[table]font=sf, position=top

# Communication networks and group effectiveness:
the case of English Wikipedia

Agnieszka Rychwalska<EMAIL_ADDRESS>Robert Zajonc Institute for Social
Studies, University of Warsaw, Poland  Szymon Talaga  Robert Zajonc Institute
for Social Studies, University of Warsaw, Poland  Karolina Ziembowicz
Kozminski University, Poland and Berkman-Klein Center for Internet and
Society, Harvard University, US  Dariusz Jemielniak

###### Abstract

A selection of intellectual goods produced by online communities – e.g. open
source software or knowledge bases like Wikipedia – are in daily use by a
broad audience, and thus their quality impacts the public at large. Yet, it is
still unclear what contributes to the effectiveness of such online peer
production systems: what conditions or social processes help them deliver
quality products. Specifically, while co-contribution (i.e. bipartite
networks) are often investigated in online collaboration, the role of
interpersonal communication in coordination of online peer-production is much
less investigated. To address this gap we have reconstructed networks of
personal communication (direct messaging) between Wikipedia editors gathered
in so called Wikiprojects – teams of contributors who focus on articles within
specific topical areas. We found that effective projects exchange larger
volume of direct messages and that their communication structure allows for
complex coordination: for sharing of information locally through selective
ties, and at the same time globally across the whole group. To verify how
these network measures relate to the subjective perception of importance of
group communication we conducted semi-structured interviews with members of
selected projects. Our interviewees used direct communication for providing
feedback, for maintaining close relations and for tapping on the social
capital of the Wikipedia community. Our results underscore the importance of
communication structure in online collaboration: online peer production
communities rely on interpersonal communication to coordinate their work and
to maintain high levels of engagement. Design of platforms for such
communities should allow for ample group level communication as well as for
one-on-one interactions.

##  1 Introduction

Arguably, individuals form groups to achieve more ambitious goals than they
could achieve alone [2, 17]. Yet, for groups to proverbially become more than
the sum of their parts, they need to organize into effective structures:
division of labor [46], structures of information flow and the structure of
interpersonal relations [1], as well as a well internalized organizational
culture are crucial for group performance.

Not surprisingly then, the structure of online collaboration teams and peer
production systems, such as Free/Open Source Software (FOSS) or Wikipedia,
have often been analyzed to pinpoint those structural properties that
determine the quality of group work. Bipartite, collaboration networks (e.g.
co-editing of Wikipedia articles or co-contributions to files in FOSS
projects) are the most likely targets of such analysis, as they show how
contributors interact with each other when performing the actual tasks. For
example, longer path lengths in co-edition networks of groups of editors on
English Wikipedia are related to lower performance of the group [42]. In FOSS
projects, contributors self-organize into subgroups working on similar subsets
of files in a bottom-up manner [40], by performing spontaneously self-chosen
and self-defined tasks [18], or self-select to tasks (e.g. modules) in a well-
defined hierarchy of labor division [34].

However, a long-standing line of research on group work in offline settings
shows that task-related structuring of the group is not enough for high
performance. Task-related group interaction is accompanied by affiliation-
related interaction [8, 9]. The task (instrumental) domain of group dynamics
drives the group to differentiate, while the affiliation processes (affective
domain) focus on integration and promote uniformity [9].

Affiliation processes are important not only for the wellbeing of group
members: group cohesion is positively related to performance [11, 22]. While
instrumental, task-related activities can be organized by procedures and
rules, social bonding within the group crucially relies on interpersonal
communication. In organizations, coordination of work often happens in
informal communication, alongside formal structures of management [13]. Thus,
the structure of interpersonal communication within online peer production
groups - specifically, its cohesiveness - should affect the groups’
performance.

In this work, we focus on communication using direct messages within
collaborating groups on the English Wikipedia. We used a mixed-methods
approach - quantitative social network analysis and qualitative analysis of
interviews - to analyze Wikipedia’s topic oriented editor groups:
Wikiprojects. By reconstructing networks of direct messages and interviewing
core members from selected projects we were able to elucidate how
interpersonal interaction, and specifically affiliation related communication,
impacts quality of group work. We show that high quality of group work is
related to communication networks with strong ties, which also exhibit
integrated connection structure, straying off the typical leader – followers,
star-like motifs. We also found that direct messaging is associated with
motivational messages and outreach, and with tapping on the group’s social
capital. Taken together, our results suggest that communication structures
that allow for complex integration of resources and knowledge distributed
across specialized contributors within the group are conducive to group
efficiency.

##  2 Background and related work

Quality of Wikipedia has been a topic in both public and academic discourse
since its inception [26, 48, 51]. On the one hand, its popularity combined
with lack of professional oversight over the content, raised concerns of
possible misinformation of the general public [29]. On the other hand, the
process of bottom up encyclopedic content creation raised interest in self-
organization possibilities afforded by new media [49]. Thus, understanding how
quality products emerge from bottom up social processes is both important for
the public at large as well as for theoretical advancement of systems
research.

With the growth of Wikipedia, and with refinement of its peer production
process, the understanding of self-organized quality control has been
accumulating [36, 42, 44]. Most importantly, Wikipedia’s peer review process
and quality scores awarded to articles provide an opportunity to quantify the
effects of distributed work and allow comparisons against a single scale of
quality ratings.

###  2.1 Quality in peer production

For the average reader of Wikipedia, the quality grades of Featured Article
(thereafter referred to as FA) or Good Article (GA) – a star or a plus sign,
respectively, displayed at the top of the page – might be unnoticeable, but
for Wikipedia editors they are cherished marks of accomplishment. Many editors
list the FA and GAs they contributed to on their Wikipedia profile pages. The
review process that awards those ratings is in many respects similar to
academic reviews: the criteria for each rank of article quality are listed
explicitly, and each submission is reviewed by independent editors whose
remarks are then incorporated into the article. The difference lies in that at
the end of the process, a discussion unfolds, and any editor can voice their
opinion on whether the article is worthy of a particular quality mark; the
ensuing decision is consensus
based111https://en.wikipedia.org/wiki/Wikipedia:Featured_article_review .The
universal criteria (well-known and applicable to all candidate articles) as
well as independent review process makes these quality ratings suitable for a
quality measurement of peer produced work.

The availability of such quality measures has spurred much investigation into
what constitutes quality work. Interestingly, quality criteria differ among
language versions of Wikipedia, pointing to possible differences in quality
perceptions across cultures [26]. On one hand, particular features of articles
that predict well how close they are to being awarded a higher quality grade,
can be identified. For example, classifiers trained on previously assessed
articles can tell how close an article is to a certain quality rank [58]. On
the other hand, the process of article production can also be investigated to
pinpoint those properties that enable some articles to improve in quality much
faster than others.

