# Community Archetypes: An Empirical Framework for Guiding Research
Methodologies to Reflect User Experiences of Sense of Virtual Community

Gale H. Prinster<EMAIL_ADDRESS>0000-0002-7018-8657 University of
Colorado BoulderBoulderColoradoUSA80304 ,  C. Estelle Smith
<EMAIL_ADDRESS>0000-0002-4981-7105 Colorado School of
MinesGoldenCOUSA80401 ,  Chenhao Tan  0000-0002-3981-2116 University of
ChicagoChicagoIllinoisUSA80304<EMAIL_ADDRESS>and  Brian C. Keegan
0000-0002-7793-398X University of Colorado BoulderBoulderColoradoUSA80304
<EMAIL_ADDRESS>
(2018)

###### Abstract.

Humans need a sense of community (SOC), and social media platforms afford
opportunities to address this need by providing users with a sense of virtual
community (SOVC). This paper explores SOVC on Reddit and is motivated by two
goals: (1) providing researchers with an excellent resource for methodological
decisions in studies of Reddit communities; and (2) creating the foundation
for a new class of research methods and community support tools that reflect
users’ experiences of SOVC. To ensure that methods are respectfully and
ethically designed in service and accountability to impacted communities, our
work takes a qualitative, community-centered approach by engaging with two key
stakeholder groups. First, we interviewed 21 researchers to understand how
they study “community” on Reddit. Second, we surveyed 12 subreddits to gain
insight into user experiences of SOVC. Results show that some research methods
can broadly reflect users’ SOVC regardless of the topic or type of subreddit.
However, user responses also evidenced the existence of five distinct
Community Archetypes: Topical Q&A, Learning & Perspective Broadening, Social
Support, Content Generation, and Affiliation with an Entity. We offer the
Community Archetypes framework to support future work in designing methods
that align more closely with user experiences of SOVC and to create community
support tools that can meaningfully nourish the human need for SOC/SOVC in our
modern world.

Sense of community, sense of virtual community, online communities, community
support, Reddit, survey, interview, data science, users, researchers

††copyright: acmcopyright††journalyear: 2018††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation emai; June 03–05, 2018; Woodstock, NY††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Human-centered computing Empirical
studies in HCI

##  1\. Introduction

For decades, concerning trends point toward the degradation of in-person
communities and a growing epidemic of loneliness (Twenge et al., 2019).
Concurrently, participation in online communities has exploded, with rapidly
evolving moderation affordances (Bajpai et al., 2022) and styles (Seering et
al., 2022), rules and norms (Fiesler et al., 2018; Frey et al., 2022;
Chandrasekharan et al., 2018), and user groups who may or may not be connected
offline (Zhang et al., 2011; Smith et al., 2020a). For example, people might
not talk to neighbors like they used to, but they might also go on Nextdoor or
their city’s subreddit to observe neighbors’ discussions. Academics might opt
to work from home more frequently and miss out on water cooler chat, but check
Twitter twice an hour to catch the latest hot takes. People suffering from
stigmatized illness may never speak a word of it at the office, later going
home to an intimate Zoom chat hosted by a private Facebook group for patients.

Examples like these make it clear that how modern humans participate in
communities—and consequently, how we experience a Sense of Community (SOC) or
Sense of Virtual Community (SOVC)—have shifted over time across different
geographical, cultural, and sociotechnical contexts. Studying how people
experience SOC/SOVC is meaningful because it is a deep and unavoidable human
need. The access to or lack of community has profound psychological and
material impacts on people’s lives. For example, SOC impacts life satisfaction
(Prezza and Costantini, 1998), well-being (Chavis and Newbrough, 1986),
perceived safety (Perkins and Taylor, 1996), problem solving (Bachrach and
Zautra, 1985), and social or political participation (Obst et al., 2002).
Considerations like these underpin the importance of improving our scientific
understanding of: how we develop SOC/SOVC; how we can best understand,
measure, and support the formation of healthy communities; and how SOC/SOVC
can either nourish our needs or cause damage, depending on the circumstances.

