several interactive services and application related to work, leisure, and information
(Brandtzæg and Heim, 2008). For example, more than 350,000 applications are in use on
the Facebook platform according to Facebook’s September 2009 statistics. It should,
therefore, be highlighted that an empirically-based user typology that divides the various
user behaviours into meaningful categories in SNSs is absent.

2.2 User types and social implications
A user typology could also benefit research into the social implications of various SNS
usage, and hence, it is important to understand various forms of participation. Social
capital can be used to investigate the social implications of SNSs. The concept has two
complementary aspects:
1 social contact, which covers patterns of interpersonal communication, including the
frequency of social contact and social events
2 civic engagement, which covers the degree to which people become involved in their
community, both actively and passively, including political and general
organisational activities (Quan-Haase and Wellman, 2004).
Most studies on social effects (e.g., Kraut et al., 1998) and social capital (Ellison et al.,
2007) have not focused on particular forms of use, but rather on the time spent online and
the frequency of online visits. One study that we are aware of, by Shah et al. (2001),
addressed four types of internet usage patterns and different facets of social capital and
found that ‘social recreation’ usage (e.g., participation in chat rooms and game playing)
was consistently negatively related to civic activities, trust in other people, and life
contentment. In contrast, an ‘information exchange’ pattern had a positive impact on the
same social variables. These results suggest a connection between different user types
and various social outcomes.


3 Research questions

An exploratory approach is a natural point of departure in a relatively immature field of
research.
Using the above review as a basis, we explore the following main RQ:
RQ1 Given that users in SNSs display different patterns of use in terms of the users’
participation and communication modes, how can users be classified into
meaningful categories or user types?
We asked the following sub question:
RQ2 What are the associations between different types of SNS users and
demographic factors such as age, gender, and education?
To further validate the typology, we introduced RQ3 and RQ4, below. The measures used
to identify UGC participation, social contact, and political activity included distinct
measures that were used to identify the typology, to avoid circular questions.
RQ3 How are diverse user types reflected in different levels of UGC participation?
RQ4 What are the relationships between distinct user types and social capital, in
terms of number of friends in the SNS user’s profile, frequency of contact with
friends, people, and political activity?

4 Method

4.1 Sample
The data were collected using an online questionnaire over a three-week period in March
2007 on four different SNSs. These SNSs were chosen because, at the time of the
investigation, they were the most popular SNSs in Norway, which, consequently, might
give us a good picture of what typical SNS users seek regarding relationships. The
frequent usage and popularity of these sites were documented in a recent report for the
Ministry of Government Administration and Reform in Norway (Brandtzæg and Lüders,
2008), which provides a detailed overview of the most popular SNSs in Norway.
A total of 5,233 persons responded to the survey, of which 3,078 were women and
2,155 were men, with a median age of 16 years. Demographic details given by the
respondents on each SNS are as follows:
• Biip.no (n = 2,278) consists of adolescents with a median age of 15 years mostly in
their final year of comprehensive school. The number of females (62%) greatly
exceeds the number of males (38%). The users have no specific type of residence
and live all over the country.
• HamarUngdom.no (n = 1,598) consists of adolescents with a median age of 16 years
who are mostly in high school. There are somewhat more females (53%) than males
(47%). The majority of users live in rural areas.
• Nettby.no (n = 512) consists mostly of young adults with a median age of 20 years.
There are more females (58%) than males (42%). The sample members have no


special type of residence and live all over the country. About one third of the
members are high school students or college/university students.
• Underskog.no (n = 335) consists of adults with a median age of 29 years. The
members have a high level of education compared to the rest of the sample. There
are somewhat more males (55%) than females (45%). Most live in the Oslo area (the
largest city in Norway).
The four sites fit well into the definition of an SNS (e.g., Boyd and Ellison, 2007). In
addition, they reflect some interesting differences (e.g., size and/or focus) that make it
possible to investigate more closely how different user types are distributed in SNSs with
different numbers of members and diversities in focus. Table 1 describes these SNSs in
more detail.
Table 1 Description of the four SNSs used in this study

SNS Origin Members Description
Biip.no 2005 June 280,000, One of the most popular SNSs for teenagers in
March 2007 Norway. Mostly for socialising and sharing
pictures, music, and videos.
HamarUngdom.no 2002 190,000, Initially a local online community targeting
August March 2007 youths in a small town in Norway called Hamar.
The community has become very popular and
grown outside its original borders. Mainly for
socialising and discussions.
Nettby.no 2006 320,000 Norway’s most popular SNS, connected to
September March 2007 Norway’s largest online newspaper. It attracts a
wider sample of the population. Popular for
discussion and interests groups, as well as
socialising.
Underskog.no 2005 10,000, Originally a user-generated cultural calendar for
November March 2007 Oslo. Very academic and cultural oriented, but
also for socialising and discussions. This is an
invitation-only community.
Note: These numbers were collected by e-mail from the different community owners at
the same time as this study was executed, March 2007.

4.2 Procedure
We used an online survey questionnaire that contained open and fixed-response
questions. We urged the site owners to distribute the survey to all of their members inside
the SNS, either by a tag in the members’ user profiles or with a message. This was done
on Biip.no and HamarUngdom.no, whereas on Nettby.no and Underskog.no, the survey
was merely introduced by a banner ad on the front page and by blogging contributions
that urged users to participate. This type of survey allowed us to easily access a very
large number of users of these SNSs, while the users were actually using the sites. To
motivate as many users as possible (i.e., including less active to very active users) to
participate in the survey and complete the questionnaire, all the participants from the four
SNSs were entered into a raffle to win a travel gift coupon worth US $1,750. A technical
solution made it impossible for any user to participate multiple times in the study.


