=Paper=
{{Paper
|id=None
|storemode=property
|title= A Typology of Collaboration Platform Users
|pdfUrl=https://ceur-ws.org/Vol-870/paper_2.pdf
|volume=Vol-870
}}
== A Typology of Collaboration Platform Users==
https://ceur-ws.org/Vol-870/paper_2.pdf
A Typology of Collaboration Platform Users
Anastasia Bezzubtseva1,2, Dmitry Ignatov1
1
Higher School of Economics, Moscow, Russia
2
Witology, Moscow, Russia
nstbezz@gmail.com, dignatov@hse.ru
Abstract. In this paper we present a review of the existing typologies of Inter-
net service users. We zoom in on social networking services including blogs
and crowdsourcing websites. Based on the results of the analysis of the consid-
ered typologies obtained by means of FCA we developed a new user typology
of a certain class of Internet services, namely a collaboration innovation plat-
form. Cluster analysis of data extracted from the collaboration platform Witolo-
gy was used to divide more than 500 participants into 6 groups based on 3 ac-
tivity indicators: idea generation, commenting, and evaluation (assigning
marks) The obtained groups and their percentages appear to follow the “90 – 9
– 1” rule.
Keywords. Crowdsourcing, typology classification, collaborative platform, in-
novation, social network, community, blog.
1 Introduction
Collaboration innovation platforms are relatively young and less common than blogs
or social networks (e.g., compare [1] and [2]), yet interest in their organization and
audience is not decreasing. The existing studies of consumer or media behavior of
Internet users cannot be fully applied to collaboration platform participants, while
general psychological or sociological typologies of people miss many important fea-
tures, inherent only to networking and crowdsourcing.
For a certain type of social network services, i.e. the collaboration innovation plat-
forms, finding user types pursues also some other objectives. Understanding user
types could make a major contribution in the platform effectiveness. For instance,
dynamic participant type detection and displaying are useful as a motivational game
component, and the type itself will probably supplement or refine the exiting rating
systems. Also, information about the amount of users of different groups could help
platform moderators turn community life to a beneficial for invention direction.
In this study we present a review of the existing Internet service user classifica-
tions. Based on examined materials we attempted to develop a new typology of col-
laboration platform participants using data of one of the projects of Russian innova-
tion platform Witology [10].
10 A. Bezzubtseva et al.
2 Terminology
In this paper we analyze not only collaboration platforms, but also all other kinds of
social networking services and Internet services, as typologies of their users can be
applied to platform participants. There is no fixed terminology in this area yet, but we
will try to give some definitions of the important concepts used in the research in
order to clarify its subject.
By Internet service we mean any website that provides any kind of service (e.g.
blogs, file-sharing networks, chats, multiplayer games, online shops). Internet ser-
vices which provide human interaction are referred to as Social Networking Services
(SNS). They include social networks (Facebook, MySpace, last.fm, LinkedIn, Orkut),
blogs (LiveJournal, Tumblr, Twitter), wiki (e.g., Wikipedia), media hosting sites
(Flickr, Picasa, YouTube), etc. [3], [4]. Social networking services often generate
online communities, i.e. groups of people, who share similar interests and communi-
cate via a certain Internet service. Some scientists [5], [6], [7] understand community
in a wider sense as the entire audience of some social networking service, which is
wrong, according to Michael Wu [8]. We kept the original author vocabularies when
describing the typologies, in other cases the first definition of community was used.
Crowdsourcing platforms are social networking services which are used to obtain
the necessary services, ideas or content from platform participants, i.e. platform
community, as opposed to regular staff or vendors [9]. Crowdsourcing (collabora-
tion) innovation platforms are the ones which focus on idea generation. Activities on
collaboration platforms often include message (idea or comment) posting, message
reading and message evaluation. The winning solutions and true experts are identified
on the basis of the amount and quality of such activities. Work on the platform usual-
ly goes as a certain time-limited project, devoted to some company’s problem. Witol-
ogy [10], Imaginatik [11], BrightIdea [12] and some other platforms are organized
this way; though, there are many collaboration sites which are not alike (see list [13]).
3 Research objectives
To begin a classification of collaboration innovation platform users, we plan to per-
form the following tasks:
1. Study of the existing Internet service user typologies. The discovered user types
and data mining techniques might be helpful in developing another typology.
2. Developing of a new typology of collaboration innovation platform. By means of
mathematical methods we plan to analyze data of one of the collaboration platform
project and identify distinct user types.
