We are all Lurkers: Consuming Behaviors among Authors
and Readers in an Enterprise File-Sharing Service
Michael Muller, N. Sadat Shami, David R. Millen, and Jonathan Feinberg*
IBM Research
One Rogers Street, Cambridge, MA, USA 02142
{michael_muller, sadat, david_r_millen} @ us.ibm.com, * [email protected]
+1-617-693-4235
ABSTRACT
Most knowledge repositories focus on the role of knowledgecreators. In this paper, by contrast, we examined the work of
Lurkers in an enterprise file-sharing service, and we compared
their lurking behaviors to the lurking behaviors of users who
uploaded files (Uploaders), and users who contributed metadata
about files (Contributors). For comparability, we restricted our
analyses to the consuming behaviors that are common to the three
roles (Uploaders, Contributors, and Lurkers). Independent
principal components analysis showed highly similar seven-factor
solutions of lurking activities across all three roles, although the
relative emphases of those factors varied across roles. Uploaders
tended to view and download more groups of files, showed less
emphasis on searching for files, and tended to work directly with
the file-sharing application, unmediated by external applications.
Contributors showed the opposite pattern: more emphasis on
searching and responding to recommendations from other users,
often via a form of remote access. Lurkers’ lurking behaviors were
less intense, and showed little difference in emphases among the
lurker factors. We use these results, and the published research
literature, to motivate a research agenda for lurkers in social
media.
Categories and Subject Descriptors
H5.3. Group & Organizational Interfaces: CSCW.
General Terms
Human factors
Keywords
Collective intelligence, Lurker, Non-public participant, Social
software, Social media, File-sharing
1. INTRODUCTION
Most analyses of knowledge repositories focus on the role of
knowledge-creators. Users who do not contribute knowledge are
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists,
requires prior specific permission and/or a fee.
GROUP’10, November 7–10, 2010, Sanibel Island, Florida, USA.
Copyright 2010 ACM 978-1-4503-0387-3/10/11...$10.00.
usually termed "Lurkers," and are sometimes called "free-loaders"
because they take information from the shared resource, but do not
contribute anything in return [6, 8]. There are several reasons now
to question this perspective [2].
1
In most social software applications, lurkers are the modal class
of users [13, 18, 21]). By definition, lurkers consume the
information in the application. Of course, the more active, public
users also consume the shared information. Thus, the consuming
behaviors of lurkers are also part of the larger repertoire of users
who also create the information that is consumed by everyone. In
this sense, we are all lurkers, at least some of the time, in most of
the social systems that we use. Studies, designs, and evaluations
that benefit lurkers will also benefit the consuming behaviors of
other classes of users.
In the business research literature, recent reports by Li and Bernoff
[13], McDermott [14], and Porter [23] have highlighted the
commercial importance of users who do not create primary
content. When lurking behavior is measured properly, it can be a
resource for indirect contributions. A log of which resources are
consumed can provide evidence of the usefulness and perceived
value of each resource. A summary of search terms can give an
idea of what people are interested in. This kind of information can
help knowledge-creators to serve the needs of their readers, and
can help an organization to anticipate future needs.
Within CHI and CSCW, the work of Preece and colleagues [19,
20, 24], and of Tahakashi et al. [32], has shown the motivations
and subtle contributions of lurkers, or "non-public participants"
[19]. For example, some lurkers decline to contribute for altruistic
reasons, preferring not to clutter an already-filled shared
information space. Lurkers may also feel that they lack the
standing or authority to make contributions [2, 11, 19, 26, 31].
In a partially-convergent research programme, Takahashi et al.
explored how “active lurkers” first find information from the
shared resource, and then carry the information outside of the
shared resource and application, to share it with other, indirect
users [32]. In these ways, lurkers add to the effectiveness and the
“reach” of the knowledge-creators and of the shared repository as-
1
Jonathan Feinberg is now at Google.
Figure 1. One aspect of the Cattail user interface, showing the files related to a particular user. (A) List of files uploaded by the
user. (B) One file has been selected for a more detailed view, or for download. (C) More detailed information about the selected
file. (D) A menu of other possible actions. (E) Link to a view of files shared to the user from other users. (F) Link to files that
have been shared from the user to other users. (G) Link to recent events in the user’s social network. (H) Link to a list of all
public files (which can be viewed in order of popularity or recency). (I) Link to collections with which the user is associated. (J)
Ability to share a file to other user(s). (K) Ability to add a file to a collection. (L) Ability to search all accessible files (public
plus private files on which the user has permission) by tag or content. We show fictitious names and picture to preserve
employee privacy.
a-whole, through increasing the number of people who are
influenced by the information in the shared resource.
analysis of variance. We close with a proposed research agenda to
continue the study of lurkers.
However, Lurkers have received relatively little quantitative study
(for exceptions, see [2, 27, 29, 31]. Lurkers have remained largely
“silent” [28] and “invisible” [31], because data collection in most
production systems focuses on the activities of informationcreators. It is possible to “instrument” a system so that it records
details of both creation (e.g., upload, modify, etc.) and
consumption (e.g., view, download, search). We report on
findings from that kind of instrumentation.
