A typology of sns users


Published on

Brandtzæg, P.B. & Heim, J. (2011). A typology of social networking sites users. International Journal of Web Based Communities,7(1), 28-51.

Published in: Technology, Education
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

A typology of sns users

  1. 1. 28 Int. J. Web Based Communities, Vol. 7, No. 1, 2011A 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 andCopyright © 2011 Inderscience Enterprises Ltd.
  2. 2. 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 IntroductionThe popularity of social networking sites (SNSs) such as Facebook, Orkut, LinkedIn, andMySpace has boosted social interactions and user engagement online among severalhundred million users worldwide. In Norway, where this study takes place, 53% of theNorwegian online population between 15 and 74 years are users of SNSs (Brandtzæg andLüders, 2008). According to Bishop (2007), SNSs have changed the nature of online userparticipation into more democratising forums where people can communicate and addtheir user-generated content (UGC). SNSs are said to represent an important mechanismfor knowledge exchange and sharing (Shen and Khalifa, 2009). The change from passiveconsumption to more active use indicates more varied use and forms of participation(Preece and Shneiderman, 2009) and a new digital divide between actives andnon-actives (Brandtzæg, 2010). Participation inequality in SNSs might, therefore, affectpeople’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 thevarious 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 forwhat purposes, especially outside the USA. Therefore, this article explores the patterns ofadoption and use of SNSs by developing a typology of the users of these sites. Thedevelopment of a classification scheme of SNS users’ patterns identified through anempirically-based typology could reveal the mix of underlying activities and participationinequality of such sites (e.g., Bunn, 1993). Such a typology could also help researchers,designers, and managers understand what motivates technology-mediated socialparticipation. This will enable them to improve the interface design and social supportfor such sites (e.g., Brandtzæg et al., 2010; Preece and Shneiderman, 2009; Shen andKhalifa, 2009). More specifically, the main objective of this article is to outline an empirically-basedSNS typology that can be used to describe and identify distinct user types characterisedbased on the ways in which SNS users behave in terms of participation level andparticipation objective. There were two sub-goals to achieve this:
  3. 3. 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 comparingthe different user types’ responses to other survey questions reflecting a diverse userparticipation. In addition, different user types are validated by analysing typicalqualitative responses from distinct user types. It should be noted that the approach in this study is exploratory rather than one oftesting theoretical hypotheses. This is because no firm body of empirically-basedtheoretical knowledge exists about users of SNS, especially not about typologies.1.1 A user typology approachThe 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 offrequency of use only (Heim et al., 2007) and lacks a framework to identify differentmedia 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 theSNS, as shown in Figure 1. Both participation level and participation mode are treated astwo equally important dimensions; at the same time, these dimensions cover a broadrange of SNS activities such as time-killing, debating, and UGC production. Thisapproach is similar to Shih and Venkatesh’s (2004) conceptualisation of user types interms of variety and rate, where variety referred to the different ways in which theproduct 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
  4. 4. A typology of social networking sites users 31In this paper, user types in SNSs are conceptualised by1 their participation objective2 level of participation.Users may be either high or low in terms of their participation, but some users may haveboth a recreational mode and an informational mode of communication.2 Previous research on user types in SNSs2.1 User typologiesTo generate the research questions (RQs) and validate our approach for this article, thissection provides a brief introduction to the study of user types that might relate to SNSusage. First, it should be noted that the categorisation of people based on a typology isnot a new theoretical approach. In attempting to understand human nature, psychologistshave been developing personality typologies for many years. The most influential theory in research on technology use and adoption that applies toa typology is Rogers’ diffusion of innovations model (Rogers, 1962, 2003). This modelexplains the process by which innovations are adopted and offers the followingcategorisation 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 andadoption rate in general, rather than the differences in actual media behaviour. Still, thetheory might prove useful in determining methods to describe, predict, and identifydifferent user types in an SNS, since SNSs are a kind of innovation. Some media studies have applied a typology approach to understand differences inmedia behaviour in general. Johnsson-Smaragdi (2001) identified four main user typesamong children in nine European countries:1 low media users2 traditional media users3 specialists4 screen entertainment fans.A similar approach was suggested by Heim et al. (2007) in their study of children’smedia usage in Norway, and by Ortega Egea et al. (2007) in their attempt to understandthe diffusion and usage patterns of internet services in the European Union amongcitizens in general.