In standard organizations’ production process, quality is ensured, among
others, through a careful division of labor among employees with appropriate
expertise. In online peer production systems, contributors self-select to
tasks [14], and thus it has been suggested that composition of the editor base
impacts quality of Wikipedia articles. [53] have found that articles edited by
contributors of rich and broad background – i.e. those that in their history
have edited many varied articles – are more likely to reach FA status. This
might not only be an effect of broader subject expertise of these editors but
also of a process of “creative abrasion” wherein editors with varied knowledge
and experience engage in discussions to resolve their differences, which leads
to article improvement [4]. Whilst some abrasion might be positive and promote
quality, too much of conflict can also be detrimental [6]. It seems there is
an optimal level of diversity of contributors working on an article and that
it is moderated by the size of the group: smaller groups can be less diverse
and still deliver high quality articles [45].

Diversity might be a factor positively impacting quality, but combining
diverse inputs requires processes of coordination, which are recognized as a
crucial aspect of quality control in standard organizations [12]. In online
social systems coordination is peer produced in a similar manner to product
development [14]. That is, contributors self-select to perform coordination
tasks. Not surprisingly, coordination plays a part in creating quality
articles on Wikipedia: even such simple coordination as splitting into core
and periphery roles (defined as editors who submit the bulk of content vs.
editors who make minor additions) increases the chances for FA status [27].
Interestingly, without such a split, simple increase in the number of editors
did not lead to improved quality (ibid.).

Finally, an important factor impacting quality is engagement. In standard
organizations motivation and conscientiousness can be promoted by various
incentives, including financial ones. In online peer production other
incentives are at play: ranging from reputation, to self-development, to
socializing [39]. Engagement can be induced or sustained by interactions with
other, well-socialized members of the community: welcoming messages,
assistance and constructive criticism increase commitment of new editors on
Wikipedia and increase the chances for their prolonged contribution [19]. Such
motivational activities are mostly carried out by sending direct messages,
i.e. editing a newcomer’s personal talk page [60]. Whilst core community
members send more such messages per person, in total peripheral members
deliver the bulk of engagement related direct messaging, contributing to the
shared leadership within Wikipedia (ibid.). Interestingly, within the thematic
oriented groups on Wikipedia (Wikiprojects) task related and feedback related
direct messages are initiated more often by peripheral members while
socializing aimed messages are sent more often by core members (ibid.). This
suggests that just as it happens in offline groups [9], in online peer
production communities social and task leadership is divided to a degree.

###  2.2 Direct communication in group work

Interpersonal, one-on-one communication not only affects motivation of group
members but it is also the vehicle for much coordination in standard
organizations. Whilst formal oversight and management are important for policy
and top-level management, coordination of tasks is often carried out in
informal meetings or communication [13]. Dyadic relationships and
communication play a role in maintaining motivation, engagement and
cohesiveness of a group [31], contributing to the affective domain within the
group [9]. Through dyadic interactions, members of the group transmit
emotional support and share task-related resources [38]. Direct communication
also warrants social learning, i.e. learning that occurs through observation
and direct instruction of more experienced team members [50].

In digital collaboration, personal messages have also been identified as
impacting group work. For example, informal, direct communication among
distributed workers helps to build bonds and ensures effective information
flow [23]. The importance of direct communication in distributed teams is
corroborated by the fact that activity on user pages on the English Wikipedia
continued to grow in spite of declining number of edits on article pages [36].
Moreover, disassortative direct communication network structures – i.e.
linkages between hubs and periphery of the networks – are prevalent among
Wikipedia’s Wikiproject teams and mild disassortativity is related to higher
quality of articles that these projects curate [47].

###  2.3 Summary and hypotheses

The effectiveness of an online collaboration group – i.e. the quality of its
products – can thus be related to both its task related structuration that
ensures proper task performance and to interpersonal relations (affiliation
processes) that allow coordination and promote engagement [8]. The former has
been extensively studied in both Wikipedia and FOSS by investigating division
of labor and (co-)contribution patterns (e.g. [5, 42]). Relation building and
motivational structures, on the other hand, have been much less investigated;
yet offline theory and research of group processes as well as initial
investigations into the role of personal messaging indicate that it plays a
crucial role in group effectiveness. Specifically, the content of direct
communication has been shown to impact motivation to contribute [19] and to
relate to relational leadership [60], however the structure of such
communication may also play a role.

On Wikipedia this structure can be gleaned from direct messages sent between
editors and analyzed as a network [47]. If indeed direct messages are a
vehicle for building and maintaining engagement, (H1) successful collaborating
groups will exchange a larger volume of such messages than unsuccessful ones.
Moreover, if they are a means for self-regulation and coordination (H2),
direct communication structure of successful teams will allow for both local,
specialized information sharing as well as global integration of information
within the group [28]. Finally, if they serve as a vehicle for group
integration (H3) direct messages will link a larger part of the group (rather
than some selected group members) within successful groups than unsuccessful
ones. To test these three hypotheses we have designed an exploratory study
into the communication structure of thematic groups on English Wikipedia:
Wikiprojects, and into its impact on the effects of group work: quality of
articles. For each project we have reconstructed a network of direct messaging
and measured the related properties: average node strength, network degeneracy
and determinism (components of effective information as defined by [28]), and
fraction of project editors that take part in direct communication network. We
related these network properties to the quality of articles within the
relevant project’s scope and we found that direct communication networks with
high effective information and strong links are related to more effective
group work. To investigate in depth the role of such cohesive communication
networks in Wikiprojects’ work and with results of our quantitative analysis
in mind, we designed semi-structured interviews that we then carried out with
selected members of two projects: the one that was most successful with regard
to producing quality articles (Tropical Cyclones), and one that was the
fastest growing project in 2020 (COVID-19). We found that direct messaging is
crucial for feedback on individual work and that one-on-one communication
networks play a part in tapping on the groups’ social capital.

##  3 Methods

We conducted a Thick Big Data analysis [25] of collaboration on English
Wikipedia. Thick Big Data is a novel mixed-method approach, allowing for
combining quantitative analysis of large datasets with deep, qualitative
inquiry. In our case, the analysis relied on combining qualitative interviews
with heavily engaged Wikiproject participants and network analysis of direct
communication within the projects.

###  3.1 Wikiprojects

As our unit of analysis we have chosen English Wikipedia’s Wikiprojects. These
are groups of editors that work together on encyclopedic articles related to a
specific topic222https://en.wikipedia.org/wiki/Wikipedia:WikiProject .For
example, there is a project on Military History, on Michael Jackson or on the
Simpsons. Projects vary in scope (i.e. the number of articles they curate),
depending both on the breadth of the subject as well as on its popularity
among Wikipedians (the more editors are interested in a particular topic, the
more articles they will create and the better the coverage of this topic on
Wikipedia). There is one thing all projects share, though: they all strive to
improve the quality of articles within their scope. To achieve this goal they
use various methods of coordination - from discussion on article importance,
to editathons and drives to improve articles, to templates and preparing
manuals of style, among others. Each Wikiproject has a dedicated space on the
so-called project namespace on Wikipedia. In there, the goals, rules and
procedures, as well as current information on the project are displayed (Fig.
1). These spaces also include talk pages that are used for discussion of
current issues within the project.