1.1 The Cattail File-Sharing Service: A Site to
Study Lurking
This paper builds on previous research through a quantitative
comparison of the usage patterns of lurkers and other user roles, in
an enterprise file-sharing service called Cattail [16, 17]. We use
these results to motivate a research agenda focused on lurkers and
lurking behaviors in social software. Our agenda proposes a new
direction, in distinction to previous proposals [2, 19, 20, 24, 27,
32]. Rather than assuming that lurkers will eventually become
more public participants (e.g., [13, 25, 29, 30, 31]), we focus on
lurkers as lurkers in organizations (e.g., [2, 11, 27, 28]. We
consider their needs as a distinct group of users, some of whom
may become experts in their role as lurkers.
In the remainder of this paper, we introduce the Cattail FileSharing Service, focusing on the features that are relevant to the
analyses in this paper. We then develop research questions, and
answer them through a principal components analysis and an
We examined the activities of lurkers in a research prototype
enterprise file-sharing system. Unlike most production services,
this prototype had been instrumented to capture not only the
conventional production events (upload, comment, tag, etc.) but
also consumption events (download, view, search).
The Cattail file-sharing system is a centralized service that allows
users within an enterprise to share files, to create metadata about
those files, to search for files, and to download files (Figure 1)
[16, 17]. More specifically, users may engage in the following
types of activities:
•
Upload a file
•
Contribute metadata on a file:
•
o
Comment on a file
o
Share a file (recommend a file to another user)
o
Create a named collection of files, and add a file to a
collection
Make use of a file or metadata about the file (consume)
o
Download a file
o
View metadata about a file
Figure 2. Detail of sharing information about one file. (M and N) List of people a file has been shared with. A full blue circle
beside a user’s name denotes the current version of the file was downloaded while a half full blue circle denotes a previous version
of the file was downloaded. (P) List of people that have downloaded the file, even though it was not shared with them. (Q)
Information about which collections the file has been added to. A single file may be included in zero, one, or many collections.
We show fictitious names to preserve employee privacy.
o
View metadata about another user
o
Search for files through keyword search or by clicking a
tag in a tag cloud
A file in Cattail may be classified as public or private. A public
file may be viewed or downloaded by any user. A private file may
only be viewed or downloaded by users who have access to that
file. Access is automatic for the person who Uploads a file. For
private files, access can be granted to other users through the
action of Sharing the file to named users (the user operation of
sharing performs two system operations: [a] it adds the shared-to
user to the permissions list for the file, and [b] it notifies the
shared-to user that s/he has access to the file, optionally with an
informal message from the sharer). A view onto the sharing data
for a file is shown in Figure 2. Any user with access to a file may
share that file to any other user.
i.e., activities that do not contribute files to the shared repository,
and that do not create visible metadata about those files. By
contrast, in this report, we take a closer look at consuming
behaviors of all users in Cattail.
Following the research and commercial insights of [12, 13, 14, 23,
27, 28, 33, 34], we compared the consuming activities of three
categories of users of Cattail (Table 1):
•
Lurkers never deliberately add information to the database,
but they do engage in traditionally “non-public” actions [19]
such as downloading files, viewing metadata about files,
viewing lists of files, viewing information about other users,
and searching for files. In the language of “producing” and
“consuming,” Lurkers engage only in consuming behaviors.
•
•
Uploading one or more files, and publicizing them to other
users
•
Annotating a file and monitoring it for actions by other users
Contributors do not upload files, but they do create
metadata about files through actions such as commenting,
sharing to specific other users, adding files to named
collections of files, and adding tags to files. Contributors
thus produce metadata about files. Contributors may also
perform the same actions as Lurkers – i.e., Contributors also
consume files and metadata about files.
•
Finding files that had been uploaded by others, and telling
additional users about those files
•
Table 1. Summary of behaviors in three self-selected user
roles.
Refinding one's own files
An earlier report [17] provided an overall analysis of the pattern of
activities in Cattail, including the following four activity patterns:
Previous work has also examined the use of collections of files in
Cattail [16], highlighting the work of creators of those collections,
described as "information curators."
Those two reports tended to focus on the activities of file creation
and discussion (see also [12, 27, 28, 33, 34] for other studies of
file-sharing systems). As with many studies of social media, we
paid little attention to the less visible activities of consuming --
Download
Share
Collect
Annotate
Tag
Upload
Lurkers
Contributors
Uploaders
9
9
9
9
9
9
9
9
9
9
9
9
Table 2. Lurking behaviors in the analyses.