  5. 5. 32 P.B. Brandtzæg and J. Heim When it comes to online communities or social media sites such as SNSs, only a fewattempts 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 socialmedia with UGC. An example of this rule is Wikipedia, where more than 99% of usersdo not contribute and only consume. Wikipedia has 68,000 active contributors, who makeup 0.2% of the 32 million unique visitors to the site in the USA alone. A roughexplanation 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 detaileddescriptions of these results or of the method exists, other than the defective informationpublished on Nielsen’s Alterbox, a blog site. However, the 90-9-1 rule has beeninfluential in the discussion on social media usage since the idea was launched, and itshould be interesting to see if we find the same pattern in the study presented in thispaper. An online community user typology was suggested by Kozinets (1999). In atheoretical paper, he described various types of community members according to twofactors: the degree of consumption activity and the intensity of relationships with othermembers of the virtual community. These two factors enable four distinct types ofcommunity 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 ties2 Minglers are users who maintain strong social ties while being marginally interested in consumption activity3 Devotees are users who maintain a strong interest in consumption but have few social attachments4 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. Kozinetsalso stressed the importance of categorisation in making the fragmentation of onlinecommunity 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 people2 attention seekers are users who crave attention and comments from others, often by posting photographs of themselves and friends
  6. 6. A typology of social networking sites users 333 followers are users who join sites to keep up with what their peers are doing4 faithfuls are users who rekindle old friendships, often from school or university5 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, butrather on sparse qualitative and theoretical judgments. This means that we know littleabout both user types and the distribution of user types in SNSs. Further, to the best ofour knowledge, OFCOM’s typology is the only one that addresses SNSs specifically.Generally, the sparseness of empirical material related to SNSs and typologies of theirusers (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 roledifferent from that described in the early literature on virtual communities (Ellison et al.,2007). This literature focused on how community participants who were geographicallydispersed 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 mergesseveral interactive services and application related to work, leisure, and information(Brandtzæg and Heim, 2008). For example, more than 350,000 applications are in use onthe Facebook platform according to Facebook’s September 2009 statistics. It should,therefore, be highlighted that an empirically-based user typology that divides the varioususer behaviours into meaningful categories in SNSs is absent.2.2 User types and social implicationsA user typology could also benefit research into the social implications of various SNSusage, and hence, it is important to understand various forms of participation. Socialcapital can be used to investigate the social implications of SNSs. The concept has twocomplementary aspects:1 social contact, which covers patterns of interpersonal communication, including the frequency of social contact and social events2 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 andthe 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 andfound 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 lifecontentment. In contrast, an ‘information exchange’ pattern had a positive impact on thesame social variables. These results suggest a connection between different user typesand various social outcomes.