3. Comparison of the obtained percentages with the ones from existing studies. This
might help to understand whether the community under analysis is typical and to
find out, whether it can be improved (for example, by calculating community
health index [14]).
A Typology of Collaboration Platform Users 11
4 Review of the existing typologies
Despite the fact that the online community being a relatively young phenomenon, tens
of attempts in classifying internet users have been undertaken. Some of the studies
[15], [16], [17] explore only children’s media-behavior, others [18] investigate behav-
ior in terms of online shopping. A significant part of early typologies (e.g. [19], [20])
is developed based on frequency and variety of web and new gadgets use, which re-
sulted in rather trivial and similar typologies (generally people were divided into “ad-
vanced”, “average” and “non-users”, the three types were occasionally interspersed
with “entertainment” and “functional” users).
Almost half of the encountered researches used cluster analysis as means of ex-
tracting user types, factor analysis appeared to be the second most popular method.
Much more uncommon were regression analysis, qualitative in-depth analysis, graph
mining, statistical analysis, etc.
Very few authors based on some sociological or psychological theories or referred
to the existing typologies when classifying internet service users (it can be explained
by their desire to take a new look on the differences in human behavior). One of the
studies (Nielsen, 2006) [7] is not only descriptive, but is considered informal, and in
spite of that the classification and the “90 – 9 – 1” rule are highly respected and popu-
lar.
As for the user typologies of the communities, which organization is close to that
of innovation platforms, a notable part of papers is devoted to social network user
behavior analysis, but there are also some studies of behavior of blog and forum visi-
tors. Since information concerning behavior of collaboration platform participants has
not been found yet, several of social network and blog studies might be interesting
and useful as a basis for development of an original classification of collaboration
platform users. Further we describe those relevant typologies.
4.1 Describing user typologies
Brandtzᴂg and Heim (2010). The study [5] is a descriptive one, though the list of
existing theories and research papers is given in one of its sections. The results of
online survey of 4 Norway social networks users were subjected to cluster analysis.
Sporadics visit social network from time to time, mainly to check if somebody
contacted them.
Lurkers is the largest group, they do not create any content, but consume and
spread the content created by other groups. They are also notable for a propensity
to time-killing.
Socializers use social networks to communicate, make new friends, comment on
photos of the old ones, post congratulation messages on walls etc.
Debaters are a more mature and educated version of socializers. Besides commu-
nication, less shallow than in the previous case, they are interested in consumption
and discussion of news and other information available in social networks.
12 A. Bezzubtseva et al.
Actives are engaged with all possible types of activity: communication, reading,
creating, watching, establishing groups.
Budak, Agrawal, Abbadi (2010). This paper [13] describes the three types of people
(presented in 2002 by Malcolm Gladwell [21]) in terms of graph theory in context of
modern online communities (especially blogs). The presence of those people, in
Gladwell’s opinion, is the main cause of the resounding popularity of some innova-
tions. Authors also introduce a new type (the Translators), which, along with the
Sellers, more than other groups influences idea spread and success.
Connectors are people who easily make friends and, thus, have a lot of them.
Mavens are very informed due to their curiosity and like to share their knowledge.
Salesmen, – it is natural for them to convince people and establish an emotional
contact with them.
Translators are “bridges” between different interest groups. They have the ability
to interpret ideas in a different way, so that more people could understand and ac-
cept them.
Li, Bernoff, Fiorentino, and Glass (2007) present another classification [25] with-
out theoretical basis. Groups were extracted with the help of cluster analysis of the
poll values.
Creators blog, publish video, maintain their own web-sites; usually belong to the
young generation.
Critics select and choose useful media content; typically older than the previous
group.
Collectors are known for their addiction to saving bookmarks on special services.
Joiners spend much time in social networks; the youngest group.
Spectators read blogs, watch video, listen to podcasts; main consumers of user-
generated content.
Inactives are not active in social services.
Nielsen (2006). In the study [7] it is assumed that active members of large communi-
ties are very few. No special mathematical instruments were used to develop the ty-
pology, although the author mentions that user activity follows Power law (in the Zipf
curve variant).
Lurkers (90%) are those who only consume.
Intermittent/sporadic contributors (9%) are those who contribute rarely, occasion-
ally.
Heavy contributors/active participants (1%) are responsible for up to 90% of
community materials.
Jepsen (2006). This is one of the few classifications [23] with a theoretical founda-
tion (Kozinetz, 1999) [22]). The members of Danish newsgroups were classified ac-
cording to mean and median survey values.