•
Variable
Explanation
Download
The user downloads a file
Download-remote
The user downloads a file via a remote access (feed, blog, or post)
ViewFile
The views metadata about a file
ViewFile-remote-feed
The user obtains metadata about a file via a remote access through a feed
ViewFile-remote-blog
The user obtains metadata about a file via a remote access through a blog
ViewFile-Share-Email
The user is notified via email about a shared file, and follows a link to view the file’s metadata
ViewFile-Version-Email
The user is notified via email about a new version of a file in which s/he has an interest, and follows
a link in the email to view the file’s metadata
ViewHome-Date
View the user’s files in recency order
ViewHome-Type
View the user’s files in order of type of document (e.g., pdf, text, slides…)
ViewInbox
View files shared to the user
ViewOutbox
View files shared from the user
ViewDyadSharedToMe
View files shared to the user from a particular other user
ViewDyadShared FromMe
View files shared from the user to a particular other user
ViewOneCollection
View the files in a selected collection of files
ViewAllCollections
View the names of all collections to which the user has access
ViewPerson
View the files associated with another user
ViewPublic
View a list of all files that have public access
Search
Search the files by tag and/or by content
Uploaders create files in the service through upload
operations. Uploaders thus produce primary content and,
optionally, metadata. Uploaders may also consume – i.e.,
Uploaders may perform the same actions as Contributors and
Lurkers.
2. METHOD, RESEARCH
AND HYPOTHESES
QUESTION,
Our dataset consisted of user actions in the Cattail enterprise filesharing service during the period 26 November 2008 through 5
June 2009 (n=133708 users). There were 17982 (13.5%)
Uploaders, 19103 (14.3%) Contributors, and 96623 (72.2%)
Lurkers. These categorizations proceed from the broadest to the
narrowest sets of behaviors. Thus, most of the 13.5% of Uploaders
also produced metadata and also consumed files and metadata.
Most of the 14.3% of Contributors also consumed, but none of the
people in the Contributors category performed any Uploads.
Finally, the 72.2% of Lurkers engaged only in consuming
behaviors – i.e., Lurkers performed no Uploading or Contributing.
2.1 Research Strategy
In this paper, we wanted to compare Uploaders, Contributors, and
Lurkers on a core set of behaviors that are common to people in all
three categories. We therefore excluded from our analysis the
unique behaviors of Uploading and Contributing, because Lurkers
(of course) perform none of those behaviors. We restricted our
analysis to consuming behaviors only, because people in all three
categories were likely to consume from time to time. Our
question thus becomes: Do users in different categories engage in
different consuming behaviors? This narrowing of focus led to a
set of 18 consuming activities in our study (see Table 2). Using a
principal components analysis, we asked:
Research Question 1: What are the patterns of consuming
behaviors?
It seems likely that Uploaders engage in consuming behaviors in
support of their uploading activities (e.g., determining which files
are available, and then uploading new files to augment the set of
shared information with new content). It also seems likely that
Contributors engage in consuming behaviors in support of their
contributing (e.g., finding files to collect, share/recommend,
annotate on, and/or tag).
Therefore, using the principal
components analysis and an analysis of variance on the factors
scores from that principal components analysis, we examined the
following hypotheses:
Hypothesis 1: It seems likely that there will be different
overall levels of consuming behaviors across the three
different user roles of Uploaders vs. Contributors vs. Lurkers.
Hypothesis 2: It seems likely that there will be different
patterns of behaviors (distinct configurations of consuming
actions, as compared with one another) across the three
different user roles.
A Note on Statistical Independence. One anonymous reviewer
asked if the three role-based groups (Uploaders, Contributors,
Lurkers) were really independent. Formally, the answer is “yes,”
because each user appeared in only one of the three groups. The
anonymous reviewer suggested that the occurrence of similar
behaviors among all three groups rendered them non-independent.
Our answer is that this is exactly our analytic strategy: To compare
the patterns and extents of those common behaviors as
distinguished by the role that each user chose to adopt.
The goal of the principal components analysis is to provide a
statistical description of which consuming behaviors are highly
inter-correlated. Each resulting factor represents behaviors that
tend to co-occur. Each of these factors, then, may be considered
as an underlying driver, motivation, or strategy of the observed
consuming behaviors.
In brief, all three principal components analyses led to the same
set of seven factors (using the criterion of eigenvalue > 1.0). The
seven factors were as follows:
•
Factor 1: Search + View summary metadata about a
file + Download. In the “core work” of consuming, the
user searches for information, examines a description of each
search result, and downloads selectively the items that satisfy
her/his search.
•
Factor 2: Browse lists of Collections of Files + View
the contents of a single Collection. Users focus on
collections of files as a means of finding desired information.
•
Factor 3: View files shared/recommended by
particular other users. Users respond to active social
recommending, in which the consuming user receives a
personally-targeted recommendations from a particular other
user (i.e., from a Contributor or an Uploader3).
•
Factor 4: View files shared/recommended as a
group (involving multiple users). Factor 3 occurred
within specific pairs of users – i.e., within the context of a
particular dyadic relationship. By contrast, the grouporiented Factor 4 involves sharing/recommendations by
potentially large numbers of other users.