  7. 7. 34 P.B. Brandtzæg and J. Heim3 Research questionsAn exploratory approach is a natural point of departure in a relatively immature field ofresearch. 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 usedto identify UGC participation, social contact, and political activity included distinctmeasures 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 Method4.1 SampleThe data were collected using an online questionnaire over a three-week period in March2007 on four different SNSs. These SNSs were chosen because, at the time of theinvestigation, they were the most popular SNSs in Norway, which, consequently, mightgive us a good picture of what typical SNS users seek regarding relationships. Thefrequent usage and popularity of these sites were documented in a recent report for theMinistry 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 and2,155 were men, with a median age of 16 years. Demographic details given by therespondents 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
  8. 8. A typology of social networking sites users 35 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). Inaddition, they reflect some interesting differences (e.g., size and/or focus) that make itpossible to investigate more closely how different user types are distributed in SNSs withdifferent numbers of members and diversities in focus. Table 1 describes these SNSs inmore detail.Table 1 Description of the four SNSs used in this studySNS Origin Members DescriptionBiip.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 ProcedureWe used an online survey questionnaire that contained open and fixed-responsequestions. We urged the site owners to distribute the survey to all of their members insidethe SNS, either by a tag in the members’ user profiles or with a message. This was doneon Biip.no and HamarUngdom.no, whereas on Nettby.no and Underskog.no, the surveywas merely introduced by a banner ad on the front page and by blogging contributionsthat urged users to participate. This type of survey allowed us to easily access a verylarge number of users of these SNSs, while the users were actually using the sites. Tomotivate as many users as possible (i.e., including less active to very active users) toparticipate in the survey and complete the questionnaire, all the participants from the fourSNSs were entered into a raffle to win a travel gift coupon worth US $1,750. A technicalsolution made it impossible for any user to participate multiple times in the study.
  9. 9. 36 P.B. Brandtzæg and J. Heim4.3 MeasuresAll the measures and questions were identical in all four online surveys.4.3.1 Socio-demographicsWe 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 yourreasons 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 ofthe respondents. This question was followed by 18 yes/no alternatives that reflectdifferent modes of communication (informational vs. recreational) and levels ofparticipation (high vs. low). These alternatives are shown in Table 2. Some examples forthe informational mode are ‘look for new information’, ‘discuss’, and ‘makeappointments for meetings with other people’, while examples for the recreational modeinclude ‘profile surfing’, ‘kill some time’, and ‘look for a new friend’. These alternativesaimed to cover most of the possible and common user activities or behaviours within theSNS. The fact that very few participants used the alternative ‘other’ also indicates that weconsidered most of the possible user activities in the present questionnaire. The 18activities were defined based on a discussion with the developers of Nettby.no and a pilotinvestigation conducted with ten target users between the ages of 14 and 38 years whohad 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 thesubject’s own SNS experiences, so that we could achieve a more in-depth understandingof the user typology yielded. In addition, the questions were selected with the aim ofconducting a qualitative analysis that could verify the validity of the different user typeswe identified in the statistical cluster.4.3.4 UGC participationTo answer RQ3 concerning how user types relate to levels of UGC production, wemeasured UGC contribution in terms of how often users reported text production, imagecontribution, and video contribution on a six-point scale (i.e., ranging from ‘never’ to‘several times a week/daily’).
  10. 10. A typology of social networking sites users 374.3.5 Social capital – social contact and civic engagementThe two aspects of social capital (RQ4), frequency of contact and level of civicengagement, are commonly used measures in surveys to reveal people’s level ofinvolvement in a community (Quan-Haase and Wellman, 2004). Social contact wasmeasured in terms of frequency of contact with different forms of relationships:a friendsb familyc 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. Civicengagement was measured in terms of the user’s attitude toward his or her politicalengagement with the question, “How important is it for you to express yourself politicallyinside your SNS?” using a six-point Likert scale (i.e., ranging from ‘not important’ to‘very important’).4.4 AnalysisA k-means cluster analysis (SPSS, v.14) was employed to identify typical user types inthe SNS. Cluster analysis is an empirical technique designed to partition a heterogeneoussample into homogeneous subgroups to discover classifications within complex datasets(Blashfield and Aldenderfer, 1978). The method implies the classification of similarobjects 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 – oftenproximity, according to some defined distance measure. It is a method of categorisingobjects 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 ofusage patterns into occurring user types in SNSs as well. K-means cluster analysis (see Tan et al., 2006) is recommended when the number ofentities (persons) in the analysis is high (more than 1,000). All variables in the analysiswere dichotomous: zero or 1. We used 18 variables and 5,233 respondents, and therewere no missing values. However, it is always a challenge to define how many cluster solutions should bechosen. Several different cluster solutions were evaluated for their effectiveness inproducing cohesive groups that were distinct from one another, large enough in size to beanalytically useful, and substantively meaningful (Horrigan, 2007). To validate the finalcluster solution, we repeated the same analysis ten times with a random sample of 40% ofall cases in the material. In six out of the ten analyses, all the clusters were reproduced. Inone of the ten analyses, four of the five clusters were reproduced. In three out of the tenanalyses, three of the five clusters were reproduced. This indicates that the final solutionwas robust. A five-cluster solution seems to be an adequate size when we compare this solutionwith 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 betweenfour and six.