A Typology of Collaboration Platform Users 13
Tourists are not very interested in community content.
Minglers are sociable people, who prefer not to consume the site’s content, but to
communicate with other members.
Devotees are compared to minglers more interested in newsgroup materials than in
communication.
Insiders both communicate and consume information.
Golder and Donath (2004). This is one more descriptive study [24] which examined
16 unmoderated Usenet newsgroups. The taxonomy was built after in-depth analysis
of the message posting frequency and message content.
Celebrities are central community figures, contribute more than others.
Newbies are new members, which ask many questions and do not know how to act
and communicate appropriately.
Lurkers are those who read discussions, but do not take part in them.
Flamers, Trolls, Ranters – three subgroups, members of which are notable for their
negative behavior and love to conversation spoiling.
4.2 Comparing user typologies
Analysis of the mentioned typologies resulted in an assumption that, despite some
significant differences in social networking services, there is a universal set of user
types. Though, some sources claim that there could be no such a meta-typology [23],
when others [6] make attempts in developing one.
The resemblance of user types can be seen more clearly from table 1. Also some
insights could be provided by a formal concept lattice, derived from the table (fig. 1).
Rows of the table represent the user types described previously (objects), columns are
the relevant typologies (attributes). Similar classes were merged: thus, class Actives
of the table includes Actives (Brandtzaeg & Heim, 2010), Active participants (Niel-
sen, 2006), Insiders (Jepsen, 2006), and Celebrities (Golder & Donath, 2004).
Table 1. Formal context (types as objects, typologies as attributes)
Brandtzaeg Budak Golder &
Li et al. Nielsen Jepsen
& Heim et al. Donath
(2007) (2006) (2006)
(2010) (2010) (2004)
Inactives 1 0 1 0 1 0
Lurkers 1 0 1 1 1 1
Socializers 1 1 1 0 1 0
Debators 1 0 1 0 0 0
Actives 1 0 0 1 1 1
Salesmen 0 1 0 0 0 0
Translators 0 1 0 0 0 0
14 A. Bezzubtseva et al.
Collectors 0 0 1 0 0 0
Creators 0 1 1 1 0 0
Newbies 0 0 0 0 0 1
Negatives 0 0 0 0 0 1
Fig. 1. Formal concept lattice of user typologies (built in ConExp [24])
It can be assumed from the picture that the three general classes of users at the
bottom (Lurkers, Creators and Socializers) and, perhaps, two or three important, but
less general classes (concepts) above (Actives, Inactives, Debators) form a universal
classification of social networking service users. It can also be seen that three studies
introduced five original user classes (Negatives, Newbies, Collectors, Translators,
Salesmen), which are less likely to be found in a community. As for the typologies,
the one of Brandtzaeg & Heim (2010) appears to be the most common.
We built Duquenne-Guigues base for the context and selected the implications
with support greater than 4:
1. supp = 4, Actives ==> Lurkers;
2. supp = 3, Inactives ==> Lurkers Socializers;
3. supp = 3, Lurkers Socializers ==> Inactives;
4. supp = 2, Debators ==> Inactives Lurkers Socializers.
E.g., implication 1 can be read as “Each user typology which contains Actives also
contains Lurkers and it is valid in 4 cases out of 6”.
A Typology of Collaboration Platform Users 15
5 Typology construction and analysis
5.1 Data sample
We used data obtained in one of the projects [25] of the collaboration platform Witol-
ogy. It includes quantitative indicators of each of participants’ activity: the number of
generated ideas, the number of posted comments and the number of submitted evalua-
tions.There were also some other types of activities on the platform, but the men-
tioned ones are the most basic and easy to interpret.
The project administrators and moderators were not considered as a part of a
crowdsourcing community, so only 504 of all 519 registered platform users were
sampled.
5.2 Analysis
Initially we detected those participants, who never commented, evaluated or generat-
ed ideas. These 248 users were clearly not interested in the project (165 of them never
logged on the platform after the third day of its work); thus, they could be excluded
from the further analysis.
Then we used clustering algorithm (k-means [26]) to divide the sample based on
several parameters. The results of cluster analysis of 256 objects are presented in fig.1
(we used XLSTAT 2011 [27] for the analysis, and XLSTAT-3DPlot package for vis-
ualization).