•
Factor 5: Respond to emails that shared/recommended a file. Users follow the instructions or proposals
of other users, acting directly from an email message (i.e.,
working from email rather than from the Cattail user
interface).4
•
Factor 6: View information about another user and
her/his actions. Users browse the attributes and filesharing behaviors of other users, as reflected in the actions of
those users that appear in a summary of activity in the user’s
social network (see also [7]).
•
Factor 7: View files via a remote access (i.e.,
through an API). The user performs direct file-access from
3. RESULTS
We restricted our analysis to consider only consuming behaviors.
That way we could make direct comparisons among the user roles
in the activities that all three roles engaged in.
3.1 Amount of Activity
Using an ANOVA, we found that Uploaders were most active in
their consuming behaviors, followed by Contributors, and finally
Lurkers (p<.001 for 17 of the 18 variables). 2 Hypothesis 1 was
supported.
3.2 Understanding Lurkers and Lurking
Hypothesis 2 predicted that the consuming behaviors of Uploaders
might differ in their patterns from the consuming behaviors of
Contributors, and that both might differ again from the consuming
behaviors of Lurkers. To test this hypothesis, we began a detailed
investigation into Research Question 1, i.e., the question of what
patterns of consuming behavior occur in file-sharing. We
conducted three separate principal components analyses on the
consuming behaviors within three subsets of the data. One
principal components analysis used consumption data from the
17982 Uploaders. A second principal components analysis used
consumption data from the 19103 Contributors. A third principal
components analysis used consumption data from the 96623
Lurkers. These analyses examined the 18 lurking-behavior
variables in Table 2.
2
For the 18 consuming behaviors, factors, the F(2,133705) tests were
as follows:
Download, F=1043; Download-remote, F=32;
ViewFile, F=5730; ViewFile-remote-feed, F=88; ViewFileremote-blog, F=1005; ViewFile-Share-Email, F=3293; ViewFileVersion-Email, F=1861; ViewHome-Date, F=4930; ViewHomeType, F=386; ViewInbox, F=5552; ViewOutbox, F=7072;
ViewDyadSharedToMe, F=2263; ViewDyadSharedFromMe,
F=2622; ViewOneCollection, F=6049; ViewAllCollections,
F=6317; ViewPerson, F=10888; ViewPublic, F=79; Search,
F=1175. All F tests were significant at p<.001 or better. With the
exception of Download-remote, all pairwise comparisons were
significant at p<.025 or better by Tukey HSD test.
3
4
By definition, a Lurker cannot create a recommendation.
It could be argued that direct response to an email is outside the
scope of a social file-sharing system, because it is substantially the
same operation as file-transmission via email. We include this
behavior in our analysis because (a) it is social (one user recommends or assigns a file to another), and (b) it is an act of consuming material that was initiated by another user. Note that the
conclusions of the paper do not rest on this replacement-of-email
behavior, but are based on the entire set of consuming behaviors.
Figure 3. Diagrammatic summary of the loadings in the principal components analyses of consuming behaviors. Following a Varimax
rotation, we considered any factor loading greater than .490. The long purple rectangles show the high-loading factors for the pooled
data from all three user roles. Within each rectangle, the leftmost green squares summarize the high-loading factors for the Uploaders.
The middle orange squares summarize the high-loading factors for the Contributors. The right-most blue squares summarize the highloading factors for the Lurkers. Note the strong similarities in the factor patterns across the three different user roles.
(Technical details: The seven factors in the Uploaders analysis accounted for 66.00% of the variance. The seven factors in the
Contributors analysis accounted for 64.40% of the variance. The seven factors in the Lurkers analysis accounted for 65.96% of the
variance. The seven factors in the pooled analysis accounted for 66.19% of the variance.)
a separate application. This factor is similar to Factor 5
above, insofar as the user can see relatively little descriptive
information about the file, but simply downloads it on the
basis of the context in which its link appears (e.g., a blog).
•
Contributors tended to rely more on the actions of other
people while searching, as shown by their use of Collections
and viewing activities in their social net-work, combined in
the same factor as Search (Factor 1). Thus, Contributing is a
social activity both in intent to reach an audience, and also in
deciding which files to contribute to.
•
Lurkers appeared to engage more in social search, -- i.e.,
person-centric searches (rather than data-centric searches), as
shown by the inclusion of Search, in Factor 6, which is generally concerned with viewing information about another user.
Lurkers also showed tended to use date/time-based views in
association with browsing through collections of files.
Figure 3 presents a diagrammatic summary of the loadings of the
consuming factors in the three analyses.
3.3 Do People Lurk Differently in Different
Roles?
We observed differences across the three user roles among the
common patterns of consuming from the principal components
analysis. These differences appear to reflect the fundamentally
social nature of the use of this social file-sharing system:
•
In comparison to Lurkers, Uploaders had a slightly greater
tendency to use Search, to view summary metadata about a
file, and to view profile data about another user.
The principal components analyses suggested differences in factor
patterns across the three user roles. We used an analysis of
variance (ANOVA) to clarify these differences.
Because the principal components analysis outcomes were so
similar among the three groups of users (Figure 3), we pooled the
data from all user roles into a single, omnibus principal
components analysis, resulting in the same core factor structure
that we described above (see the larger, rectangles in Figure 3).