4.3 Measures
All the measures and questions were identical in all four online surveys.

4.3.1 Socio-demographics
We used the socio-demographic measures of residence, gender, age, and education.

4.3.2 User typology measure (fixed question)
To identify distinct user types in the SNS (RQ1), we asked the question, “What are your
reasons for visiting this SNS today?” This is a very concrete question about the users’
actual activities when they are on the site and is not subject to interpretation on the part of
the respondents. This question was followed by 18 yes/no alternatives that reflect
different modes of communication (informational vs. recreational) and levels of
participation (high vs. low). These alternatives are shown in Table 2. Some examples for
the informational mode are ‘look for new information’, ‘discuss’, and ‘make
appointments for meetings with other people’, while examples for the recreational mode
include ‘profile surfing’, ‘kill some time’, and ‘look for a new friend’. These alternatives
aimed to cover most of the possible and common user activities or behaviours within the
SNS. The fact that very few participants used the alternative ‘other’ also indicates that we
considered most of the possible user activities in the present questionnaire. The 18
activities were defined based on a discussion with the developers of Nettby.no and a pilot
investigation conducted with ten target users between the ages of 14 and 38 years who
had experience with SNS usage.

4.3.3 User typology measure (open-ended questions)
After the fixed question (RQ1), we included the following three open-ended questions:
1 What is your most important reason for using this SNS?
2 Why have you stopped using, or are less active in, other SNSs? (This question was
answered by only 48% of the sample who reported being less active in an SNS.)
3 Under what conditions would you consider contributing more video footage to the
SNS?
These three questions were designed to encourage full, meaningful answers using the
subject’s own SNS experiences, so that we could achieve a more in-depth understanding
of the user typology yielded. In addition, the questions were selected with the aim of
conducting a qualitative analysis that could verify the validity of the different user types
we identified in the statistical cluster.

4.3.4 UGC participation
To answer RQ3 concerning how user types relate to levels of UGC production, we
measured UGC contribution in terms of how often users reported text production, image
contribution, and video contribution on a six-point scale (i.e., ranging from ‘never’ to
‘several times a week/daily’).


4.3.5 Social capital – social contact and civic engagement
The two aspects of social capital (RQ4), frequency of contact and level of civic
engagement, are commonly used measures in surveys to reveal people’s level of
involvement in a community (Quan-Haase and Wellman, 2004). Social contact was
measured in terms of frequency of contact with different forms of relationships:
a friends
b family
c other people, using the same six-point scale as above (i.e., ranging from ‘never’ to
‘several times a week/daily’).
In addition, the number of friends in the user’s own profile was reported. Civic
engagement was measured in terms of the user’s attitude toward his or her political
engagement with the question, “How important is it for you to express yourself politically
inside your SNS?” using a six-point Likert scale (i.e., ranging from ‘not important’ to
‘very important’).

4.4 Analysis
A k-means cluster analysis (SPSS, v.14) was employed to identify typical user types in
the SNS. Cluster analysis is an empirical technique designed to partition a heterogeneous
sample into homogeneous subgroups to discover classifications within complex datasets
(Blashfield and Aldenderfer, 1978). The method implies the classification of similar
objects into different groups or, more precisely, the partitioning of a dataset into subsets
(clusters) so that the data in each subset (ideally) share some common trait – often
proximity, according to some defined distance measure. It is a method of categorising
objects into naturally occurring groups and is used in medicine (disease groups), biology
(animal and plant groups), and marketing (groups of people with similar buying habits)
(Gore, 2000). The method could, therefore, be employed to determine similarities of
usage patterns into occurring user types in SNSs as well.
K-means cluster analysis (see Tan et al., 2006) is recommended when the number of
entities (persons) in the analysis is high (more than 1,000). All variables in the analysis
were dichotomous: zero or 1. We used 18 variables and 5,233 respondents, and there
were no missing values.
However, it is always a challenge to define how many cluster solutions should be
chosen. Several different cluster solutions were evaluated for their effectiveness in
producing cohesive groups that were distinct from one another, large enough in size to be
analytically useful, and substantively meaningful (Horrigan, 2007). To validate the final
cluster solution, we repeated the same analysis ten times with a random sample of 40% of
all cases in the material. In six out of the ten analyses, all the clusters were reproduced. In
one of the ten analyses, four of the five clusters were reproduced. In three out of the ten
analyses, three of the five clusters were reproduced. This indicates that the final solution
was robust.
A five-cluster solution seems to be an adequate size when we compare this solution
with the cluster outcome in similar studies identifying user types (e.g., Ortega Egea et al.,
2007). According to a review by Brandtzæg et al. (2009), cluster solutions vary between
four and six.


To further validate the cluster solution and to illustrate the different user types more
in depth, one or two typical representatives for each pattern were extracted from the
dataset and presented. These typical respondents were chosen from those with the least
deviation from their cluster mean. A further requirement was that they gave a reasonable
number of comments in the open-ended questions for statistical data analysis.