  11. 11. 38 P.B. Brandtzæg and J. Heim To further validate the cluster solution and to illustrate the different user types morein depth, one or two typical representatives for each pattern were extracted from thedataset and presented. These typical respondents were chosen from those with the leastdeviation from their cluster mean. A further requirement was that they gave a reasonablenumber of comments in the open-ended questions for statistical data analysis.5 Results5.1 The user typologyThe analysis suggested five distinct clusters, interpreted as user types that reflect differentparticipation levels and communication modes or deeper motivations for using an SNS.Table 2 shows the proportion of cluster members indicating their reason “for visiting theSNS today” in terms of user activities reflecting the users’ typical behaviour inside thecommunity. The clusters were given user-type labels, the formulation of which was basedon an interpretation of the values in Table 2, indicating the users’ level of activity indifferent 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 ActivesNote: To make this table more readable; values below 0.10 are in italic, while values above 0.70 are in bold.
  12. 12. A typology of social networking sites users 39As 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. Socialisersmostly socialise and engage in other activities to a lesser extent. Actives are so-labelledbecause they are active in almost every type of participation inside the SNS – theysocialise, 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 howthe clusters follow the specific criteria related to the level of participation (low/high) andmodes 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 participationNote: 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
  13. 13. 40 P.B. Brandtzæg and J. HeimFigure 3 gives the distribution in percentage of the five distinct user types. Lurkersrepresent the largest user group (27%), while debaters are the smallest user groupidentified (11%). Table 3 shows an overview of the different user types with regard to demographicssuch as gender, age, and the level and proportion of users who have received highereducation (college or university) (see RQ2). All reported differences between the clustersare significant in regard to Pearson’s chi-square (χ2) test; this is due in part to the largenumber 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 39Note: 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 theyoungest user type (median age: 15 years). In terms of gender, girls are significantlymore common among lurkers, socialisers, and actives. As one might expect, debaters arethe most educated user type. However, sporadics are also educated. This could be relatedto 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 inTable 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 Socialisers 22 15 32 3 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 relatedto each user type to answer RQ1. This section also includes results from the analyses ofthe answers to the open questions from users representing each user type.
  14. 14. A typology of social networking sites users 415.1.1 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 moretoward an informational mode since they, for the most part, check their status and see ifsomebody has contacted them (see Table 2). Sporadic users give few reasons for visitingthe SNS. They are spread equally over the four SNSs and age groups. There is no largegender difference (see Table 3). A typical sporadic user is exemplified in the qualitativematerial, using the open-ended questions. One typical user is August, a 16 year old. Hehas 17 people in his profile but has been in contact with only one other person during thepast week. August joined the community to keep in touch with friends. He is notinterested in contributing UGC. He puts it this way: “It is just not my thing to submit abunch 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 quitelow in participation and participate in activities that are more related to recreation. Theseusers 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 sometime” (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 to31% on HamarUngdom.no) and consist of more females (58%) than males (42%). Atypical ‘lurker’ identified in the qualitative analysis is June, a 16 year old from a smalltown. She has been a member of HamarUngdom.no for about three years but is starting tolose interest. She thinks that technology is important for entertainment but less so forkeeping 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 agood 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 theirbehavior is characterised by recreational in terms of ‘small talk’ with others, but theusers’ 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 ofteenage girls (median age: 15 years; 69% of the sample), mainly on Biip.no (32%) andpractically absent among the Underskog.no members (3%). A typical ‘socialiser’ in thequalitative data is Mary, a 14 year old who lives in a medium-sized town. She is an eagermember of Biip.no and has been a member for almost a year. The internet is veryimportant to her. She uses the community to keep in touch with friends, and she likes tomake connections with new people. In the last week, she has been in touch with fivepeople. She has about 30 contacts in her profile. Her response to the question, “What isyour most important reason for using the SNS?” is, “It’s interesting. I have something todo 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”.