We calculated a factor score for each user on each of the seven
factor scores. We then compared the three user roles using a
mixed-model repeated-measures ANOVA on the three user roles
and the seven factor scores.
All effects were significant. Uploaders had highest scores on each
of the seven factors, followed by Contributors, and finally Lurkers
(F(2,133705)=14543.015, p<.001, all pair-wise differences significant
at p<.001 by Tukey HSD). Factors differed from one another
(F(6,802230)=136.582, p<.001, all pair-wise differences significant at
p<.002).
More importantly, there was a significant Roles x Factors
interaction (F(12,802230)=466.007, p<.001, pair-wise differ-ences
shown by standard error bars in Figure 4). Lurkers showed little
differentiation among their factor scores. By contrast, Uploaders
showed relatively high use of Factors 2 and 4 (use of Collections and browsing Groups of files), and relatively low use of
Factors 1 and 7 (Search+view+download and View-viaremote-access). Contributors showed a very different pattern
from Uploaders, with relatively low use of Factors 2 and 4, and
relatively high use of Factors 1 and 7 and also Factor 5 (respond to
Email recommendations). It appears that Uploaders make
their decisions to add new files based on groups of files (e.g.,
Collections), whereas Contributors make their decisions about
sharing, collecting, commenting, tagging, etc., more socially, -i.e., in response to the recommendations of others. These patterns
suggest that Uploaders may be less aware of an audience for their
actions, as contrasted with Contributors. Overall, these results are
in agreement with the qualitative finding that some users act as
“curators” of Collections of files, preparing those Collections for
use by colleagues [16]; see also the “Upload and Publicize” factor
in [17], in which curators promote their own uploaded content to
other users.
By contrast, Contributors appear to be more reactive to the work
of others. Of course, a Contributor is, by definition, in a
somewhat reactive role, because s/he needs to find files in order to
Contribute metadata to them (i.e., through the operations of Share,
Collect, Annotate, and Tag). However, there is evidence that
some people who act in the “curator” role of [16] are working
primarily with files that were Uploaded by other users, as shown
by the “Discover and Tell” factor in [17]. More generally, Li and
Bernoff described actions of rating and commenting as distinct
roles among users who do upload their own content [13].
Therefore, we might have expected Contributors to take more
independent actions, with greater emphasis on searching, rather
than on receiving recommendations from others. We will need to
conduct further research to understand whether Contributors
should be considered a single role, or whether there are distinct
sub-patterns that distinguish different forms of Contributing.
Figure 4. Factor scores for each user role:
Means and standard errors.
3.4 Summary of Results
The principal components analysis provided a quantitative
description of the work of a large sample of consumption behavior
across three user roles. We showed seven distinct factors that
underlie the 18 observable consuming behaviors. Research
Question 1 asked what patterns of consuming behavior occur
among the users of the file-sharing service. The convergent
principal components analyses showed seven strong patterns of
usage, including a core path of actions leading to a download
operation, plus four distinct strategies for viewing files and the
social data that describe those files, and two factors related to
remote notification and remote access.
Hypothesis 1 predicted that there would be different overall levels
of activity across the three different user roles. We showed that
this prediction was supported through ANOVAs on 17 of 18
consuming behaviors and all seven factors, and again in the
analysis of variance based on factor scores (Figure 4).
Hypothesis 2 predicted that there would be different patterns of
activity across the three different user roles, independently of the
overall levels of activity that we had predicted in Hypothesis 1.
The principal components analysis (Figure 3) and the ANOVA
based on factor scores (Figure 4) showed strong contrasts in the
ways in which the three user roles used various features of the filesharing service. In contrast with Lurkers, Uploaders used more
groups of files, and Contributors used more social
recommendations based on individual files.
3.5 Limitations
We only examined one system, with one core type of shared
resource, inside of one enterprise. It will be interesting to see if
the same patterns obtain in other social media, such as socialnetworking, and social-tagging, and in the sharing of other types
of media. In addition, we will be interested to see if similar
patterns occur in other organizational settings.
4. DISCUSSION &DESIGN IMPLICATIONS
4.4 Toward a Lurker Research Agenda
Users in different roles engage in the same overall structure of
consuming behaviors, as shown in the principal components
analysis (Figure 3). However, each role is associated with a
distinct pattern of relative emphasis among those factors (Figure
4). These observations suggest several design decisions.
We use these results as a starting point for sketching a research
agenda concerning the role of lurkers in file-sharing and other
social applications.
4.1 Importance of Consumption Data from All
Roles
As we noted at the beginning of this paper, most social media
systems are not instrumented to provide consumption data for
analysis (i.e., data about viewing information or about consuming
information, for example downloading). Without such data, the
work of lurkers will remain largely invisible, and the consumption
behaviors in other roles will also be under-studied. We hope that
the results in this paper will encourage others to collect such data
(see also [21]). Part of our current work involves tools to make
this kind of data-recording easier.