Figure 1: Screenshots from the Wikipedia coordination space for Wikiproject
Military History: (a.) the front page, delineating the goal of the project;
(b.) fragment of a section with pending tasks for members to work on; (c.)
quality statistics for the project; and (d.) fragment of the member list where
Wikipedians can join the project.

###  3.2 Data gathering and network construction

Source code of all user talk pages on English Wikipedia was collected via
Wikipedia API333https://en.wikipedia.org/w/api.php during the first half of
February 2021 (the process ended on February 11) using the CirrusDoc query
endpoint. Simultaneously, information on all quality assessments of articles
available in the main encyclopedia (so-called main namespace) was extracted
using the AllPages query method of Wikipedia API (the process ended on
February 12). Inconsistent naming of Wikiprojects was standardized. The
extraction process spanned several days as in both cases a high level of auto-
throttling was used in order not to consume too much of Wikipedia API
bandwidth.

Next, we defined sets of Wikipedia editors associated with individual projects
as all editors who left at least one signed message on one of the project
pages (but not talk pages) belonging to a given Wikiproject. Crucially,
project pages also include lists of project members which consist of (usually)
user signatures. Hence, we included all editors properly signed on member
lists as well as all who contributed in a detectable way to creation of
project pages of a given Wikiproject. This second case is important as not all
editors active within a project choose to sign up to member lists. We
consciously did not include all editors who contributed to the encyclopedic
articles curated by projects, as such lists would include many accidental
editors, not collaborating within the projects.

Then, we used a discussion parser tailored for Wikipedia talk pages [54] to
extract all discussion threads from talk pages of all users who were
identified as Wikiproject editors. The threads selected by the parser included
template based messages (such as warnings, barnstars, welcome messages, etc.)
but did not include mass messaging, such as newsletters.

Finally, the discussion threads were used to define undirected weighted links
between pairs of editors who left messages on each other’s user pages with
weights corresponding to the number of interactions between them. We chose to
use undirected links as it is usually very hard to keep track of messages and
their replies due to many different communication customs used on Wikipedia.
For instance, in some cases an original question left on a user’s talk page
may be answered by the user on that exact same page while in other cases the
reply may be left on the talk page of the editor asking the original question.
This is why we prefer to consider any message between two users as an instance
of an undirected interaction between them.

We used the above approach to recreate a total of 1625 networks. However, for
downstream analyses we used only a subset of projects with at least 5 editors
being present in the direct communication network. This filtering step was
necessary as most structural network metrics are not really meaningful for
smaller networks as they are constrained to take values from only a very small
and discrete set. Moreover, projects with no FA/GA pages were also excluded:
having produced no quality output these projects may well comprise a different
population and hence would not help us address our research questions on
impact of interpersonal communication on group efficiency. After these two
filtering steps the initial set of 1625 direct communication networks was
reduced to 997 observations of undirected networks.

###  3.3 Interviews: choice of respondents and procedure

For the purpose of our qualitative part, we conducted four interviews: two
with early participants from Wikiproject Covid-19, and two with early
participants from Wikiproject Tropical Cyclones. We reached out to eight of
the early editors listed on the Wikiprojects’ pages. We focused on those who
are still active and who allow contact by email. Four people finally agreed to
an interview. The interviews took about one hour each, and were conducted via
Google Meet platform (with video). All interviews were recorded with consent
and transcribed. The interviews were loosely structured and open-ended [20].
The questions were grouped into a few themes that reflected our hypotheses:
engagement, project coordination, and communication within the project. The
sample questions included:

  * •

Engagement and outreach

    * –

How did the project start?

    * –

What makes a successful wikiproject?

    * –

How are the new members recruited and on-boarded?

  * •

Project coordination, division of tasks and governance

    * –

Why do you participate, and how do you choose what to do?

    * –

What does the leadership of the project look like?

    * –

What could you tell about the changes in coordination of the project over the
years?

  * •

Communication

    * –

How do project members communicate?

Table 1: Interviewee list User | Active since | Project
---|---|---
Interviewee 1 | 2007 | COVID-19
Interviewee 2 | 2006 | Tropical Cyclones
Interviewee 3 | 2005 | Tropical Cyclones
Interviewee 4 | 2003 | COVID-19

##  4 Results – network analysis

###  4.1 Analytical strategy and measures

Our hypotheses are centered on the impact of communication structure on the
capacities of a group to deliver high quality products by collaboration. By
reviewing extant findings on quality work in peer production we pinpointed
that direct, one-on-one communication can bring in group integration,
engagement and complex coordination patterns, all required to produce complex
artifacts of high quality such as featured Wikipedia articles. However, these
theoretical concepts can be measured in multiple ways and for the results to
be meaningful a lot depends on proper operationalization.

###  4.2 Group integration

Following the results of [60] we assume that both peripheral and core members
are involved in leadership behaviors aimed at affiliation processes. Thus an
important aspect of quality Wikiproject work would be to encourage
communication between as many members of the group as possible. This can be
measured as the fraction of project members that are included in the direct
communication network.

###  4.3 Engagement-building strong ties

Following the work of [9] we assume that affective processes within a group
are important for group effectiveness. Moreover we accept, after [39] that
engagement of group members results in sustained, high quality contributions,
and after [60] that direct messages can be used to raise such engagement. Thus
we might conclude that a simple network measure reflecting the number of
communication links between project members (e.g. average node degree) would
capture the extent to which such a network can transmit motivational messages.
However, affective processes within a group are not the same as information
transfer. For example, informing all group members about a task requires only
one message per member. Not surprisingly, such processes on Wikipedia are
automated – Wikiprojects use newsletter or mass-messaging to spread
information broadly. However, raising motivation and building strong social
ties requires reciprocity and perseverance; that is, multiple communication
acts – messages – are conducive to a stronger relationship. Therefore, average
volume of messages per node (i.e. average node strength) – rather than average
degree – would be a better measure of Wikiprojects’ capacity to build
engagement.

###  4.4 Complex coordination: Determinism, Degeneracy and Effective
Information in networks

Finally, following [37] we assume that complex functions in social systems
require complex coordination patterns between elements. Theoretical work by
[56] showed that structures that are capable of complex coordination need to
have local specialization and global integration. Examples of such complex
coordination can be found in brain imaging studies and neural modelling [57].
These concepts from information theory have recently been adapted into network
measures of determinism (local specialization) and degeneracy (reverse of
global integration) [28]. Both can be used to assess a social system’s
capability to coordinate in a complex manner.

We follow [28] in our approach to quantifying structure and organization of
the direct communication networks through the lenses of determinism,
degeneracy and effective information. Here we review the main definitions.