This paper investigates SOVC on Reddit, a platform that serves millions of
users and communities worldwide and has attracted growing research attention
(Proferes et al., 2021).111This paper assumes familiarity with Reddit-specific
terminologies, however supplemental material section 1 includes detailed
descriptions of all terms, such as “subreddit,” “thread,” “Original Poster”
(OP), etc. for reference. Our work is motivated by two overarching goals: (1)
to provide future researchers with an excellent resource for thinking through
major methodological decisions in studies of Reddit communities; and (2) to
provide the foundation for a new class of data science techniques and data-
driven community support tools. Regarding the first goal, studies that aim to
foster healthy online communities should ensure that community spaces and
members are properly identified, and that researchers’ selected methods align
with community members’ experiences and values. Although much prior work
refers to each subreddit as its own community, recent work suggests that
individual subreddits are not the best “units” of community. Most users report
greater SOVC across multiple subreddits (Smith et al., 2022), and each
subreddit in these sets may meet different needs (Hwang and Foote, 2021;
TeBlunthuis and Hill, 2022). If researchers assume that each subreddit is
individually experienced as a community, they risk inaccurate conclusions, or
they may miss key aspects of the larger picture of user experience. Providing
better ways to identify community spaces will enable researchers to better
support the human need for SOC.

Figure 1. Summary of Research Approach.

Drawing upon prior work in organizational psychology and HCI, our second goal
aims to advance our ability to measure and predict SOVC. Inspired by recent
work on Twitch (Kairam et al., 2022), we are motivated by the promising
opportunity to design and evaluate human-centered data science techniques.
However, academic constructions of SOC/SOVC do not always align with the lived
experiences of community members (Pretty et al., 1996; Puddifoot, 1995; Rapley
and Pretty, 1999). Consequently, “the problems raised by the use of the notion
of community are increased if the gap between academic and lay meaning is not
taken into account.” (Mannarini and Fedi, 2009) To reduce this gap, we took an
empirical, qualitative, and community-centered approach to developing a
guiding framework for new methods. We engaged with two stakeholder
groups–Reddit researchers and Reddit users–to ask:

RQ1::

How do researchers conceptualize and operationalize virtual community on
Reddit?

RQ2::

How do Reddit users experience a sense of virtual community in their use of
the platform? And how does this vary across communities?

RQ3::

How can we operationalize important community-related concepts to better align
researchers’ methods with users’ experiences?

Figure 1 overviews our research approach. We conducted interviews with 21
researchers to address RQ1 and found that they typically view subreddits as
topical affinity groups; not all subreddits can or should be described as
communities. Rather, certain types of interactivity, membership boundaries,
homogeneity, and norm enforcement may distinguish non-community affinity
groups from communities (Table 3). For RQ2, we surveyed 12 subreddits. User
responses confirmed that their SOVC formation is broadly tied to how
researchers tend to study communities. However, users expect nuanced forms of
community activity in different types of subreddits; their experiences of SOVC
arise primarily when these specialized activity forms are happening well and
frequently. Thus our data suggest the existence of five underlying Community
Archetypes distinguished by particular user roles and content patterns (Table
4). Our discussion synthesizes these two sets of results to address RQ3. Some
methods for measuring community structures and SOC/SOVC can be used broadly,
no matter the type of subreddit, whereas others must be designed according to
which archetype(s) the subreddit(s) embody. We provide examples to illustrate
these concepts within existing subreddits and studies. Finally, we offer a
road map for future work to apply the Community Archetypes framework and to
create community support tools that can meaningfully nourish the human need
for SOC/SOVC in our modern world.

##  2\. Related Literature

###  2.1. The history of sense of community research

##### Organizational and community psychology

Long before the emergence of the Internet, researchers in organizational and
community psychology devised varied ways of assessing the structural elements
that define geographical communities, and for measuring sense of community
(SOC), examining factors like common fate (Campbell, 1958), supportive climate
(Doolittle and Macdonald, 1978), and the amount of time one expects to stay
(Glynn, 1981; Gusfield, 1975). As research advanced, definitions became less
attached to geographical centers and more inclusive of relational communities
denoted by facets like interpersonal connection, interests, and hobbies
(Gusfield, 1975; McMillan and Chavis, 1986; Ashforth and Mael, 1989; Lickel et
al., 2001). One prominent theory of SOC by McMillan and Chavis (1986) proposes
four elements: membership, influence, fulfillment of needs, and shared
emotional connection (McMillan and Chavis, 1986). This theory can be applied
to geographical or relational communities and remains popular today, with
occasional uses in HCI (e.g., (Kim et al., 2022)).