5 Results

5.1 The user typology
The analysis suggested five distinct clusters, interpreted as user types that reflect different
participation levels and communication modes or deeper motivations for using an SNS.
Table 2 shows the proportion of cluster members indicating their reason “for visiting the
SNS today” in terms of user activities reflecting the users’ typical behaviour inside the
community. The clusters were given user-type labels, the formulation of which was based
on an interpretation of the values in Table 2, indicating the users’ level of activity in
different types of behaviour.
Table 2 Five user types based on results from k-means cluster analysis of the 18 variables
(N = 5,233)

SNS usage Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
1 Write a contribution 0.04 0.03 0.08 0.78 0.53
2 Find an announcement for 0.04 0.01 0.03 0.00 0.17
an event
3 Publish or share pictures 0.01 0.11 0.52 0.22 0.81
4 Publish or share 0.01 0.00 0.02 0.03 0.09
audio/music
5 Publish or share 0.00 0.00 0.02 0.04 0.02
film/video
6 See if somebody has tried 0.28 0.94 0.94 0.90 0.98
to contact me
7 Look for a new friend 0.10 0.13 0.67 0.22 0.77
8 Read new contributions 0.14 0.41 0.37 0.94 0.86
9 See other people’s 0.06 0.42 0.55 0.38 0.92
videos/pictures
10 Make appointments 0.03 0.06 0.17 0.12 0.43
11 Look for new information 0.05 0.21 0.11 0.30 0.52
12 Write letters or messages 0.09 0.34 0.83 0.54 0.92
13 Discuss 0.03 0.04 0.10 0.70 0.64
14 Run community groups 0.00 0.00 0.02 0.09 0.13
15 Profile surfing 0.06 0.44 0.39 0.39 0.90
16 Contact others 0.17 0.54 0.92 0.65 0.96
17 Kill some time 0.13 0.89 0.28 0.67 0.71
18 Other reasons 0.11 0.11 0.13 0.10 0.30
Final cluster labels: Sporadics Lurkers Socializers Debaters Actives
Note: To make this table more readable; values below 0.10 are in italic, while values
above 0.70 are in bold.


As seen in Table 2, actives seem to be even more social than socialisers. Similarly,
actives seem to be at least as involved in ‘debating’ as debaters, but on closer inspection,
debaters seem to mainly debate and almost completely ignore other activities. Socialisers
mostly socialise and engage in other activities to a lesser extent. Actives are so-labelled
because they are active in almost every type of participation inside the SNS – they
socialise, debate, and engage in several other activities, even killing time (as do lurkers).
A detailed interpretation of each user type is described below, while Figure 2 shows how
the clusters follow the specific criteria related to the level of participation (low/high) and
modes of participation.

Figure 2 How the different user types link to participation level and modes of participation
High participation

Actives
Debaters

Socialisers

Informational mode Recreational mode

Lurkers
Sporadics

Low participation

Note: Since actives are active in different participation modes, actives are, in practice,
placed in several different locations on this axis but are ‘high’ in participation in all
activities.

Figure 3 Distribution of different SNS user types in terms of % (see online version for colours)

30

25
In percentage

20

15

10

5

0
Socializers Debaters Lurkers Sporadics Actives


Figure 3 gives the distribution in percentage of the five distinct user types. Lurkers
represent the largest user group (27%), while debaters are the smallest user group
identified (11%).
Table 3 shows an overview of the different user types with regard to demographics
such as gender, age, and the level and proportion of users who have received higher
education (college or university) (see RQ2). All reported differences between the clusters
are significant in regard to Pearson’s chi-square (χ2) test; this is due in part to the large
number of participants in the analysis.
Table 3 SNS user types and socio-demographic differences

User types Median age Male in % Female in % Higher education in % (> 18 years)
Socialisers 15 31 69 33
Debaters 18 51 49 70
Lurkers 16 42 58 60
Sporadics 16 47 53 70
Actives 16 41 59 39

Note: To avoid results being confounded with age, only people older than 18 were
included in the education analysis. Higher education refers to a college or
university education.
In terms of age, debaters are older (median age: 18 years), while socialisers are the
youngest user type (median age: 15 years). In terms of gender, girls are significantly
more common among lurkers, socialisers, and actives. As one might expect, debaters are
the most educated user type. However, sporadics are also educated. This could be related
to the fact these users are characterised by a kind of a goal-directed instrumental use,
using the community as an advanced form of e-mail.
Further, the user types are distributed unequally in the different SNSs, as shown in
Table 4. Underskog.no attracts debaters (42%), and Biip.no attracts socialisers (32%);
actives (24%) are most common on Nettby.no and quite rare on Underskog.no (6%).
lurkers are the most equally distributed user type in the four SNS communities.
Table 4 Distribution of user types in different SNSs in %

User types HamarUngdom.no Nettby.no Biip.no Underskog.no
Debaters 11 26 36 42
Lurkers 31 21 26 27
Sporadics 14 14 22 22
Actives 22 24 16 6
TOTAL 100% 100% 100% 100%

The following section is a detailed description of the different user characteristics related
to each user type to answer RQ1. This section also includes results from the analyses of
the answers to the open questions from users representing each user type.


5.1.1 Type 1: sporadics (19%)
The ‘sporadics’ are so named because they visit the community only from time to time,
but not a frequent basis. These users have a low level of participation and tend more
toward an informational mode since they, for the most part, check their status and see if
somebody has contacted them (see Table 2). Sporadic users give few reasons for visiting
the SNS. They are spread equally over the four SNSs and age groups. There is no large
gender difference (see Table 3). A typical sporadic user is exemplified in the qualitative
material, using the open-ended questions. One typical user is August, a 16 year old. He
has 17 people in his profile but has been in contact with only one other person during the
past week. August joined the community to keep in touch with friends. He is not
interested in contributing UGC. He puts it this way: “It is just not my thing to submit a
bunch of videos, etc., on the net”.