A graph $G=(V,E)$ consists of a set of $n=|V|$ vertices and a set of $m=|E|$
links connecting selected pairs of vertices. Moreover, each link $(i,j)\in E$
has a weight $w(i,j)\in\mathbb{N}^{+}$ indicating the number of interactions
between $i$ and $j$ and moreover $w(i,j)=w(j,i)$ for all $i,j=1,\ldots,n$.
Each graph can be represented by a $n\times n$ adjacency matrix $\mathbf{A}$
such that $a_{ij}=w(i,j)=a_{ji}$ if $(i,j)\in E$ and $0$ otherwise. Then, from
any $\mathbf{A}$ a directed transition matrix $\mathbf{W}$ can be derived by
dividing each row by the strength (sum of weights) of the corresponding node
(rows with zeros only are left without change). This way values in each row
are non-negative and sum up to $1$ so they can be interpreted as a valid
probability distribution over possible next positions of a random walker
starting at node $i$.

Then, determinism (local specialization of connections) of a graph G is
defined as:

$\text{Det}(G)=\log_{2}{n}-\frac{1}{n}\sum_{i=1}^{n}H(w_{i})$ (1)

where $H(w_{i})=\sum_{j}w_{ij}\log_{2}{w_{ij}}$ is Shannon entropy over the
$i$-th row of the transition matrix $\mathbf{W}$ (isolated nodes are ignored
in the calculation). Thus, determinism is the average certainty associated
with the next move of a random walker placed randomly somewhere on the
network. It is maximized when each node is connected to only one other node
and minimized when each node connects to all other nodes. Hence, it is a
measure of how specific local connections of nodes are on average.

Degeneracy of a graph G is defined quite similarly but in terms of Shannon
entropy of the average over rows of W:

$\text{Deg}(G)=\log_{2}{n}-H\left(\frac{1}{n}\mathbf{1}_{n}^{\top}\mathbf{W}\right)$
(2)

where $\mathbf{1}_{n}$ is a vector of ones of length $n$ (again, isolated
nodes are ignored). Thus, degeneracy is a measure of how concentrated the
connectivity of $G$ is around a small subset of nodes. It is maximized when
all nodes are linked to only a single hub and minimized when on average each
node is visited equally often. In other words, it is a measure of the global
integration of $G$. Example, artificial networks with representing structures
of low and high degeneracy are presented in Fig. 2.

Figure 2: Examples of networks with low and high measures of degeneracy; more
examples can be found in [28], figure 2.

Both Det and Deg have well-defined maximum values of $\log_{2}{n}$ so they can
be normalized in the range $[0,1]$ simply by dividing by $\log_{2}{n}$. Last
but not least, they allow to define a quantity known as effective information
which describes the overall structure of $G$ in terms of local specificity and
global integration:

$\text{EI}(G)=\text{Det}(G)-\text{Deg}(G)$ (3)

The above definition of effective information mimics closely the theory of
integrated information of [56] and can be seen as a measure of non-trivial,
complex structural organization in networks.

###  4.5 Group size

Acknowledging previous work on quality in peer production that focused on task
processes within a group – e.g. the composition of teams in terms of expertise
or size of the collaborating teams [59] – we chose the size of the contributor
group (i.e. number of project members) as a control variable. By controlling
for size of the projects we can assess whether affiliation processes
contribute to group efficiency when controlling for task-related processes.

###  4.6 Quality of group work

The quality of group work that we aimed to relate to the properties of
communication networks can be approximated by the quality tags assigned to
articles by the Wikipedia community. The two ranks that are assessed by the
whole community with respect to the same criteria are FA and GA (other ranks
can have different criteria depending on the Wikiproject and the review
process for them is carried out within projects). The numbers of FAs and GAs
for each project are highly correlated ($r_{\text{Pearson}}=.92,p<.0001$) and
thus they can be combined into a single measure of quality. However, projects
differ vastly in their scope – i.e. the number of articles that they curate –
and we can expect that larger projects would naturally have a larger number of
quality articles. For example, more articles within a thematic area can draw
in more contributors interested in the various subtopics, who would self-
select to work on the relevant articles. Moreover, the number of articles
within a project’s scope is the cap on the number of possible quality
articles. Thus an absolute value of quality articles will lead to a quality
ranking with the top filled with projects of very large scope.

On the other hand, normalizing the quality score by the number of articles
within a project’s scope (i.e. the quality score becomes the fraction of high
quality articles within all project’s articles) privileges smaller projects.
In the most extreme case, a project curating only one article can achieve a
hundred percent efficiency in producing quality articles with relatively
little effort. Since the distribution of projects’ scope is highly skewed
(most curate few articles), using such normalization would unduly lower the
quality ranking of the largest projects.

We can thus consider a family of quality scores indexed by a parameter
$p\in[0,1]$ such that the quality score for a project $x$ is
$Q_{p}(x)=N_{Q}(x)/n^{p}$ where $N_{Q}(x)$ is the number of FA+GA pages
curated by $x$. Within this family the two cases described above constitute
the extreme cases. A quality score with no normalization by the projects’
scope is given by $Q_{0}$ and quality score normalized with the number of
articles within scope is given by $Q_{1}$ . To construct our response variable
we have chosen the middle case in this family of quality rankings: $Q_{1/2}$ .
In effect, we normalize the number of quality articles not by the absolute
number of articles within a project’s scope but rather by the size class of
the project’s scope. Indeed, the square root of size serves as variance-
stabilizing transformation and as such attenuates the effects of the highly
heterogeneous distribution of project sizes while not discarding this
information completely. An additional confirmation of the validity of this
approach is given by the fact that $Q$-score with $p=1/2$ has almost perfectly
log-normal distribution. Table 2 lists descriptive statistics for the chosen
measures.

Table 2: Descriptive statistics of project measures Variable | Mean (SD) | Median
---|---|---
Quality | 1.172 (1.684) | .652
Fraction in communication network | .512 (.173) | .503
Determinism | .761 (.093) | .782
Degeneracy | .265 (.139) | .238
Average connection strength | 30.094 (39.185) | 17.143
Number of project members | 136.39 (249.98) | 61

###  4.7 Modelling quality

To verify if group integration (fraction of project contributors within
communication network), engagement building social ties (average node
strength), and communication structure allowing for complex coordination
(determinism and degeneracy) impact the quality of artifacts produced by peer
production groups, we tested a linear regression model with all the above
variables as predictors, quality score normalized by the square root of
articles within scope as the response variable and number of editors listed
within the project as control variable (Table 3, Model 1). Quality score,
number of users within the project and average node strength were
approximately log-normally distributed so we used their log transforms in the
model. The model explained 23% of variance in the quality scores of
Wikiprojects ($F(5,991)=59.66,p<.001$). All predictors except the fraction of
users within the communication network were significant. We tested a model
(Table 3, Model 2) without the non-significant predictor, and the decrease in
variance explained was not significant ($F(4,991)=.178,p>.05$). The model
without fraction of project members within the communication network also
explained 23% of the variance in quality scores ($F(4,992)=74.6,p<0.001$).