##### Sociology and communication studies

The fields of sociology and communication studies have also embraced a
relational conception of communities. For example, Benedict Anderson examined
national identity as a community builder. Although all citizens do not know
each other personally, they may nonetheless view themselves as members in the
“imagined community” of their nation (Anderson, Benedict, 1983). This concept
of imagined communities provides a helpful lens for online community scholars
to understand how users can build communities online without either
geographical closeness or personal connections. Sociologists in the 90s have
also examined the weak and strong ties that users made both off- and online
and found that ties still existed online, despite users not having any
geographical proximity (Wellman, Barry et al., 1999). Communications scholars
have also operated with the idea that communication is a major indicator and
strengthener of communities both off- and online since at least the 90s
(Rothenbuhler et al., 1996; Shah et al., 2001). Social media may simply be the
next step in humans’ millennia-long history in mediated, text-based
communities rather than a fundamentally “new” innovation in the course of
human history (Standage, Tom, 2015).

##### Measuring SOC/SOVC

While decades of scholarship across different academic disciplines grappled
with what a community is (as does our present work in this paper), people seem
to reliably know when they are a part of communities and when they are not
(Lickel et al., 2001; Sarason, 1974; Anderson, 2006), allowing researchers to
use their cognitive evaluations to measure SOC using psychometric
instrumentation (Hoyle, 2013). While early scales were developed for offline
communities, researchers in the early 2000s began to investigate how online
communities may differ from offline and to create new psychometric measures
for SOVC (Koh et al., 2003; Blanchard and Markus, 2004).222For one example,
people in online communities are not embodied but are represented by online
profiles; consequently issues of purposefully creating an identity, and making
identifications of others, are possibly more important to SOVC, relative to
in-person communities, where much more information is naturally communicated
about identity by physical appearance and proximity (Blanchard and Markus,
2004). One critical issue with these preliminary scales is that they conflated
the cognitive evaluation of SOVC with a variety of psychological factors that
cause SOVC to develop. Therefore, a more modern approach seeks to distinguish
antecedents to SOVC from the experience of SOVC itself, as well as from
specific outcomes of SOVC. For example, similarity, interactivity, membership
boundaries, common goals, and history of interactions can be distinguished as
individual psychological constructs which all precede SOVC. Similarly,
identification with the group, commitment to the group, group satisfaction,
and centrality are all distinct, measurable psychological outcomes, when SOVC
exists (Blanchard et al., 2020). Because of the increasing prevalence of
automated bots as social actors in online communities (Seering et al., 2020,
2018), recent work also introduced bot governance as a new SOVC antecedent
(Smith et al., 2022).

##### Analogizing aspects of behavioral trace data as SOVC antecedents

Our work’s theoretical basis benefits from the recent move in organizational
psychology to distinguish antecedents from SOVC and its outcomes. In
particular, SOVC is an internal and highly variable user experience; it cannot
be directly measured via behavioral trace data. However, we aim to understand
how users’ internal experiences of antecedents to SOVC may relate to empirical
measures that can be calculated from behavioral trace data. If we can develop
data science-based analogs of known SOVC antecedents, we may be able to
effectively predict SOVC. As described in section 3.2.3, this work uses four
of the most well-studied SOVC antecedents to structure our interviews with
researchers: interactivity, homogeneity, norm enforcement, and membership
boundaries.

###  2.2. Bridging from organizational psychology to HCI

HCI and social computing have a long and rich history of studying online
communities. However, this history has largely developed adjacent to the
literature in organizational psychology. Studies of platforms like Facebook
(Zhang et al., 2011), YouTube (Rotman et al., 2009), Twitter (Blight et al.,
2017; Ricoy and Feliz, 2016; Java et al., 2007), Twitch (Kairam et al., 2022),
Discord (Jiang et al., 2019), Instagram (Blight et al., 2017; Thomas et al.,
2020), TikTok (Bonifazi et al., 2022), and Wikipedia (Baytiyeh and Pfaffman,
2010; Pentzold, 2011) have explored whether and how users experience community
and sense of community on the platform. However, other papers use “community”
without specifically justifying it. Prior work points to the importance of
triangulating qualitative and quantitative data to develop deeper
understandings of online communities (Rotman et al., 2009; Kairam et al.,
2022). As we aim to develop more precise measures and techniques,
incorporating concepts across organizational psychology, HCI, and users’
subjective perspectives will improve our ability to delineate and study what
users experience as community and how to build community support tools—e.g.,
moderation policies and toolkits (Zhang et al., 2020; Schneider et al., 2021),
community-in-the-loop algorithms (Smith et al., 2020c) and governance bots
(Smith et al., 2022). Therefore, we will next summarize four main ways that
the HCI literature has previously delineated online communities: platform
affordances; shared interests; common vernacular; and interactivity.