5.1.2 Type 2: lurkers (27%)
Lurkers make up the largest user category. They are named ‘lurkers’ since they are quite
low in participation and participate in activities that are more related to recreation. These
users are somewhat involved in several activities, but only passively or to a small degree.
In addition to “see if somebody has tried to contact me”, lurkers score high on “kill some
time” (0.89) (see Table 2). In addition, lurkers are less likely to be contributors of UGC.
lurkers are spread quite equally over all four communities (from 21% on Nettby.no to
31% on HamarUngdom.no) and consist of more females (58%) than males (42%). A
typical ‘lurker’ identified in the qualitative analysis is June, a 16 year old from a small
town. She has been a member of HamarUngdom.no for about three years but is starting to
lose interest. She thinks that technology is important for entertainment but less so for
keeping in touch with others. She has not socialised with anybody on the SNS this week.
She has about 20 people in her profile. According to June, “It is always funny to watch a
good video before the day starts”. Further, she is not a contributor, but more of a
‘free-rider’, who consumes others’ UGC.

5.1.3 Type 3: socialisers (25%)
These SNS users are the next biggest user type and are labelled ‘socialisers’ since their
behavior is characterised by recreational in terms of ‘small talk’ with others, but the
users’ participation level is high. They score high on ‘write letters and or messages’,
‘contact others’, and ‘look for a new friend’ (see Table 2). This pattern is typical of
teenage girls (median age: 15 years; 69% of the sample), mainly on Biip.no (32%) and
practically absent among the Underskog.no members (3%). A typical ‘socialiser’ in the
qualitative data is Mary, a 14 year old who lives in a medium-sized town. She is an eager
member of Biip.no and has been a member for almost a year. The internet is very
important to her. She uses the community to keep in touch with friends, and she likes to
make connections with new people. In the last week, she has been in touch with five
people. She has about 30 contacts in her profile. Her response to the question, “What is
your most important reason for using the SNS?” is, “It’s interesting. I have something to
do in my spare time (…). I have contact with friends, write in friends’ guest books,
comment on people’s pictures, send SMS, and submit pictures of myself and things”.


5.1.4 Type 4: debaters (11%)
Debaters are as high as socialisers in terms of participation level, characterised by being
highly involved in discussions, reading, and writing contributions in general (see
Table 2). In addition, this participation mode is related to a more informal practice as
suggested in Figure 2. The debating pattern is unequally distributed among the
communities: very frequent on Underskog.no and practically absent on Biip.no. The users
are somewhat older than the medians of other user types, and there is practically no
gender difference. A typical ‘debater’ is odd, a 42 year old who lives in Oslo. He has
been a member of Underskog.no for more than a year. He is university educated and likes
to discuss and express himself in writing. He depends on the internet for carrying out
practical tasks and uses it mainly for instrumental reasons. He considers video
contribution too time-consuming. He also regards his SNS as being more text- and
picture-related than video-focused. He uses the SNS to keep updated on cultural events
and new publications, as well as for social contacts, explaining this as follows: “I get
informed about events, publications, and Net experiences; at the same time I am making
bonds and having discussions with other people”.

5.1.5 Type 5: actives (18%)
‘Actives’ are so labelled because these users are engaged in almost all kinds of
participation activities within the community, which includes being a member to “publish
and share pictures” (see Table 2). The majority in this group are young females, as shown
in Table 3. This user type is distributed unequally among the SNSs, being most common
on Nettby.no and HamarUngdom.no. A typical Active user is Lena, a 16 year old who
lives in the countryside. She has been a member of the HamarUngdom.no community for
more than three years and is still very active. During the week before she answered the
questionnaire, she was in contact with 20 people, almost everybody in her profile. When
she is logged on, she engages in many activities, usually related to events and publishing
music or videos. She uses the SNS mainly for social contact and communicates primarily
with friends in her profile. She contributes videos and summarises her activity in this
way: “I wish I had my own video camera. So far, I am just uploading and sharing videos
that others have made”. Her willingness to be even more involved in UGC participation
is high.

5.2 User types and social capital
Table 5 (as well as Figure 2) answers RQ4 and shows that actives are most active in the
social field as well. Socialisers and debaters also have high activity here; they frequently
have daily contact with their friends, and 10% have more than 150 friends in their profile.
Socialisers and actives are also more frequently in touch with their family on a daily
basis.
Further, debaters and actives more often report that political expression within the
SNS is ‘very important’ to them. These user types view their SNS as a significant arena
for community participation in terms of political activity. On the other hand, lurkers and
sporadics are less interested in this type of activity. This result also indicates that the
highest level of social capital inside an SNS is associated with actives, socialisers, and
debaters.


Table 5 Social capital: social contact and civic activity within user types and between different
user types in %

More than Daily Daily Contact with more SNS is very
User types 150 contacts contact contact than 30 people last important to me for
in user profile family friends week on SNS political expression
Socialisers 10 13 61 6 10
Debaters 10 8 57 5 20
Lurkers 9 5 45 2 6
Sporadics 5 10 41 4 7
Actives 16 15 69 12 18

5.3 User types and UGC contribution
Table 6 presents results of the answers to RQ3 – an overview of the different user types
in regard to the amount of UGC they contribute (several times a week and daily, added
together) and the difference among the user types in terms of their willingness to publish
videos on an SNS in the future.
Table 6 Contribution level of UGC (several times a week or daily) within user types and
differences between user types in %

User types Text Photo Video Willingness to publish videos in the future
Debaters 55 12 5 34
Lurkers 26 7 5 22
Sporadics 27 8 2 20
Actives 51 24 6 40

Debaters are the most active group in terms of the contribution of text. This is not
surprising, given that discussion seems to be their passion inside the community.
However, actives and socialisers also actively contribute text. Lurkers are less active and
are more of an observant type. Actives are most active in their photograph (24%) and
video (6%) contributions; actives are also significantly more willing to publish videos in
the future. It should be noted that reported video contribution is quite uncommon among
all user types. In general, the highest level of participation in relation to UGC is seen
among actives; debaters have the second highest level, and socialisers third. Lurkers and
sporadics participate less. These results validate the typology.