Table 3: Effects of network structure on quality

Predictor | Model 1 | Model 2 | Model 3
---|---|---|---
Fraction in communication network | -.093 (.22) |  |
Determinism | 1.313∗ (.54) | 1.33∗ (.54) |
Degeneracy | -.665∗ (.30) | -.71∗ (.28) |
Average connection strength (log) | .285∗∗∗ (.04) | .278∗∗∗ (.04) | .246∗∗∗ (.03)
Number of project members (log) | .315∗∗∗ (.04) | .32∗∗∗ (.04) | .347∗∗∗ (.03)
Effective information |  | .667∗ (.28) |
Constant | -3.369∗∗∗ (.38) | -3.41∗∗∗ (.37) | -2.994∗∗∗ (.18)
R2 | .231 | .231 | .23

  * •

Note: Linear regression coefficients; standard errors in parentheses;
${}^{***}:p\leq.001$, ${}^{*}:p\leq.05$

Note that the regression coefficients for determinism and degeneracy are
similar but with opposite signs. We can confirm this with a test of linear
hypothesis: $H_{0}:b_{\text{deg}}+b_{\text{det}}=0\ (F(2,992)=2.11,p>.05)$.
The opposite impact of degeneracy (global integration) and determinism (local
specialization) is indeed proposed to characterize complex systems. The
measure of effective information, defined as
$\text{EI}(G)=\text{Det}(G)–\text{Deg}(G)$ [28, 56] describes how much
information is integrated within a complex system and can be used as an
estimate of the system’s complexity. It has even been suggested as an
approximation of the level of consciousness of a system [55].

We tested a simplified model with degeneracy and determinism predictors
replaced with effective information (Table 3, Model 3). All predictors were
significant, the model explained 23% of variance in quality scores
$(F(3,993)=98.65,p<.001)$, and in that was not significantly different from
the model with degeneracy and determinism $(F(2,992)=2.11,p>.05)$. Example
networks, depicting various levels of effective information are presented in
Fig. 3. Thus we can conclude that effective information of the communication
structure within Wikiprojects plays a significant role as a factor
facilitating production of high-quality content on Wikipedia.

Figure 3: Sample Wikiproject communication structures, varying in degeneracy,
determinism and position in the quality ranking.

##  5 Results – interviews

###  5.1 Analytical strategy

Our quantitative analysis of the structure of one-on-one communication
networks within Wikiprojects indicated that quality of their collective output
grows with the strength of the ties between Wikiproject members, with the
specialization of local connections (i.e. when project members have specific
partners with whom they communicate more than with others), and with
integration of information among all group members (i.e. when the connections
in the network do not coincide on a limited set of nodes but rather the
information spreads among all parts of the communication network). To further
verify these findings and to gain an in-depth understanding of why such
communication structures form and in what way they contribute to group
collaboration, we interviewed selected Wikiproject members. We focused our
analyses of this qualitative data on the uses of direct communication within
group work and its role in fostering engagement and in coordinating group
work. The choice of an established, highly successful Wikiproject provided us
with information on how direct communication contributes to success.
Wikiproject Tropical Cyclones ranked as the project with highest quality of
collective output. The choice of a fast-growing project allowed us to
understand what processes lead to the formation of particular patterns of
communication. Wikiproject COVID-19 was started on 15.03.2020 and by February
2021 had accumulated 1375 articles and gathered 274 participants.

###  5.2 The role of direct communication

At the moment of founding, a new project’s communication is concentrated
within the project’s coordination page and thus easily accessible to all
prospective members. In the case of our analyzed COVID-19 project, the quick
growth in interest and participation resulted in a fast flow of information:
“You also just saw lots of people contributing to those initial discussions on
the main project talk page. There were so many discussions being started and
ideas being floated (…) that it’s almost overwhelming to follow.” (Interviewee
1)

This flood of discussions resulted in diversification of communication
channels. More specific discussions (pertaining e.g. to the epidemics
development in specific regions) migrated, in an organic manner, to the talk
pages related to specific encyclopedic articles: “a lot of the discussion that
you saw early on on the project talk page has migrated to the article space”
(I1). This left the project’s main coordination space as a place for more
general discussions: “the number of discussions being generated has of course
declined a lot but it [is] still active. You still have editors asking
thoughtful questions and soliciting feedback on specific pages.” and “…I feel
like it’s still being used as a space for new information”. (I1)
Interestingly, this early diversification of information points to an
important role of direct messages posted on users’ talk pages.

Interviewee 1 said that he himself had little time to engage in interpersonal
communication but that it was a common vehicle to provide feedback and
corrections to the fast paced growth of contributions to the articles the
project curated. “There were a lot of people who probably felt that their
contributions to edit to Wikipedia were reverted [but] their additions were
just relocated. (…) Moving those requests to other spaces, I’m sure there was
a lot of back and forth between editors on their talk page.” (I1) It seems
that in this case direct messages were used as corrective feedback and
explanation of actions taken by other editors. Importantly, “back and forth”
exchanges took place, confirming our intuition that direct messages are used
often not as a simple transfer of information but as a vehicle to build common
understanding of the situation of group work: what are the accepted actions
(norms) and what an article should look like (goals). In this, such messages
might have reduced the discouragement of editors whose contributions were not
accepted as they were but rather were moved to other articles.

Direct messages very often play the role of positive feedback as well. Such
messages may take the form of a standardized text filled in with particular
information (i.e. a filled in template), which can be pasted onto a user’s
talk page using semi-automated tools such as the Twinkle Wikipedia add-on. A
specific form of such templates are barnstars, acknowledgements and other
awards which take the form of an image together with a personalized text
message: “If someone writes a good article or like, they really show that
initiative, they’ll probably get a couple barnstars from a few different
users.” (I3) More personalized feedback is delivered via messages written
especially for the occasion. Interviewee 4: “I’d rather say thank you in my
own words. I’d rather go and write, you know, thanks for your help with that.
It was really useful.” In addition, the “thank you” function (an option
available when viewing the edits contributed to a specific article) is an
automated tool frequently used to give positive feedback, once an editor makes
a good input (Interviewees 1 and 4). In contrast to personalized messages,
such “thank yous” are not visible on the user talk page, but rather appear as
a notification in the user profile. This gives editors a range of options to
provide feedback that ranges from automated to very personalized, amplifying
their capability to build social ties of different strength.

Negative feedback also ranges from more to less personal. Interviewee 4: “I
also use twinkle to issue warnings and notifications. To people, I don’t know.
(…) But for people I know I’m more likely to use a talk page message.” The
templates available in the Twinkle tool allow users to provide depersonalized
feedback and also to speed up the communication. This presumably allows to
streamline task-related work. However, as the above quote shows, when there is
already a relationship between users (a stronger social tie), a personalized
message proves a better option: it might invite a reply and thus a
deliberation on the shared understanding of norms and goals of the group.
Thus, direct communication differs depending on the closeness of the
interlocutors, with personalized messages inviting reciprocity and thus
increasing the strength of the social tie.

The role of personalized, one-on-one communication is even more vivid when
projects engage in soliciting new editors and in socializing them with the
group. Communication on user talk pages is a way to establish sustainable
relationships between more experienced users and newly joined members: “(…) I
tell them to leave me a talk page message [as a] part of their training. So
again, they get the experience of doing that and I capture their username. So
it’s quite a cheating way of gathering everybody’s username.” (I4). After
leaving their name on each other’s talk pages the editors are no longer
anonymous strangers – they establish a personal, one-on-one channel of
information exchange.