Fig. 2. Sample clustering (the number of objects in each cluster is displayed to the left to the
color scale)
16 A. Bezzubtseva et al.
The first cluster (grey) represents the participants, who did not show much activity in
evaluation and commenting. Because of the difference in orders of numbers of created
ideas, comments and evaluations, the participants who seem to be prominent idea
generators (created more than 10 ideas) ended up in this group.
The second cluster (blue) differs from the first with slightly higher evaluation ac-
tivity of users. It can be assumed that those people were interested in project, but
lacked motivation for message posting. It is reasonable to merge a certain part of this
cluster with the previous one.
The third cluster (green) as a whole is hard to characterize. Its members are less
passive: they may skip idea generation or comment posting, but they always evaluate
something.
The fourth cluster (yellow) is not far from the previous one in terms of evaluation
activity, but the number of comments is quite different.
The last cluster (red) is the smallest one. It consists of four absolute project lead-
ers, who together with some of the yellow participants turned out to be winners or
winning ideas authors.
For greater classification veracity the obtained clusters were modified: some of
the grey, blue and green balls formed a new class of creators, the rest of the blue
joined the grey cluster; also, some minor rearrangements were made.
6 Results
Table 2 represents the resulting user types, their percentages, descriptions and equiva-
lents in other studies.
Table 2. Types of collaboration platform Witology participants
Number / % User types of
User type Description
of objects previous studies
Actives [5], mavens [13],
Outstanding users, active participants [7],
Celebrities 4 1%
champions. insiders [23], celebrities
[24]
Debators/socializers [5],
Those who comment connectors/salesmen
Debators 21 4%
and evaluate actively. [13], active participants
[7], minglers [23]
Idea generators. Could Mavens [13], creators
be divided into two [25], active partici-
Creators 20 4% groups: energetic crea- pants/sporadic contribu-
tors (6 users), who not tors [7], insid-
only create, and socio- ers/devotees [23]
pathic ones (14 users),
A Typology of Collaboration Platform Users 17
who comment or eval-
uate many times less.
Those who evaluate but Critics/spectators [25],
Critics 34 7% don’t meddle in discus- sporadic contributors [7],
sions. lurkers [24]
Sporadics/lurkers [5],
Those who rarely make spectators [25], lurkers
Tourists 177 35%
attempts to participate. [7], tourists [23], new-
bies/lurkers [24]
Sporadics/lurkers [5],
Those who do abso-
Inactives 248 49% inactives[25], lurkers [7],
lutely nothing.
tourists [23]
The developed typology and type percentages can be compared with two rather
general typologies from the top of the lattice (fig. 1). Table 3 shows how the six clas-
ses of this research correspond to their classes.
Table 3. Comparison of different typologies class percentages
Nielsen % Brandtzᴂg % This study %
Active partici- Celebrity
1% Actives 18% 5%
pants Debators
Sporadic con- Debators Creators
9% 36% 11%
tributors Socializers Critics
Lurkers Tourists
Lurkers 90% 46% 84%
Sporadics Inactives
Interestingly, the percentages in the obtained typology are very close to the ones
in Nielsen typology. Brandtzᴂg explains the discrepancy with the “90 – 9 – 1” rule by
a relatively low popularity of Norway social networks compared to YouTube or Wik-
ipedia and by smaller content creation barriers, but such an explanation is not likely to
be relevant for the given collaboration project. Nearly 90% of lurkers could be ac-
counted for by initially a small interest of participants to the work itself and a great
curiosity to a new for Russia phenomenon, crowdsourcing, as means of some compa-
ny’s growth and development. Other reasons may also take place, but it seems to be
difficult to identify them without several projects or platforms comparison.
7 Conclusions
During the process of literature exploration it appeared that there is no generally ac-
cepted SNS user classification or any specific collaboration platform participant ty-
18 A. Bezzubtseva et al.
pology. Based on the existing relevant typologies of social networks, blogs, news-
groups users by means of cluster analysis we developed an original collaboration
platform typology. The six classes are so far not expected to be suitable for other
crowdsourcing communities. The percentages of classes follow the rule “90 – 9 – 1”,
according to which only a minor part of the community is really active.
Thus, all the research objectives were mainly attained.
7.1 Future Work
The developed typology is far from being complete and final. Only a small sample of
one of the project was analyzed, while different projects data comparison is expected
to specify the classification greatly. Possible future work also includes the following:
Involving more diverse information on the project (e.g. logs, qualitative values of
user evaluations).
Using other methods (factor analysis, graph mining, mean analysis) of group detec-
tion or other clustering algorithms.
Finding special users (e. g. trolls, flamers, flooders [24]).