6 Discussion

6.1 Five user types and participation inequality
Five distinct types of users have been identified based on their SNS behaviour patterns in
terms of participation level and participation mode in four SNSs. The user types in this
study reflect substantial differences in the underlying patterns of SNS usage, as well as


preferences and reasons for participating in a community. The actives engage in more or
less all activities within the SNS community, whereas the debaters interact with others for
the purpose of discussion. The socialisers primarily use the sites for communication and
‘small-talk’ activities, while the sporadics are, as are all other users, ‘socially curious’ in
that they sporadically check to see whether anybody has contacted them. The lurkers
might be the only user group that is somewhat asocial, as these users seem to log
on to the sites for ‘time-killing’ and consuming entertainment rather than social
interaction. Together with socialisers, lurkers is the least active in contributing text and
photographs.
In our study, we found that the user population, not surprisingly, is made up of the
younger part of the Norwegian population; this age skew is quite typical for most SNSs
(e.g., Pfeil et al., 2009). Nevertheless, the types display large differences in usage
patterns, reflecting different levels of motivation for participation. As expected, two main
types of motivation for social participation in SNSs stand out. The first type is related to a
recreational mode in terms of informal chatting, whereas the other is a more
informational mode related to serious debate and discussion. Although both genders and
most age groups are represented in both modalities, younger girls are more typically
socialisers, while older boys and men are typically debaters. These modalities have
existed on the internet for many years, supported by informal e-mailing, chat channels
such as IRC and MSN, and blogs. Nowadays, these forms of interaction are being merged
into a single type of system: the SNS. This also underscores the fact that we see a
convergence of different types of applications in these systems such as mail, blog, chat,
gaming, and homepage into one integrated service.

6.2 User types differences in diverse SNSs

The four SNSs in this study vary with respect to these two modalities of participation,
highlighting the fact that SNS characteristics and design are important factors that
influence and support different user types. In our study, all the different user types are
found inside the diverse SNSs, but the socialisers are more frequent in the Biip.no
community, whose members are younger, while Underskog.no is more often represented
by debaters. Underskog.no has more highly educated users and a higher mean age than
the other SNS. Underskog.no is also an SNS where members who share content are very
culturally and academically oriented. In addition, Underskog.no is a very small SNS. The
importance of SNS size relative to the level of participation among users has been
suggested by others (e.g., Butler, 2001; Preece and Maloney-Krichmar, 2003). Smaller
and locally bounded SNSs seem to involve more enthusiastic users who are willing to
share and participate more frequently compared to, for example, YouTube (e.g., Nielsen,
2006), which is discussed in the next section.
The three other SNSs (i.e., HamarUngdom.no, Biip.no, and Nettby.no) have a greater
mix of user types. This might be explained by the fact that these sites are free and open to
everyone. These SNSs have no specific interest target and, therefore, may also have a
more diverse distribution of user types. For example, Nettby.no is associated with the
largest online newspaper in Norway and recruits a wide spectrum of users through this
connection.


6.3 Theoretical implications
This paper draws upon theoretical threads to create a framework for a typology of SNS
users according to their behaviour. To date, a typology of SNS users has not been
explored in a way that draws upon a large source of SNS behavioural data combined with
theoretical insights from Kozinets’s (1999) marketing model and Rogers’s (2003)
adoption and innovation model. These and other studies should be compared to give more
insight into the connections between the different user types presented (see Table 7 for a
comparison).
Table 7 Connections between different models of user types

This study’s Kozinets’s Nielsen’s OFCOM Rogers’s
Justification
typology typology ‘typology’ typology model
Socialisers Minglers Intermittent Faithfuls Early majority Use social media in
contributors particular; can be viewed
as the early majority
since they may be seen as
open to new ideas, active
in the community, and
influencing their
neighbours.
Debaters Devotees Intermittent Functionals Early adopters Bloggers and debaters in
contributors SNSs can also be viewed
as early adapters because
of interests in A/V UGC.
Lurkers Tourists Lurkers (Late Account for the biggest
majority) user type in SNSs and in
regard to UGC in
general. Include people
using media for lurking
or time-killing.
Sporadics Alpha Late majority Newcomers and sporadic
socialisers (and laggards) users of the particular
and media studied.
followers
Actives Insiders Heavy Attention Innovators Use media frequently and
contributors seekers and early are advanced compared
adopters to the rest of the user
population.

The previous user typologies have been shown to connect in some ways to our
meaningful categories of user types. This might suggest that previous research on
typologies can help us understand and categorise the increasingly complex behaviours
found on SNSs (see Table 7). Kozinets’s (1999) market segmentation model seemed to
be the most similar typology when compared to the different user types in our SNS
typology. However, Kozinets’s typology is related to the marketing and consumption of
products or the provision of information about goods inside virtual communities, rather
than being a general user typology for SNS. As Table 7 shows, we included two OFCOM
types, both in the category of sporadics. It was not appropriate to identify lurkers in the
OFCOM study. There may be a connection between attention seekers and actives,