Experienced editors teach new project members about what needs to be edited
and what makes a good edit. Interviewee 4 provides a detailed description of
such training: “(…) I train a lot of new people and I watch their early edits
and I use welcome templates purely because it’s a lot easier than trying to
remember all the links that you want to give them for help pages and so on.
(…) And I always look for their first proper edits outside of their sandbox
and leave a thank [you] with the thank [you] mechanism. Partly to thank them
and encourage them but also so that they see how that works.”

Over time and with increasing volume of communication, relationships tend to
transcend from task into more socio-emotional realm. This is accompanied by
further diversification of communication channels. Interviewee 3: “A lot of it
[is] user talk pages, there’s a Facebook group among a couple users, we’re all
Facebook friends. (…) there’s an IRC Channel. There’s also a Discord channel.
So there’s lots of different ways that the users will talk to each other. And
it’s become a bit of like socializing as well. Like there’s some users I talk
to almost every day. (…) In a way, they’ve become almost a family of sorts.”
Interviewee 4: “Off-Wiki friendships develop, and they obviously develop
sometimes within projects. And you can tell people are making reference to
things that they’ve talked about in email or that they met in a purple gun for
a coffee or had a pizza or whatever”. Such strong, social bonds impact
collaboration, for example: “I’m probably more likely to help if it’s somebody
I’ve met and work[ed] with previously than if it’s just a name on a talk page,
I’m not saying I wouldn’t help and I’m not saying it’s the only reason that I
would help, but it’s certainly encourages me to get involved.” (I4)

In sum, throughout a project’s existence, collaboration is coordinated via
various channels of communication that serve different communicative purposes.
Direct messages on users’ talk pages often serve to invite and socialize new
members and to provide feedback to encourage further contributions. Stronger
relationships are enacted through more personalized messages, presumably to
invite reciprocal and more prolonged exchanges.

###  5.3 Organization and coordination of group work

Interviewee 1 defines Wikiproject as “a single space where lots of editors (…)
come together and really work to bring some consistency across” thus
underscoring the importance of coordination of work within the group. Good
projects differ from bad projects in the extent to which they are able to
maintain “an active community (…), engagement with the wider community and an
outreach” by organizing and announcing editathons, campaigns and competitions
(I4). Thus, the distinction between successful and unsuccessful projects has a
lot to do with how the project is connected to the wider Wikipedia community:
“Good projects (…) interact with the rest of the community to help people to
understand the technicalities, to reach out to people who are working in that
sort of area and involve them. And the bad projects see themselves as being
the custodians, the ring fences, the owners of stuff” (I4). From this we can
understand that good projects are able to tap on resources available in the
community, while bad projects remain a specialized, closed group of
contributors.

This understanding of success as an ability to mobilize distributed resources
into a single effort is also reflected in how the work within projects is
organized. When asked about division of labor and coordination of tasks, the
interviewees underscore the bottom-up, organic manner in which such processes
take place. In the newly formed COVID-19 project editors try to “find their
niche, their dedicated space within the project (…), saying: I want to focus
on this or I’m gonna sign up to focus on [COVID-19] case counts in Italy or
Spain (…)” (I1). Similarly, our interviewee from the Tropical Cyclone projects
reminiscens: “I remember these users (…) just took the initiative to make
standardized info boxes, eventually getting standardized [hurricane] track
maps, making sure that all the different hurricane articles look pretty much
the same, that they were high quality, that they were referenced, that they
weren’t using blogs for information.” (I3) In a sense, “the division of labor
(…) kind of became self-evident” (I3).

At the same time they notice that there is little in the way of formal roles
or leaders that would serve as coordinators: “(…) People just wanted to help
organize the movement’s efforts, (…) so that editors weren’t just thoroughly
confused about where they were supposed to be adding new information. I wish I
had a more clear answer for you on leadership. (…) I’m sure there were people
who stepped up and, you know, really took initiative.” (I1) “I haven’t seen
any sort of more formal structure. The only [projects] that tend to have a
more formal structure are the ones where there is a user group off Wiki. So
Wikiproject Medicine has the medicine foundation with a board and fundraising
(…)” (I4). In effect, the within project coordination becomes seamless and
implicit, as users self-select to fill in where others left work to be done:
“It’s almost an informal collaboration, like we know what’s expected so it’s
not exactly coordination” (I3).

In the case of established projects, this smooth collaboration might be an
effect of experience, of editors internalizing their specific roles in the
group effort. “Over [the] years we’ve collaborated and discussed (…) and in
the new [hurricane] season articles (…) people can look back at other years
and realize oh that’s what we’ve done, so that’s how it happens (…).” (I3)
It’s an effect of “past years’ work, coordination, reviewing the good
articles, setting the standards, making sure that all the articles look the
same”. (I3) The project works like well-oiled machinery, where the members are
specialized in their tasks but at the same time rely on others to fulfill
theirs.

Communication plays an important role in making such a system work. Project
members specialized in certain areas (e.g. making maps for hurricane tracking)
naturally communicate with each other, but they also need to reach out farther
- within and across projects - to tap on resources and skills they do not
possess. Interviewee 4: “I go to the project page and find somebody, and then
go and talk on their talk page. (…) If I need to go and ask for some advice or
get a second opinion (…), you might have a source that I want to look
something up for me.” Whilst direct messaging might be one of the choices for
such advice seeking, other means of communication may be used. “I will private
message them or leave them a message on their talk page. And in one or two
cases, contact them off Wiki: a couple of them I know by email, or by
Facebook. And I might just say, do you have this paper? Or I’m trying to find
out this person’s date of birth. Can you look it up in one of your sources for
me?” (I4)

Such resource seeking might also happen on a project’s page to increase the
visibility of the request or in situations when the editor does not have a
personal relationship with anyone with fitting expertise. Interviewee 4: “you
use your social capital, to use the jargon, to get help and to help other
people. (…) There are a few Wikiprojects that I go to when I have a question.
(…) If ever I’m writing a biography of somebody and they have some experience
in the military (…), the Military History project is a good place to go and
check. (…) Ask people to look things up in resources that they may have
subscriptions to. (…) Make sure I’ve got the terminology correct, things like
that. (…) There’s a UK Railway project or rather a route and international
railway project and that’s simply a good place where you can go and get
technical information and get people to look stuff up. Being sources for you.”

Project members also notify each other when they notice something that might
be of interest to somebody. Interviewee 4: “I also tend to drop things into
projects (…) if I think they’ll be of interest. So if I see something in the
news and I can’t write about it, but I think somebody might be interested and
I know it’s an active project in an area that I’m interested in, then I’ll go
and leave something on a talk page.”