Platform affordances. Researchers often designate communities by using
particular platform affordances. For example, social network analysis analyzes
connections between users who have “added” or “followed” each other. Users are
considered “nodes” or “vertices” in the network, connections between them are
“edges,” and researchers detect communities based on communication patterns,
edge weights, etc. (Fortunato and Hric, 2016) Some studies use hashtags to
indicate communities, such as Black Twitter (Klassen et al., 2021; Sharma,
2013) or communities of users on Tumbler (Griffith and Stein, 2021) or
Instagram (Andalibi et al., 2017; McEwan, 2016) who use the hashtag
#depression. On other platforms, communities are designated by more concrete
digital “containers” that users elect to join, e.g., Facebook Groups (Zhang et
al., 2013), online health blogs (Smith et al., 2020a; Levonian et al., 2021),
or Twitch channels (Hamilton et al., 2014). In line with this “container”
view, Reddit’s website copy and many papers (e.g., (Weld et al., 2021; Tan,
2018; Cagle and Herndl, 2019; Kou and Gray, 2017; Prakasam and Huxtable-
Thomas, 2021; An et al., 2019; Potts et al., 2019)) refer to each subreddit as
its own community. Other papers don’t mention community (e.g., (Hermes and
Stoete, 2019; Kou et al., 2017)) or identify families of subreddits (Tan,
2018) with overlapping membership (TeBlunthuis and Hill, 2022). Moreover, most
users do not perceive individual subreddits as the best “units” of community,
instead reporting greater SOVC across multiple subreddits (Smith et al.,
2022), each of which may meet different needs (Hwang and Foote, 2021). These
studies suggest that users’ SOVC often extends beyond the boundaries of
digital containers, and that research will benefit from more fine-grained
delineations.

Shared interests. Classic works in HCI denote online community as a group of
users with a shared interest or goal (Preece, 2000; Bieber et al., 2002; Kraut
and Resnick, 2012; McEwan, 2016). If users simply exist in the same online
space with nothing in common, it is unlikely that they will form community-
like connections. However, when users have a common interest or goal driving
them together, community bonds are much more likely to form. Shared interests
can be such a strongly unifying force that users may attempt to migrate their
communities to new platforms when current platforms are insufficient or
threatened. For example, communities of fandom users have often migrated
across platforms (Fiesler and Dym, 2020) and hundreds of thousands of Twitter
users rapidly migrated to Mastodon after the 2022 Musk acquisition (Keegan,
2022).

Common vernacular. Another approach from natural language processing that is
commonly used in HCI focuses on the common vernacular that users employ when
interacting in online spaces. Use and familiarity with specific language gives
users the sense that they are part of an “in-group.” (Johnstone, 2008)
Therefore, the more that specific forms of language, discursive conventions,
or terminologies exist in an online space, the more likely it is to be a
community (Johnstone, 2008; Danescu-Niculescu-Mizil et al., 2013).

Interactivity. Finally, HCI researchers have focused broadly on interactivity:
the more activity that exists in a group of users, the stronger the community
(Preece, 2000; McEwan, 2016; Danescu-Niculescu-Mizil et al., 2013). Some HCI
work considers interactivity as a mechanism for designating user groups, such
as core v.s. peripheral community members (Bryant et al., 2005; Halfaker and
Taraborelli, 2013), old-timers v.s. newcomers (Choi et al., 2010; Yang et al.,
2017), or differing user roles (Zhang et al., 2022; Müller-Birn et al., 2015;
Jin et al., 2007). Diverging from considerations of interactivity as a
determination of membership or as an antecedent to SOVC however, much work
refers to the type and volume of interaction instead as a success metric. For
example, Cunha et al. describe four success measures: (1) growth in the number
of members; (2) retention of members; (3) long-term survival of the community;
(4) volume of activity (Cunha et al., 2019). Many studies take a similar
approach, with community size or survival as major targets of prediction.
Recent work has also sought more refined metrics, such as measuring pro-social
behaviors, rather than identifying all interaction as positive, community-
oriented behavior (Bao et al., 2021). This prior work provides important
insights, however such HCI methods can be refined through: (1) a more coherent
alignment with advances in organizational psychology theory that distinguish
between antecedents, SOVC, and outcomes of SOVC; and (2) a community-centered
approach that reflects users’ actual experiences of SOVC in the design of
research methods. These considerations motivate our research questions and
goal of creating an empirical framework to guide future community data science
techniques.