4.2 Feature Sets to Support each User Role
We have described evidence of several distinct consuming strategies. Uploaders tend to examine collections and groups of files,
while Contributors tend to focus more on individual files. These
results suggest different feature sets to support these different
strategies. As suggested earlier [16], people who create collections
of files may need better tools to describe and promote their
collections.
4.3 Unified User Interface
If there are different usage patterns and strategies among the three
user roles, should there be different user interfaces for users in
each role? We recommend against this design choice, because of
the overlap in behaviors across the user roles. Among the Uploaders, 78% also Shared, 40% also Collected, 13% also Annotated,
and 23% also Tagged. Thus, large percentages of Uploaders also
engaged in the behaviors of Contributing. Similarly, among the
Uploaders and Contributors, more than 94% viewed files in one
way or another, more than 60% viewed files via a “home” view,
more than 22% viewed a list of files recommended to them, and
more than 21% used the Collections-oriented views. Thus, large
percentages of Uploaders and Contributors engaged in the
behaviors that were also exhibited by Lurkers.
The three user roles should therefore be seen as subsets of one
another, in which Uploaders make use of nearly all of the features
of Cattail, Contributors make use of many of the features used by
Uploaders, with greater emphasis on the social features, and
Lurkers make use of a number of the features used by both
Uploaders and Contributors. Providing separate user interfaces for
each role would require Uploaders to shift among three systems,
and would require Contributors to shift among two systems. The
use of separate systems might discourage people from changing
roles, which could ultimately reduce the number of Lurkers who
decided to participate more fully in the role of Contributor or
Uploader (see “adoption models,” below).
4.4.1
Lurking as the Modal User Role
First, we note that, while users in all roles engaged in some
amount of consuming behavior, fully 72% of Cattail users did
nothing other than consume (i.e., the Lurkers). This pattern is not
unusual in social media (e.g., [13, 18, 21]). Thus, Lurkers in
Cattail and in many other social applications are the modal users.
However, most design decisions are motivated by the needs of the
more active, “public” user roles (Uploaders and Contributors),
even though they constitute a minority of the user population.
The results in this paper show that there is structure and (by
inference) purpose in consumption behavior inherent in each of
the different user roles, including Lurkers.
A design decision that is responsive to the needs of lurkers has the
potential to benefit 100% of the users, because all users consume
at least some of the time. By contrast, for Cattail, a design
decision that is responsive to the needs of Uploaders or
Contributors will provide direct benefits to at best 27% of the
users of Cattail (although the Lurkers may of course derive
secondary benefit from anything that improves the use of
Uploaders and Contributors). Thus, studies of the activities and
needs of Lurkers have the potential to achieve the numerically
greatest impact. In consequence, we propose to extend earlier
work [2, 24, 25, 32] into a new research agenda on lurkers.
4.4.2
What are the goals & motivations of lurkers?
As our principal components analyses showed, the consuming
activity of Lurkers is not a monolithic set of behaviors. Different
people engage in up to seven distinct patterns (factors) of
consuming, and more than 96% of the users engaged in two or
more of the seven patterns of consuming shown in the principal
components analysis.
Lurking can be a major component of a user’s work in an
organization. One example is a customer-care agent, who digests
information provided by researchers and technologists in order to
provide quality customer service (e.g., [32]). A second example is
a corporate reference librarian, who becomes familiar with diverse
literatures in order to assist clients in searches. A third example is
a senior sponsor of online groups, who may quietly observe the
activity of each team, harvesting insights for knowledge
management – a form of “executive lurking.”5
We propose that Lurker-ethnographies be conducted, not only for
file-sharing, but for other systems that allow users to consume the
shared resources. Studies on the motivations for lurking [2, 21,
24] provide a good starting point. New ethnographies should go
further, to include organizational roles as well as motivations.
5
We thank Elizabeth Daly for a discussion leading to this insight.
4.4.3 How do lurkers use the information that they
obtain through lurking activities?
Takahashi et al [32] reported that lurkers often share information
outside of the system through which they obtained it. How do
Lurkers choose which resources to share with others, external to
the shared resource database? Do they approach the service with
that purpose, or do they discover resources during the course of
other activities, and then recognize the potential usefulness of
those resources to other people who are not users of the original
service and resource? What are their strategies in selecting
resources? What are their strategies in sharing resources?
4.4.4
Are there adoption models for Lurkers?
Organizations often want to know how information and influence
flow through their departments. Consuming is a key measure of
both flow and value [2, 26], if we assume (per rational choice
theory [22]) that employees make good decisions about what
information to view or consume. Organizations can then construct
“business hypotheses” that information should flow from
Uploaders in one department, and be used by Lurkers in another
department, and then organizations can test those hypotheses by
examining patterns of Uploading and Lurking in the relevant
departments. Organizations may also want to communicate
certain information to selected groups of employees, based on the
information that those employees are consuming.