Thus it seems members of Wikiprojects treat each other as a reliable source of
information. Interviewee 2: “I’ve got access to Discord Channel, I’ve got
access to the IRC, I can just generally ask: can you just take a look at this
article? See what you think? [Am I going ] in the right direction. Am I
wrong?” However, information exchange relies on previous experience with a
particular Wikipedian and mutual trust. Interviewee 4: “If you (…) use the
[project] talk page to ask a question, you know which group of people will be
replying and indeed, you know which ones you can rely on if you get an answer.
(…) You think: well, I’ve interacted with them often enough and seen their
interaction with other people. Often up, I know they have a level of expertise
and they’re reliable and they [are] trustworthy in terms of the technical
response. And indeed more generally.”

In sum, Wikiproject’s divide and integrate their work through a bottom-up
self-selection to tasks. Experience in collaboration helps them develop
procedures for implicit coordination of tasks and allows them to build strong
ties that result in trust and reliance on each other. Moreover, project
members specialize in specific tasks and learn where to seek advice and help
when they need information or skills beyond their own expertise. Communication
within and across projects helps to connect such specialists; however trust is
crucial to rely on such sources. In this, Wikiproject’s display a high level
of social capital: even if project members do not know something, they
proverbially “know who knows” and are able - through their stronger (personal
relationship) or weaker (through projects or initiatives they know) ties - to
tap on this knowledge.

###  5.4 Summary of results

Our quantitative investigation into Wikiprojects’ direct communication
structure showed that strength of the relationships within the group - as
measured by the average number of messages sent over the social ties -
correlates with higher quality products of collaboration. By interviewing
selected members of an established and a growing Wikiproject we confirmed that
direct messages can serve as a channel for socializing new project members and
for sending positive and corrective feedback. Other popular means of
communication within Wikipedia - such as project talk pages - are used for
setting the agenda for future work or soliciting less personal feedback from a
broader group of project members.

Moreover, our interviewees differentiated between automated and personalized
messages: those framed as a standardized warning or acknowledgment of work
were used for contacting distant or altogether unknown relations, while
personalized messages were written for the strong and close connections. Thus,
not only the volume of communication but also the form of messages can convey
how close the interlocutors are. In sum, personalized, direct communication
seems to complement more official, group communication channels by providing a
vehicle to build strong social ties that facilitate affective processes within
the group. In contrast, the fraction of group members that take part in direct
communication did not play a part in group efficiency. We may conclude that
what matters is the quality of relationships, not their span.

The other important result from the network analysis is that successful
Wikiprojects have a direct communication structure that allows for complex
coordination: for sharing of information locally, between project members
clustered into small groups, and at the same time globally across such groups
rather than by a single coordination center. In interviews, Wikiproject
participants shared that they preferably seek advice from and offer help to
those whom they trust and whose expertise they are aware of; thus they operate
within a close circle of strong social ties. At the same time, they are
skilled at using group level communication (e.g. project talk pages) to
acquire information that their closest relations do not possess. In effect,
whilst locally they might communicate with a restricted number of
collaborators, globally, it is easy for any resource or information to
traverse the network. Thus both quantitative and qualitative analyses converge
to show that complex patterns of information integration and coordination
within Wikiprojects are related to better group performance.

##  6 Discussion

In our studies we attempted to verify how affiliation processes (affective
domain) complement task-related coordination (instrumental domain) within
groups collaborating online in the course of producing high quality output of
collective effort. As argued by early influential research on group
interaction [8, 9], group dynamics must be studied in the context of these two
conflicting developmental tendencies within the group, as they create inherent
challenges that determine how the group will operate [7, 10].

The first type of challenges pertains strictly to the group task and how
effective the group is in its achievement (instrumental domain). The second
type of challenges relates to the extent to which group members are able to
maintain close social ties and sustain group cohesion (expressive domain).
Tensions caused by the instrumental challenges are reduced through division of
resources and labor, and establishment of status and authority hierarchies
within the group. On the other hand, tensions within the expressive domain
induce behaviors that are focused on integration of opinions, and uniformity
among group members. Moreover, according to [33], affective processes have a
regulatory (reinforcing and directing) function in relation to task behavior.
Thus, a group is a system in a dynamical equilibrium between the conflicting
drives for cohesion and differentiation [8]. With the growing ubiquity of
virtual collaboration - a tendency which the pandemics has strengthened and
possibly calcified - it is important to understand how this balance can be
achieved even when groups collaborate solely via new media. Wikipedia is a
renowned success case of such technology mediated collaboration.

A growing body of work investigates task related coordination on Wikipedia. An
influential study has found that implicit coordination - in the form of
division of contributors into a highly productive core and periphery - was
related to higher quality of articles, more so than the number of editors or
explicit coordination in the form of discussions on the article talk pages
[27]. Other studies, however, found that numbers matter: both a higher number
of editors, as well as a higher number of edits correlated with high quality
[59]. An explanation of this discrepancy might be that it is important who
comprises such a high number of editors. For example, varied background and
diversified experience in editing [53], longer tenure, and willingness to
perform multiple roles (functional tasks within article editing, [32])
positively impact quality of the articles on which editors work.

Explicit coordination has also been proven to matter. The structure of
communication on Wikiproject talk pages impacts projects’ efficiency (measured
as number of edits to articles): again, existence of a core of editors with
high numbers of discussion posts and high values of betweenness centrality in
the discussion network, was beneficial. Thus both implicit (division into core
and periphery) as well as explicit (discussions) task related coordination is
correlated with higher efficiency in pursuing collective goals. There is much
less known, however, about the affiliation processes in online collaborative
environments. Our study fills this gap.

First, interviews’ analysis showed that Wikipedians diversify their
communication, depending on the communicative goal (task or affiliation
related) and strength of relationship. The preferred task-related coordination
space are talk pages - both of encyclopedic articles and of Wikiprojects. The
affective processes, on the other hand, take place on user talk pages where
Wikipedians post direct messages to one another, or outside of Wikipedia, on
various social media sites. While such direct messages can have multiple goals
(e.g. they can also convey task related information) they are a dominant place
to send general feedback (both positive and negative) which may help sustain
engagement of contributors as well as socialize them with the collaborating
group. Feedback messages were previously identified as types of leadership
behaviors on Wikipedia - transactional leadership (positive feedback) and
aversive leadership (negative feedback) [60]. Our results enhance these
findings by showing that feedback is preferentially provided via direct
messaging.

Moreover, our quantitative network analysis also confirmed that direct
communication impacts the effects of group work: high average volume of direct
messages was correlated with higher quality of Wikiprojects’ output. Thus our
results are in line with the so far scarce studies on effects of direct
messaging on Wikipedia’s collaboration: that articles benefit from their
contributors engaging in private messaging in addition to discussions on
articles [41], and that direct communication structure that allows linkages
across and within contributors from core and periphery relates to higher
quality of project output [47]. The results presented here go beyond these
previous findings by showing that high intensity of affective processes is
beneficial for effectiveness of online group collaboration. This result,
combined with previous studies on task related coordination, shows that online
groups, just like their offline counterparts, need to find a balance between
affiliation and instrumental processes within the group. Understanding the
interplay between affective and instrumental processes is important given that
even on a such task focused community as Wikipedia, personal likes and
dislikes may drive editing dynamics in a negative way (e.g. into edit
warring), wherein affiliation with particular others may be more important
than the quality of contributions [30].