because the latter type of users was the most eager to publish photographs (24%) in our
study, as shown in Table 6.
Rogers’s categories are related to the time needed for different consumers to adopt
(technological) innovations. Yet the present study identified user types by examining
SNS usage in general, whether or not the adoption of innovations was involved.
Furthermore, given that SNSs are themselves an innovation, our study investigated the
behaviour of people who have already adopted an innovation. The application of different
criteria for the identification of segments limits the possibilities for drawing comparisons
between Rogers’s study and this one. However, the degree of use and the general usage
pattern of the technology are important variables that describe the extent of diffusion of
the innovation (Shih and Venkatesh, 2004). From this perspective, it is possible that
innovators are present among the actives in our typology. Early adopters may be mainly
associated with actives and debaters. If we use education as a criterion, debaters may be
the most obvious category to associate with early adopters, because debaters are the
highest educated user type. Finally, although the late majority and the laggards might not
be using SNS at the moment, if we have to identify one of our types that should be
associated with these groups, it would be the sporadics. However, the most likely
situation is that laggards and the late majority have not yet joined SNS communities.
These categories are given in brackets in Table 7. Nielsen’s (2006) typology is more
related to a participation related to UGC contribution level in total and will be discussed
in the next section.

6.4 Participation inequality: A 50-30-20 rule in locally bounded community
Based on our findings of participation in terms of UGC level of contributions, we would
suggest a 50-30-20 rule for participation inequality:
• 50% explains the total of sporadics (19%) and lurkers (27%) and their overall
passive participation level. These two user types contributed little to the community
activity, and are users who more or less correspond with Nielsen’s (2006) lurkers.
• 30% of our sample, counting debaters and socialisers together, corresponds to
Nielsen’s (2006) ‘intermittent contributors’, which contribute from time to time.
• 20% refers to the actives in our sample, and corresponds correspond with Nielsen’s
(2006) ‘heavy contributors’. Actives are the heaviest contributors and account also
for most of the action in the SNSs.
In comparison, Nielsen’s (2006) study reported lurkers to represent 90%, indicating a
significant difference between our study, with a 50-30-20 participation, and the 90-9-1
rule presented by Nielsen. This difference might be explained by the fact that users in our
sample mainly contribute with textural comment and amateur photos inside smaller and
locally bounded communities, and not with videos on YouTube or encyclopedia articles
in Wikipedia. Hence, the 90-9-1 rule might still be applicable to large, open communities
such as YouTube or Wikipedia but not for smaller and locally bounded SNS
communities. The latter communities obviously have lower thresholds for contribution,
compared to high-profile sites such as Wikipedia or YouTube.
Another explanation for the, in general, higher level of participation in locally
bounded communities compared to large-scale networks such as YouTube is that users
share content with their peers and not with complete strangers. For example, in previous


studies, social presence dimensions have been associated with usage patterns (e.g., Shen
and Khalifa, 2009). Similarly, social presence indicators were found to be positively
correlated with the level of tagging in Flickr (Nov et al., 2008). In our study and in the
SNS where video-sharing is technically supported (Underskog.no), we see that as many
as 7% cite the purpose of their visit as uploading a video and that 25% contribute once a
month or more. These findings suggest a high level of participation in small and local
SNSs, even with video contribution, probably caused by experienced social presence in
such sites.

6.5 Migration of user types?
Different modes and levels of participation might reflect different levels of skills and
advancements in how users use SNSs. We might suggest that, over time, people develop
more advanced communication skills or user type patterns in SNSs, as most people start
out as sporadics when they are introduced to SNS, and in time progress to a higher level
of usage, either as socialisers, debaters, or actives. For instance, the debater group is
typically characterised by older users compared to the users among sporadics and
socialisers. Future research should try to confirm such a migration and investigate how
and in which directions these patterns develop over time.

6.6 Limitations
The generalisability of the present research is limited by
a the geographic scope of the sample (Norway)
b the more or less self-selected sample.
With respect to a, the Norwegian population is among the most eager SNS users in the
world, and Norway is, according to Facebook, “the biggest Facebook nation in the world”
when adjustments have been made for population size (Brandtzæg and Lüders, 2008).
Therefore, our Norwegian sample should be regarded as interesting. Still, further research
should be conducted that takes into account other nations as well, to identify any cross-
nation variations. With respect to b, the SNS members who participated in our study were
recruited through a link on the sites rather than through random sampling across the
population and are mainly represented by young people. In addition, girls are
overrepresented. This method of selection threatens the external validity of the study
because we do not have full control over possible sources of systematic biases in our
sample. However, this weakness of the study may be partially overcome, because the
participants were recruited from four different SNSs. This constitutes an improvement
over previous studies (Ellison et al., 2007) in which only one community was studied.
We also tried to involve a broader segment of SNS users by rewarding participation in
the survey with an entry to a raffle for which the prize was a travel voucher; this was
thought to appeal to a wide range of people. However, except for the potential bias of
self-selection discussed, we cannot see any other factors that should reduce the
representativeness of the sample with respect to age or gender. Most SNSs are skewed in
life stage/age and gender toward a majority of younger people (Pfeil et al., 2009) and
females participating (Hargittai, 2007), which is reflected in our data sample.


Further, the identification of the user typology in SNSs merely reflects a snapshot of
user behaviour taken at a particular time, whereas usage behaviour and SNS options are
constantly changing. The boundary between one user type and another is also fluid; some
users are at the cluster centre, while others may be on the border between two or even
three clusters. However, the qualitative data collected from typical users at the cluster
centres validate the results. A validation of the typology was also reflected in the results
reporting statistically significant differences in UGC production and social contact among
different user types.
Finally, our cluster analysis does not produce a definitive solution; instead, the
analyst usually has a choice among several probable solutions. Our solution with five
clusters resulted in groups of adequate sizes – no fewer than 11% of the cases and a
maximum of 27% of the cases (see Table 2). Five and four clusters are common when
constructing a typology. In addition, the results depend on the set of questions asked.
Other criteria may also be useful, and it is not claimed here that all possible user
behaviour is described. However, the alternative ‘other’ was not chosen very frequently
among the sample participants.