  15. 15. 42 P.B. Brandtzæg and J. Heim5.1.4 Type 4: debaters (11%)Debaters are as high as socialisers in terms of participation level, characterised by beinghighly involved in discussions, reading, and writing contributions in general (seeTable 2). In addition, this participation mode is related to a more informal practice assuggested in Figure 2. The debating pattern is unequally distributed among thecommunities: very frequent on Underskog.no and practically absent on Biip.no. The usersare somewhat older than the medians of other user types, and there is practically nogender difference. A typical ‘debater’ is odd, a 42 year old who lives in Oslo. He hasbeen a member of Underskog.no for more than a year. He is university educated and likesto discuss and express himself in writing. He depends on the internet for carrying outpractical tasks and uses it mainly for instrumental reasons. He considers videocontribution too time-consuming. He also regards his SNS as being more text- andpicture-related than video-focused. He uses the SNS to keep updated on cultural eventsand new publications, as well as for social contacts, explaining this as follows: “I getinformed about events, publications, and Net experiences; at the same time I am makingbonds 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 ofparticipation activities within the community, which includes being a member to “publishand share pictures” (see Table 2). The majority in this group are young females, as shownin Table 3. This user type is distributed unequally among the SNSs, being most commonon Nettby.no and HamarUngdom.no. A typical Active user is Lena, a 16 year old wholives in the countryside. She has been a member of the HamarUngdom.no community formore than three years and is still very active. During the week before she answered thequestionnaire, she was in contact with 20 people, almost everybody in her profile. Whenshe is logged on, she engages in many activities, usually related to events and publishingmusic or videos. She uses the SNS mainly for social contact and communicates primarilywith friends in her profile. She contributes videos and summarises her activity in thisway: “I wish I had my own video camera. So far, I am just uploading and sharing videosthat others have made”. Her willingness to be even more involved in UGC participationis high.5.2 User types and social capitalTable 5 (as well as Figure 2) answers RQ4 and shows that actives are most active in thesocial field as well. Socialisers and debaters also have high activity here; they frequentlyhave 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 dailybasis. Further, debaters and actives more often report that political expression within theSNS is ‘very important’ to them. These user types view their SNS as a significant arenafor community participation in terms of political activity. On the other hand, lurkers andsporadics are less interested in this type of activity. This result also indicates that thehighest level of social capital inside an SNS is associated with actives, socialisers, anddebaters.