Arguably, the most important and novel contribution of our paper, however, is
that direct communication structure that allows for complex information
integration within the group is related to higher quality of articles curated
by Wikiprojects. That is, in successful projects, a particular individual’s
contacts are relatively deterministic: she has a select group of relationships
with whom she tends to communicate much more often than with others. On the
other hand, in these successful projects, there is little determinism on the
global, group-wide scale: if a particular piece of information or a resource
is acquired by any member of the group, the next steps of its way across the
group are on average quite unpredictable. In this way the effective mode of
operation is that, locally, information is processed within circles of
individuals and globally, there are sufficient connections distributed over
the entire network to allow the information to spread rather than to be
captured by a limited set of individuals. The interviews further elaborate on
this result from network analysis: project members build strong trust and
expertise based ties by history of previous interactions, but they also have
multiple connections to others with different expertise through weaker
relations. Such weak ties can exist within and across projects, as evidenced
by our network analysis and interviews, respectively.

Our finding that global information integration (lack of a well determined
sink for information or a finite coordination center) is beneficial for group
effectiveness does not preclude the existence of a core of project
contributors. Indeed, the distributions of both the number of messages sent as
well as of degree centrality of Wikiprojects’ members tend to be highly skewed
and heavy-tailed, suggesting that there are members who communicate more
intensively than others and whom we might call “core”. What our results
suggest, however, is that in online collaboration groups the core should not
strive to be a pipeline for information and resource spread.

Specifically, globally integrated dynamics might be beneficial for more
complex tasks, such as upgrading an article to a high quality rank. [44] have
shown that the number of edits to articles within a projects’ scope is
positively related to existence of leadership behaviors such as dominance in
project talk page discussions (measured by betweenness centrality), which
might suggest that centralized communication is favorable for Wikiprojects.
However, mere adding of edits is a much simpler task than promoting an article
to featured status. Indeed, [36] have shown that promotion of articles to
lower ranks of quality requires more centralized leadership (as measured by
group degree centrality of direct communication networks) with less cohesive
connections (measured by average clustering coefficient) than promotion to FA
status. In this most complex task, cohesiveness was more important than
centralized leadership. Our results show that this difference might be due to
a larger press on global integration of information in such complex tasks. The
practical implication of this finding is that in management of online
collaboration, project members who are central in communication should strive
to maintain redundant ties with the periphery, and encourage links across the
periphery, so that the core of the group does not become a bottleneck in
information integration or resource mobilization.

Such redundant connectivity across specialized local groups might be important
specifically in online collaboration environments which are often
characterized by high fluidity in membership. In standard organizations,
maintenance of a skilled workforce can be achieved by, e.g. financial
incentives. Such solutions are not available in volunteer-based peer
production and this often results in high turnover of contributors. For
example, on Wikipedia, contributor career paths are volatile and
unpredictable: editors switch between various functions and roles as they gain
experience and self-select to favorite tasks. Some might take a long
“Wikibreak” or even leave the community for good. At the same time the global
proportions of functional roles are quite stable, implying that when
individuals abandon certain roles, others take their place spontaneously [3].
Our interviewees confirm that role uptake is organic and spontaneous. In such
a working environment, no person in the direct communication network should be
irreplaceable. Redundancy, not only in local, usually dense, ties but also in
those weak, wide-spanning connections is critical to maintain sufficient
levels of coordination in Wikiprojects, given the possibly fluctuating
membership. In effect, our results suggest that complex tasks undertaken by
online collaborating teams are benefited by communication structures that are
characterized by specificity in local connections together with wide-reaching,
integrative global links.

Such connectivity structure - and indeed the measures for effective
information - might be related to high social capital of a group: “the sum of
actual and potential resources embedded within, available through, and derived
from the network of relationships possessed by an individual or social unit”
[35, p. 243]. Social capital of a group is often defined in terms of bonding -
i.e. the cohesiveness of ties within a network. In offline teams an
established network of close relationships within a social system increases
commitment [16, 21] and a higher density of connections leads to a higher
likelihood of collective endeavors [43]. On Wikipedia, such social capital
brought to an article and accumulated by previous collaboration, results in
higher article quality [36]. Strong connections in a network of co-
contributions to sentences within an article - also a measure of bonding
capital - are also related to higher quality [32]. However, such capital is
different from bridging capital of individuals that can be accrued by
occupying a structural hole [24].

Bridging capital is highest for individuals when they are unique
intermediaries between parts of a network [24]. In offline studies on social
capital an important role in communication networks is played by the so-called
gatekeepers - a small number of key group members with expertise of how to
find relevant information within a system [52]. These gatekeepers are crucial
for the group to work efficiently and that crucial position gives them power
and control. Yet, for the group their uniqueness might be detrimental - they
are a bottleneck in group coordination. As discussed above, in online systems
that attempt complex tasks requiring specialized knowledge or skills, and
which are characterized by high fluidity in membership, gatekeepers could
prove disastrous: if they reduce their activity or simply are overwhelmed by
amount of work, the whole group task (i.e. FA candidate article) might fail.
Our results suggest that successful online teams are characterized by a
communication structure that lacks any such gatekeepers. Future research might
investigate whether complexity of the task (i.e. comparison between different
quality grades of articles), size of the team, as well as fluctuations in
membership impact what communication structures are effective for an online
peer production group.

##  7 Limitations

Without a doubt Wikipedia is one of the greatest success stories of
collaborative knowledge production with no tangible financial incentives. As
such it should be studied as this can reveal factors responsible for its
unique growth and success. However, for the very same reasons our results
should not be generalized to other similar platforms without additional
considerations. Wikipedia may differ in important and unique ways from an
average collaborative peer-production platform which can affect the scope of
generalizability of our results. Studies similar to ours but based on data
from other platforms need to be conducted in order to assess this.

Furthermore, in this study we focused exclusively on the structure of direct
communication networks. It may be of interest to replicate similar analyses
for other communication layers in the Wikipedia ecosystem, in particular
discussions on article talk pages in the so-called main namespace as well as
those on pages of individual Wikiprojects (project namespace). Moreover,
correlations between communication structures at these different layers may
provide additional insights and could be studied with methods developed for
multilayer networks [15].

Last but not least, we used time-aggregated networks and even though this
simplification was still enough to reveal several structural properties linked
to quality of output of Wikiprojects it did not capture the full richness of
our data. Therefore, it may be worthwhile to also study dynamic properties of
communication within Wikiproject. However, one should also note that for many
smaller projects available data may be too sparse to allow reliable dynamic
analyses leading to significantly smaller sample sizes than the one we
considered here.

##  8 Acknowledgments

This work was supported by the Polish National Science Centre through grant
2017/27/B/HS6/00626.

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