Developing a classification algorithm.
Testing connection between group membership and demographical factors (age,
sex) or psychological tests results.
Using special metrics to determine community health [14].
Judging by the number of possible work improvement directions it can be con-
cluded that this paper is only a small test sally into the investigation of collaboration
platform participants’ behavior, which describes only a static snapshot of one project
and does not claim to be indisputable and fundamental.
Acknowledgements. This work was partially done during the mutual research project
between Witology and Higher School of Economics (Project and studying group
“Data Mining algorithms for analysing Web forums of innovation projects
discussion”). We would like to thank Jonas Poelmans for his suggestions for
improving the paper.
References
1. Prediction Markets, http://wiki.witology.com/index.php/ Рынки_предсказаний (in Rus-
sian)
2. The Growth of Social Media: An Infographic, http://www.searchenginejournal.com/the-
growth-of-social-media-an-infographic/32788/
3. Kelsey, T.: Social Networking Spaces: From Facebook to Twitter and Everything In Be-
tween. Springer-Verlag, 2010.
4. Boyd, D. M., Ellison, N. B.: Social Network Sites: Definition, History, and Scholarship.
Journal of Computer-Mediated Communication, 13 (1) (2007)
(http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html)
A Typology of Collaboration Platform Users 19
5. Brandtzᴂg, P. B., Heim, J. A Typology of Social Networking Sites Users. Interna-tional
Journal of Web Based Communities 2011, 7 (1).
6. Brandtzᴂg, P. B.: Towards a unified media-user typology (MUT): a meta-analysis and re-
view of the research literature on media-user typologies. Computers in Human Behaviour,
26 (5), 2010.
7. Nielsen, J.: Participation Inequality: Encouraging More Users to Contribute. Jakob Niel-
sen’s Alertbox, 9 October 2006 (2006)
(http://www.useit.com/alertbox/participation_inequality.html)
8. Community vs. Social Network, http://lithosphere.lithium.com/t5/Building-Community-
the-Platform/Community-vs-Social-Network/ba-p/5283
9. Crowdsourcing, http://wiki.witology.com/index.php/Краудсорсинг
10. Witology, http://witology.com/en (in Russian)
11. Imaginatik, http://www.imaginatik.com/
12. BrightIdea, http://www.brightidea.com/
13. Open Innovation Crowdsourcing Examples, http://www.openinnovators.net/list-open-
innovation-crowdsourcing-examples/
14. Measuring Community Health for Online Communities. Community Health Index White
Paper. Lithium (2011) (http://pages.lithium.com/community-health-index.html)
15. Johnson, G. M., Kulpa, A.: Dimensions of online behavior: Toward a user typology. Cy-
berPsychology & Behavior, 10 (6) (2007)
16. Heim, J., Brandtzæg, P. B., Endestad, T., Kaare, B. H., Torgersen, L.: Children’s us-age of
media technologies and psychosocial factors. New Media & Society, 9(3) (2007)
17. Livingstone, S., Helsper, E.: Gradations in digital inclusion: Children, young people and
the digital divide. New Media & Society, 9(4) (2007)
18. Barnes, S. J., Bauer, H., Neumann, M., and Huber, F.: Segmenting cyberspace: A customer
typology for the Internet. European Journal of Marketing, 41(1) (2007)
19. Selwyn, N., Gorard, S., Furlong, J.: Whose Internet is it anyway? Exploring adults
(non)use of the internet in everyday life. European Journal of Communication, 20(1)
(2005)
20. Heim, J., Brandtzæg, P. B.: Patterns of Media Usage and the Non-Professional Users. In
Proc. of the SIGCHI Conference on Human factors in computing systems (CHI 2007), San
Jose, California, USA (2007)
21. Gladwell, M. The Tipping Point: How Little Things Can Make a Big Difference. — Back
Bay Books (2002)
22. Kozinets, R. V.: E-Tribalized Marketing? The Strategic Implications of Virtual Communi-
ties of Consumption. European Management Journal, 17 (3) (1999)
23. Angeletou, S., Rowe, M., Alani, H.: Modelling and Analysis of User Behaviour in Online
Communities. In.: Proc. of International Semantic Web Conf. (ISWC 2011), Bonn, Ger-
many (2011)
24. Concept Explorer, http://conexp.sourceforge.net/index.html
25. Sberbank-21, http://sberbank21.ru/
26. K-Means Clustering,
http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
27. XLSTAT, http://www.xlstat.com/en/