  16. 16. A typology of social networking sites users 43Table 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 veryUser types 150 contacts contact contact than 30 people last important to me for in user profile family friends week on SNS political expressionSocialisers 10 13 61 6 10Debaters 10 8 57 5 20Lurkers 9 5 45 2 6Sporadics 5 10 41 4 7Actives 16 15 69 12 185.3 User types and UGC contributionTable 6 presents results of the answers to RQ3 – an overview of the different user typesin regard to the amount of UGC they contribute (several times a week and daily, addedtogether) and the difference among the user types in terms of their willingness to publishvideos 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 Socialisers 33 16 2 27 Debaters 55 12 5 34 Lurkers 26 7 5 22 Sporadics 27 8 2 20 Actives 51 24 6 40Debaters are the most active group in terms of the contribution of text. This is notsurprising, given that discussion seems to be their passion inside the community.However, actives and socialisers also actively contribute text. Lurkers are less active andare more of an observant type. Actives are most active in their photograph (24%) andvideo (6%) contributions; actives are also significantly more willing to publish videos inthe future. It should be noted that reported video contribution is quite uncommon amongall user types. In general, the highest level of participation in relation to UGC is seenamong actives; debaters have the second highest level, and socialisers third. Lurkers andsporadics participate less. These results validate the typology.6 Discussion6.1 Five user types and participation inequalityFive distinct types of users have been identified based on their SNS behaviour patterns interms of participation level and participation mode in four SNSs. The user types in thisstudy reflect substantial differences in the underlying patterns of SNS usage, as well as
  17. 17. 44 P.B. Brandtzæg and J. Heimpreferences and reasons for participating in a community. The actives engage in more orless all activities within the SNS community, whereas the debaters interact with others forthe 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’ inthat they sporadically check to see whether anybody has contacted them. The lurkersmight be the only user group that is somewhat asocial, as these users seem to logon to the sites for ‘time-killing’ and consuming entertainment rather than socialinteraction. Together with socialisers, lurkers is the least active in contributing text andphotographs. In our study, we found that the user population, not surprisingly, is made up of theyounger 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 usagepatterns, reflecting different levels of motivation for participation. As expected, two maintypes of motivation for social participation in SNSs stand out. The first type is related to arecreational mode in terms of informal chatting, whereas the other is a moreinformational mode related to serious debate and discussion. Although both genders andmost age groups are represented in both modalities, younger girls are more typicallysocialisers, while older boys and men are typically debaters. These modalities haveexisted on the internet for many years, supported by informal e-mailing, chat channelssuch as IRC and MSN, and blogs. Nowadays, these forms of interaction are being mergedinto a single type of system: the SNS. This also underscores the fact that we see aconvergence 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 SNSsThe 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 thatinfluence and support different user types. In our study, all the different user types arefound inside the diverse SNSs, but the socialisers are more frequent in the Biip.nocommunity, whose members are younger, while Underskog.no is more often representedby debaters. Underskog.no has more highly educated users and a higher mean age thanthe other SNS. Underskog.no is also an SNS where members who share content are veryculturally and academically oriented. In addition, Underskog.no is a very small SNS. Theimportance of SNS size relative to the level of participation among users has beensuggested by others (e.g., Butler, 2001; Preece and Maloney-Krichmar, 2003). Smallerand locally bounded SNSs seem to involve more enthusiastic users who are willing toshare 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 greatermix of user types. This might be explained by the fact that these sites are free and open toeveryone. These SNSs have no specific interest target and, therefore, may also have amore diverse distribution of user types. For example, Nettby.no is associated with thelargest online newspaper in Norway and recruits a wide spectrum of users through thisconnection.