7 Conclusions

This study has outlined a proposed typology that can be used to describe the ways in
which SNSs users participate. SNSs have become a predominant environment for social
interactions and involvement online in the past few years, which might indicate new
forms of participation inequality or a new digital divide. However, few quantitative
studies have been performed that extensively explore usage behaviour in SNSs. The user
typology identified in this study will help provide a nuanced understanding of an
increasingly fragmented population of SNS users and allows much more subtlety in
targeting SNS users, rather than simply lumping them all together in a single category.
Our findings contribute to a new way to identify participation inequality. A typology will
aid the design of successful SNSs by enabling the requirements of different user types to
be formulated. A user typology could help requirement engineers identify stakeholders in
their systems during the requirement analysis and thus include the diversity of users and
their requirements. An SNS that desires to nurture a particular user type can be designed
more successfully. For example, debaters could be nurtured by designing an SNS that
allows members to share and discuss different topics more easily. In other words, SNS
companies will need to craft a different design strategy if they want a target audience
with a higher proportion of socialisers than debaters.
By revealing the distinct characteristics of the user types, we have shown not only the
differences in usage behaviour and degree of motivation but also how this pattern is
related to different kinds of SNSs and the genders and ages of the sites’ members. In
terms of participation inequality, we also suggest a new 50-30-20 rule for participation in
smaller and locally bounded online communities. Further, our findings could contribute
significantly to the evaluation of SNSs. By being aware of and understanding different
user types, we will be able to spot the roots of existing problems or seeds of success
among different user types.
Further, it is important for the information and communication technologies (ICT)
industry to determine how different patterns of SNS use are linked to different user
models, so that new SNS services can be personalised and public services can overcome


the digital divide by promoting services that target different user types. Finally, a user
typology can help scholars see how various types of usage are associated with different
social outcomes.

7.1 Future research
Our study opens up a number of avenues for further research. First, a study should be
conducted to determine whether our findings about the robustness of the SNS user
typology are correct. A slightly modified questionnaire-tool can be found online
(http://www.tinyurl.com/6xxobl), which makes it possible for others to reproduce the
study. Second, the user typology should be analysed in a long-term study, as both
technology options and usage behaviour may evolve over time. For example, meta-
analyses of computer-mediated communication indicate that internet users progress from
initially asocial information gathering toward more social activities (Walther et al., 1994).
Third, interviews could be conducted or observations made in an attempt to
cross-validate our findings. Finally, the investigation of user typologies in SNSs as such
typologies apply to SNSs in different countries would be worthwhile to identify
cross-cultural differences.

Acknowledgements

The research leading to these results received funding from the CITIZEN MEDIA project
(038312) FP6-2005-IST and the RECORD-project (180135/S10), supported by the
VERDIKT programme of the Research Council of Norway. The authors would like to
thank all the SNSs and the users’ participation in this study.

References
Barnes, S.J., Bauer, H.H., Neumann, M.M. and Huber, F. (2007) ‘Segmenting cyberspace: a
customer typology for the internet’, European Journal of Marketing, Vol. 41, No. 1, pp.71–93.
Bishop, J. (2007) ‘Increasing participation in online communities: a framework for
human-computer interaction’, Computers in Human Behaviour, Vol. 23, No. 4, pp.1881–1893.
Blashfield, R.K. and Aldenderfer, M.S. (1978) ‘The literature on cluster analysis’, Multivariate
Behavioral Research, Vol. 13, No. 1, pp.271–295.
Boyd, D. and Ellison, N.E. (2007) ‘Social network sites: definition, history and scholarship’,
Journal of Computer-Mediated Communication, Vol. 13, No. 1, article 11, available at
http://www.jcmc.indiana.edu/vol13/issue1/boyd.ellison.html.
Brandtzæg, P.B. (2010) ‘Towards a unified media-user typology (MUT): a meta-analysis and
review of the research literature on media-user typologies’, Computers in Human Behavior,
Vol. 26. No. 5, pp.940–956.
Brandtzæg, P.B. and Heim, J. (2008) ‘User loyalty and online communities: why members of
online communities are not faithful’, INTETAIN, Proceedings of the 2008 ICST Second
International Conference on Intelligent Technologies for Interactive Entertainment,
8–10 January, ACM Digital Library, Playa del Carmen, Cancun, Mexico, available at
http://www.portal.acm.org/citation.cfm?id=1363200.1363215.
Brandtzæg, P.B. and Lüders, M. (2008) ‘eCitizen 2.0: the ordinary citizen as a supplier of
public-sector information’, Report for the Department of Modernisation and Administration in
Norway, SINTEF, Oslo, Norway, available at http://www.hellotxt.com/l/G6gh.