Much research that considers Lurkers makes the assumption that
they will -- or should -- become public participants. The sociotechnographic ladder of the Groundswell project suggests a progresssion that looks like a developmental or maturational model,
going from inactive to lurking to contributing metadata to creating core content [13]. Similar trajectories have been suggested in
the “readers to leaders” approach of Preece and Shneiderman [25],
and in the “de-lurking” analyses of Rafaeli et al. [29, 31]. Others
have suggested that the Lave and Wenger model of legitimate
peripheral participation [11] could be used to explain the
incremental development of a Lurker into a Contributor, and
eventually into an Uploader [2, 3, 21, 23, 29, 30, 31].
5. CONCLUSION
Does every Lurker share the goal of becoming an Uploader? Or
are there separate and distinct developmental/maturational paths
for Uploaders, for Contributors, and for Lurkers (i.e., as suggested
in [2])? If there is evidence for a continuing separation among the
three user roles, what organizational factors would account for
such separate user roles? What tools would enhance performance
and/or development for each of these models?
Third, we have used those seven factors to contrast the consuming
behaviors within the three user roles in file-sharing. We have
shown differences in those patterns of behavior, and we have
proposed differences in strategies that could account for those
different patterns. These results contribute to the on-going
discussion of Lurkers’ goals and strategies, and of the subtle ways
in which Lurkers serve their organizations [2, 19, 20, 24, 25, 32],
as well as more general discussions of how people use the data of
others to create new forms of value [5, 11, 14, 28]. We have also
translated those patterns into recommendations for design.
4.4.5
Communities of practice for Lurkers?
How will the silent find a voice (e.g., [1, 15])? How will Lurkers’
needs become known in organizations? One solution is to survey
the Lurkers (e.g., [19, 20, 32]). A second possibility is to help the
Lurkers to self-organize as a community of people with common
needs. Communities of practice often support a developmental
path in which a member begins with small, simple actions as a
newcomer, and gradually deepens her or his practice (and service
to the community) by taking on more challenging tasks [4, 11].
What would the developmental path look like for a creators'
(Uploaders’) community? A Contributors' community? A
Lurkers' community? How do a "master Uploader" and a "master
Lurker" differ from one another? How would these communities
of practice interact with one another?
4.4.6
How can organizations leverage lurker data?
One of the commercial uses of information about lurkers takes the
form of "attention data" [5, 9, 10] – i.e., relating a particular set of
needs or interests to a particular market segment of users. What
are the analogous opportunities within educational institutions, or
within commercial enterprises, governments, and non-profits?
We believe that this paper makes four contributions:
First, we have provided direct quantitative comparisons among
three user roles in a large enterprise social file-sharing system,
namely Uploaders, Contributors, and Lurkers. These analyses
support and inform earlier work to understand user roles in social
software [13, 14, 16, 19, 20, 23, 25].
Second, we have made a detailed analysis of consuming behaviors
that are common to those three roles, showing seven factors that
are relatively consistent across different user roles. These patterns
can be tested for generality in other forms of social software.
Finally, we have used our results as a point of departure for a
Lurker research agenda, continuing the discussion begun in earlier
proposals (e.g., [2, 24, 25]). A better understanding of how
Lurkers serve the needs of colleagues and organizations will be
important in meeting the needs of individuals and organizations
[13, 14, 19, 20, 23, 24, 25, 32].
Preece and colleagues [19, 20, 24, 25] and Takahashi et al. [32]
have shown the motivations and benefits of Lurking. Li and
Bernoff [13], McDermott [14], and Porter [23] have highlighted
the commercial importance of Lurkers. Now that we know that
Uploaders, Contributors, and Lurkers engage in similar core
patterns of consuming behaviors, we can see Lurking as a set of
positions along the same behavioral axes as Uploading and
Contributing. Research focused on Lurkers and Lurking can help
to clarify the activities of all users, because similar patterns of
consuming behaviors appear in all user roles. We are all Lurkers.
6. REFERENCES6
1.
Alcoff, L.M., ‘The problem of speaking for others,’ Cultural
Critique (winter 1991-92), 5-32.
2.
Antin, J., & Cheshire, C., ‘Readers are not free-riders:
Reading as a form of participation on Wikipedia,’ Proc
CSCW 2010.
3.
Bishop, J., ‘Increasing participation in online communities: A
framework for human-computer interaction,’ Computers in
Human Behavior, vol 23, no 4, 1881-1893, 2007.
4.
Bogdan, C., & Bowers, J., ‘Tuning in: Challenging design for
communities through a field study of radio amateurs,’ Proc
Communities & Technologies 2007, Springer, 439-461.
5.
Chi, E.H., ‘Information seeking can be social,’ Computer, vol
42, no 3, 42–46, 2009.
6.
Curien, N., Fauchart, E., Laffond, G. & Moreau, F. (2006).
‘Online consumer communities: Escaping the tragedy of the
digital commons.’ In E. Brousseau (Ed)., Internet and Digital
Economics. Cambridge: Cambridge University Press, 2006.
7.
DiMicco, J., Millen, D.R., Geyer, W.G., Dugan, C.