  18. 18. A typology of social networking sites users 456.3 Theoretical implicationsThis paper draws upon theoretical threads to create a framework for a typology of SNSusers according to their behaviour. To date, a typology of SNS users has not beenexplored in a way that draws upon a large source of SNS behavioural data combined withtheoretical insights from Kozinets’s (1999) marketing model and Rogers’s (2003)adoption and innovation model. These and other studies should be compared to give moreinsight into the connections between the different user types presented (see Table 7 for acomparison).Table 7 Connections between different models of user typesThis study’s Kozinets’s Nielsen’s OFCOM Rogers’s Justificationtypology typology ‘typology’ typology modelSocialisers 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. followersActives 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 ourmeaningful categories of user types. This might suggest that previous research ontypologies can help us understand and categorise the increasingly complex behavioursfound on SNSs (see Table 7). Kozinets’s (1999) market segmentation model seemed tobe the most similar typology when compared to the different user types in our SNStypology. However, Kozinets’s typology is related to the marketing and consumption ofproducts or the provision of information about goods inside virtual communities, ratherthan being a general user typology for SNS. As Table 7 shows, we included two OFCOMtypes, both in the category of sporadics. It was not appropriate to identify lurkers in theOFCOM study. There may be a connection between attention seekers and actives,
  19. 19. 46 P.B. Brandtzæg and J. Heimbecause the latter type of users was the most eager to publish photographs (24%) in ourstudy, 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 examiningSNS usage in general, whether or not the adoption of innovations was involved.Furthermore, given that SNSs are themselves an innovation, our study investigated thebehaviour of people who have already adopted an innovation. The application of differentcriteria for the identification of segments limits the possibilities for drawing comparisonsbetween Rogers’s study and this one. However, the degree of use and the general usagepattern of the technology are important variables that describe the extent of diffusion ofthe innovation (Shih and Venkatesh, 2004). From this perspective, it is possible thatinnovators are present among the actives in our typology. Early adopters may be mainlyassociated with actives and debaters. If we use education as a criterion, debaters may bethe most obvious category to associate with early adopters, because debaters are thehighest educated user type. Finally, although the late majority and the laggards might notbe using SNS at the moment, if we have to identify one of our types that should beassociated with these groups, it would be the sporadics. However, the most likelysituation 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 morerelated to a participation related to UGC contribution level in total and will be discussedin the next section.6.4 Participation inequality: A 50-30-20 rule in locally bounded communityBased on our findings of participation in terms of UGC level of contributions, we wouldsuggest 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 asignificant difference between our study, with a 50-30-20 participation, and the 90-9-1rule presented by Nielsen. This difference might be explained by the fact that users in oursample mainly contribute with textural comment and amateur photos inside smaller andlocally bounded communities, and not with videos on YouTube or encyclopedia articlesin Wikipedia. Hence, the 90-9-1 rule might still be applicable to large, open communitiessuch as YouTube or Wikipedia but not for smaller and locally bounded SNScommunities. 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 locallybounded communities compared to large-scale networks such as YouTube is that usersshare content with their peers and not with complete strangers. For example, in previous
  20. 20. A typology of social networking sites users 47studies, social presence dimensions have been associated with usage patterns (e.g., Shenand Khalifa, 2009). Similarly, social presence indicators were found to be positivelycorrelated with the level of tagging in Flickr (Nov et al., 2008). In our study and in theSNS where video-sharing is technically supported (Underskog.no), we see that as manyas 7% cite the purpose of their visit as uploading a video and that 25% contribute once amonth or more. These findings suggest a high level of participation in small and localSNSs, even with video contribution, probably caused by experienced social presence insuch sites.6.5 Migration of user types?Different modes and levels of participation might reflect different levels of skills andadvancements in how users use SNSs. We might suggest that, over time, people developmore advanced communication skills or user type patterns in SNSs, as most people startout as sporadics when they are introduced to SNS, and in time progress to a higher levelof usage, either as socialisers, debaters, or actives. For instance, the debater group istypically characterised by older users compared to the users among sporadics andsocialisers. Future research should try to confirm such a migration and investigate howand in which directions these patterns develop over time.6.6 LimitationsThe generalisability of the present research is limited bya 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 theworld, 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 researchshould 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 wererecruited through a link on the sites rather than through random sampling across thepopulation and are mainly represented by young people. In addition, girls areoverrepresented. This method of selection threatens the external validity of the studybecause we do not have full control over possible sources of systematic biases in oursample. However, this weakness of the study may be partially overcome, because theparticipants were recruited from four different SNSs. This constitutes an improvementover 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 inthe survey with an entry to a raffle for which the prize was a travel voucher; this wasthought to appeal to a wide range of people. However, except for the potential bias ofself-selection discussed, we cannot see any other factors that should reduce therepresentativeness of the sample with respect to age or gender. Most SNSs are skewed inlife stage/age and gender toward a majority of younger people (Pfeil et al., 2009) andfemales participating (Hargittai, 2007), which is reflected in our data sample.