Brandtzæg, P.B., Heim, J. and Kaare, B.H. (2010) ‘Bridging and bonding in social network
sites – investigating family-based capital’, Int. J. Web Based Communities.
Brandtzæg, P.B., Heim, J., Karahasanović, A., Skjetne, J., Dahl, H., Warholm, L.,
Vanattenhoven, J., Nielsen, T.K., Vermeir, L., Pierson, J., Lievens, B. and Jacobs, A. (2009)
Non-professional Users as Co-creators of Media Content, P6-2005-IST-41 (038312), D1.1.3.
Bunn, M.D. (1993) ‘Taxonomy of buying decision approaches’, Journal of Marketing, January,
Vol. 57, pp.38–56.
Butler, B.S. (2001) ‘Membership size, communication activity, and sustainability: a resource-based
model of online social structures’, Information Systems Research, Vol. 12, No. 4, pp.346–362.
Ellison, N., Steinfield, C. and Lampe, C. (2007) ‘The benefits of Facebook ‘friends’: exploring the
relationship between college students’ use of online social networks and social capital’,
Journal of Computer-Mediated Communication, Vol. 12, No. 4, pp.1143–1168.
Gore, P.A. (2000) ‘Cluster analysis’, in Tinsley, H.E. and Brown, S.D. (Eds.): Handbook of
Applied Multivariate Statistics and Mathematical Modelling, Academic Press, New York.
Hargittai, E. (2007) ‘Whose space? Differences among users and non-users of social network sites’,
Journal of Computer-Mediated Communication, Vol. 13, No. 1, article 14, available at
http://www.jcmc.indiana.edu/vol13/issue1/hargittai.html.
Heim, J., Brandtzæg, P.B., Endestad, T., Kaare, B.H. and Torgersen, L (2007) ‘Children’s usage of
media technologies and psychosocial factors’, New Media & Society, Vol. 9, No. 3, pp.49–78.
Horrigan, J.B. (2007) ‘A typology of information and communication technology users’, Pew
Internet Report, May, USA.
Johnsson-Smaragdi, U. (2001) ‘Media use styles among the young’, in Livingstone, S. and
Bovill, M. (Eds): Children and their Changing Media Environment: A European Comparative
Study, Lawrence Erlbaum Associates, Mahwah, NJ, London, pp.131–141.
Kollock, P. and Smith, M. (1996) ‘Managing the virtual commons: cooperation and conflict in
computer communities’, in Herring, S. (Ed.): Computer-Mediated Communication: Linguistic,
Social, and Cross-Cultural Perspectives, John Benjamins, Amsterdam, pp.109–128.
Kozinets, R. (1999) ‘E-tribalized marketing? The strategic implications of virtual communities of
consumption’, European Management Journal, Vol. 17, No. 3, pp.252–264.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S. et al. (1998) ‘Internet paradox – a social
technology that reduces social involvement and psychological well-being?’, American
Psychologist, Vol. 53, pp.1017–1031.
Nielsen, J. (2006) ‘Participation inequality: encouraging more users to contribute’, Jakob Nielsen’s
Alertbox, 9 October 2006, available at
http://www.useit.com/alertbox/participation_inequality.html (accessed on 27 May 2009).
Nov, O., Naaman, M. and Ye, C. (2008) ‘What drives content tagging: the case of photos on
Flickr’, Proceedings of CHI 2008: ACM Conference on Human Factors in Computing
Systems, April, Florence, Italy, pp.1097–1100.
OFCOM (2008) ‘Social networking. a quantitative and qualitative research report into attitudes,
behaviours and use’, Office of Communication, London.
Ortega Egea, J.M., Menéndez, M.R. and González, M.V.R. (2007) Diffusion and Usage Patterns of
Internet Services in the European Union Information Research, Vol. 12, No. 2, available at
http://www.informationr.net/ir/12-2/paper302.html (accessed on 27 May 2009).
Pfeil, U., Arjan, R. and Zaphiris, P. (2009) ‘Age differences in online social networking – a study
of user profiles and the social capital divide among teenagers and older users in MySpace’,
Computers in Human Behaviour, Vol. 25, No. 3, pp.643–654.
Preece, J. (2000) Online Communities: Designing Usability, Supporting Sociability, Wiley,
Chichester.
Preece, J. and Maloney-Krichmar, D. (2003) ‘Online communities’, in Jacko, J. and Sears, A.
(Eds.): Handbook of Human-Computer Interaction, pp.596–620, Lawrence Erlbaum
Associates Inc. Publishers, New York.


Preece, J. and Shneiderman, B. (2009) ‘The reader-to-leader framework: motivating
technology-mediated social participation’, AIS Transactions on Human-Computer Interaction,
Vol. 1, No. 1, pp.13–32.
Quan-Haase, A. and Wellman, B. (2004) ‘How does the internet affect social capital?’, in
Huysman, M. and Wulf, V. (Eds.): IT and Social Capital, MIT press, New York.
Rogers, E.M. (1962, 2003) Diffusion of Innovations, Free Press, New York.
Shah, D., Kwak, N. and Holbert, R. (2001) ‘‘Connecting’ and ‘disconnecting’ with civic life:
patterns of internet use and the production of social capital’, Political Communication,
Vol. 18, No. 2, pp.141–162.
Shen, K.N. and Khalifa, M. (2009) ‘Design for social presence in online communities: a
multidimensional approach’, AIS Transactions on Human-Computer Interaction, Vol. 1,
No. 2, pp.33–54.
Shih, C. and Venkatesh, A. (2004) ‘Beyond adoption: development and application of a use-
diffusion model’, Journal of Marketing, Vol. 68, No. 1, pp.59–72.
Tan, P., Steinbach, M. and Kumar, V. (2006) Introduction to Data Mining, Pearson, Addison
Wesley, New York.
Walther, J.B., Anderson, J.F. and Park, D. (1994) ‘Interpersonal effects in computer-mediated
interaction: a meta-analysis of social and anti-social communication’, Communication
Research, Vol. 21, No. 2, pp.460–487.
Wellman, B. and Gulia, M. (1999) ‘Virtual communities as communities: net surfers don’t ride
alone’, in Smith, M.A. and Kollock, P. (Eds.): Communities in Cyberspace, pp.167–194,
Routledge, New York, NY.

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