Brownholtz, B., & Muller, M. (2008). ‘Motivations for
social networking at work.’ Proc CSCW 2008.
8.
9.
Kollock, P., & Smith, M. (1996). ‘Managing the virtual
commons: Cooperation and conflict in computer
communities.’ In S. Herring (ed.), Computer-Mediated
Communications: Linguistic, Social, & Cross-Cultural
Perspectives. Amsterdam: Benjamins (1996), 109-128.
Korth, A., Hirsch, B., & Plumbaum, T., ‘A trilogy of webs
for machines,’ http://www.dai-labor.de/index.php?
id=1034&pubID=399, 2008.
10. Lantham, R.A., The economics of attention: Style and
substance in the age of information, University of Chicago
Press, 2006.
11. Lave, J., & Wenger, E., Situated learning: Legitimate
peripheral participation, Cambridge University Press, 1991.
12. Lee, J. (2003). ‘An end-user perspective on file-sharing
systems.’ Communications of the ACM, vol. 46, no. 2,
February 2003, 49-53.
13. Li, C., & Bernoff, J., Groundswell: Winning in a world
transformed by social technologies, Harvard Business School
Press, 2008.
18. Nielsen, J., ‘Participation inequality: Encouraging more users
to contribute,’ http://www.useit.com/alertbox/ participation_
inequality.html, 2006.
19. Nonnecke, B., Andrews, A., & Preece, J., ‘Non-public and
public online community participation: Needs, attitudes, and
behavior. Electronic Commerce Research vol 6, no 1, 7-20,
2006.
20. Nonnecke, B., & Preece, J. (2001). ‘Lurker demographics:
Counting the silent.’ Proc CHI 2000, ACM Press, Den
Hague, Nederlands, April 2000, 73-80.
21. Panciera, K., Priedhorsky, R., Erickson, T., & Terveen, L.,
‘Lurking? Cyclopaths? A quantitative lifecycle analysis of
user behavior in a geowiki,’ Proc CHI 2010.
22. Pirolli, P., Information Foraging Theory: Adaptive
Interaction with Information. New York: Oxford, 2007.
23. Porter, J., Designing for the social web, New Riders Press,
2008.
24. Preece, J., Nonnecke, B., Andrews, D., ‘The top 5 reasons for
lurking: Improving community experiences for everyone,’
Comp. Human Beh., vol 2, no 1, 2004.
25. Preece J. and Shneiderman B., ‘The reader-to-leader
framework: Motivating technology-mediated social
participation,’ AIS Transactions on Human-Computer
Interaction, vol 1, no 1, 13-32, 2009.
26. Priedhorsky, R., Chen, J., Lam, S.K., Panciera, K., Terveen,
L., & Riedl, J., ‘Creating, destroying, and restoring value in
Wikipedia,’ Proc GROUP 2007.
27. Rader, E., ‘The effect of audience design on labeling,
organizing, and finding shared files,’ Proc CHI 2010.
28. Rader, E. ‘Yours, mine and (not) ours: Social influences on
group information repositories.’ Proc CHI 2009.
29. Rafaeli, S., Ravid, G., & Soroka, V., ‘De-Lurking in virtual
communities: A social communication network approach to
measuring the effects of social and cultural capital.’ Proc.
HICSS 2004.
30. Schultz, N., & Beach, B., ‘From lurkers to posters,’
Australian National Training Authority, http://pre2005.
flexiblelearning.net.au/resources/lurkerstoposters.pdf, 2004.
31. Soroka, V., & Rafaeli, S., ‘Invisible participants: Now
cultural capital relates to lurking behavior,’ Proc WWW 2006.
14. McDermott, M., ‘How to measure the success of your web
app,’ http://www.thinkvitamin.com/features/ webapps/ howto-measure-the-success-of-your-web-app.
32. Takahashi, M., Fujimoto, M., & Yamasaki, N.,’The active
lurker: Influence of an in-house online commu-nity on its
outside environment.’ Proc GROUP 2003,.
15. Muller, M.J., ‘Ethnocritical heuristics for reflecting on work
with users and other interested parties.’ In Kyng, M., &
Mathiessen, L., (eds.), Computers and design in context,
Cambridge MA USA: MIT Press, 1997.
33. Voida, S., Edwards, W.K., Newman, M.W., Grinter, R.E., &
Ducheneaut, N. ‘Share and share alike: Exploring the user
interface affordances of file sharing.’ Proc CHI 2006.
16. Muller, M., Millen, D.R., & Feinberg, J., ‘Information
curators in an enterprise file-sharing service.’ Proc. ECSCW
2009.
17. Muller, M., Millen, D.R., & Feinberg, J., ‘Patterns of usage
in an enterprise file-sharing service: Publicizing, discovering,
and telling the news,’ Proc CHI 2010.
6
All URLs were verified on 8 December 2009.
34. Whalen, T., Toms, E.G., & Blustein, J. (2008). ’Information
displays for managing shared files.’ Proc CHIMIT’08, ACM
Press, San Diego, CA, USA, November 2008.