  21. 21. 48 P.B. Brandtzæg and J. Heim Further, the identification of the user typology in SNSs merely reflects a snapshot ofuser behaviour taken at a particular time, whereas usage behaviour and SNS options areconstantly changing. The boundary between one user type and another is also fluid; someusers are at the cluster centre, while others may be on the border between two or eventhree clusters. However, the qualitative data collected from typical users at the clustercentres validate the results. A validation of the typology was also reflected in the resultsreporting statistically significant differences in UGC production and social contact amongdifferent user types. Finally, our cluster analysis does not produce a definitive solution; instead, theanalyst usually has a choice among several probable solutions. Our solution with fiveclusters resulted in groups of adequate sizes – no fewer than 11% of the cases and amaximum of 27% of the cases (see Table 2). Five and four clusters are common whenconstructing 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 userbehaviour is described. However, the alternative ‘other’ was not chosen very frequentlyamong the sample participants.7 ConclusionsThis study has outlined a proposed typology that can be used to describe the ways inwhich SNSs users participate. SNSs have become a predominant environment for socialinteractions and involvement online in the past few years, which might indicate newforms of participation inequality or a new digital divide. However, few quantitativestudies have been performed that extensively explore usage behaviour in SNSs. The usertypology identified in this study will help provide a nuanced understanding of anincreasingly fragmented population of SNS users and allows much more subtlety intargeting 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 willaid the design of successful SNSs by enabling the requirements of different user types tobe formulated. A user typology could help requirement engineers identify stakeholders intheir systems during the requirement analysis and thus include the diversity of users andtheir requirements. An SNS that desires to nurture a particular user type can be designedmore successfully. For example, debaters could be nurtured by designing an SNS thatallows members to share and discuss different topics more easily. In other words, SNScompanies will need to craft a different design strategy if they want a target audiencewith a higher proportion of socialisers than debaters. By revealing the distinct characteristics of the user types, we have shown not only thedifferences in usage behaviour and degree of motivation but also how this pattern isrelated to different kinds of SNSs and the genders and ages of the sites’ members. Interms of participation inequality, we also suggest a new 50-30-20 rule for participation insmaller and locally bounded online communities. Further, our findings could contributesignificantly to the evaluation of SNSs. By being aware of and understanding differentuser types, we will be able to spot the roots of existing problems or seeds of successamong 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 usermodels, so that new SNS services can be personalised and public services can overcome
  22. 22. A typology of social networking sites users 49the digital divide by promoting services that target different user types. Finally, a usertypology can help scholars see how various types of usage are associated with differentsocial outcomes.7.1 Future researchOur study opens up a number of avenues for further research. First, a study should beconducted to determine whether our findings about the robustness of the SNS usertypology are correct. A slightly modified questionnaire-tool can be found online(http://www.tinyurl.com/6xxobl), which makes it possible for others to reproduce thestudy. Second, the user typology should be analysed in a long-term study, as bothtechnology options and usage behaviour may evolve over time. For example, meta-analyses of computer-mediated communication indicate that internet users progress frominitially asocial information gathering toward more social activities (Walther et al., 1994).Third, interviews could be conducted or observations made in an attempt tocross-validate our findings. Finally, the investigation of user typologies in SNSs as suchtypologies apply to SNSs in different countries would be worthwhile to identifycross-cultural differences.AcknowledgementsThe research leading to these results received funding from the CITIZEN MEDIA project(038312) FP6-2005-IST and the RECORD-project (180135/S10), supported by theVERDIKT programme of the Research Council of Norway. The authors would like tothank all the SNSs and the users’ participation in this study.ReferencesBarnes, 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.
  23. 23. 50 P.B. Brandtzæg and J. HeimBrandtzæg, 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.
  24. 24. A typology of social networking sites users 51Preece, 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.