##  3\. Methods

Figure 1 summarizes our research approach. We interviewed 21 researchers to
understand how they conceptualize and operationalize community on Reddit. We
also observed and surveyed 12 different subreddits to gain insight into users’
experiences of SOVC. We then used Grounded Theory Method (Muller, 2014) to
analyze the data. This study was reviewed and deemed exempt by our
institutional IRB office. We begin with a positionality statement to disclose
how our own personal identities have influenced our study before describing
our methods in detail.

###  3.1. Positionality & Ethical Stance

We view the increasing research attention on Reddit (Proferes et al., 2021) as
an urgent opportunity to ensure that new methods are conscientiously,
respectfully, and ethically designed for purposes of service and
accountability to communities rather than extraction or manipulation. All
authors are regular Reddit users.333Three team members, including the first
author and two assistants listed in the acknowledgements, were originally
recruited to join this research project through a subreddit post.
Consequently, we are influenced by our own on- and offline experiences related
to our usage of Reddit. Most subreddits were selected somewhat randomly
according to criteria we will soon describe. However, our own membership and
prior engagement with moderators contributed to our ability to successfully
recruit a few of the subreddits in Table 2. For example, the second author is
a member of S8. Due to research and personal interests in mental health, she
felt it vital to include at least one mental health subreddit. Given an
otherwise arbitrary set of candidates, it was most sensible to recruit a
familiar subreddit. As academic researchers with values and ethics oriented
toward equity and inclusion, we also felt it was vital to recruit subreddits
for underrepresented populations (e.g., S6, S7, S11), even though we do not
identify as members. We acknowledge that a complex set of privacy, safety, and
equity concerns must be addressed in the development of new methods that can
measure and impact human communities. Our research team values collaborative
efforts with moderation teams and users, and we suggest that future
researchers continue to work directly with users and communities, especially
ensuring that such research efforts are allowed and desired.

###  3.2. Interviews with Reddit Researchers

During our literature review, we observed that most papers on Reddit seem to
be based on unstated assumptions about subreddits existing as communities.
However, literature review does not enable us to make claims about Reddit
researchers’ actual mental models of community, how these impact selected
methodologies, or other unwritten limitations or opportunities they have
considered. We chose to interview Reddit researchers in order to ensure the
empirical validity and rigor of our claims and to directly compare researcher
and user perspectives.

####  3.2.1. Recruitment

A systematic review of 727 Reddit publications from 2010–2020 shows that 57.4%
are first-authored by researchers in Computer Science, Math, and Engineering
units, with disciplines like the Humanities (18.2%), Medicine & Health (9.6%),
and Social Science (7.8%) accounting for most of the rest. 482 (66.3%) papers
used quantitative methods (primarily computational techniques such as machine
learning and natural language processing), 183 (25.2%) used qualitative
methods (e.g., discourse analysis, ethnography, grounded theory), and 56
(7.7%) used mixed methods (Proferes et al., 2021). We manually collated a list
of email addresses for all first and last authors in this set of papers, on
the assumption that first and last authors are most influential in methods
decisions. We emailed the list a recruitment message and screening survey and
selected researchers across disciplines, methods, and seniority, purposefully
ensuring representation of as balanced and diverse a collection of voices as
possible. We continued recruiting until we reached data saturation–i.e. new
interviews were no longer providing new information (Muller, 2014). Table 1
summarizes participant information of 21 participants: 13 faculty, 1 postdoc,
2 industry researchers, and 4 Ph.D. students (2 of whom also indicated
industry researcher). 7 participants (33.3%) self-identified Computer Science,
Math, and Engineering (including HCI) as their primary discipline, 6 (28.6%)
Social Science, 5 (23.4%) Humanities, and 2 (9.5%) Medicine & Health. Of
these, 6 primarily used quantitative methods, 7 qualitative, and 8 mixed. 14
participants (66.7%) identified as white, 6 as Asian (28.6%), and 1 as Middle
Eastern. 16 participants (76.2%) were at a US institution, 4 in Europe (19%),
and 1 in Asia. 13 identified as male (including 1 trans) (61.9%), and 8 female
(38.1%).

ID | Career Stage | Discipline | Methods | Reddit User?
---|---|---|---|---
R1 | Postdoc | CS, Math, Eng. | Quant | prior to research
R2 | PhD Student/Industry Researcher | CS, Math, Eng. | Mixed | prior to research
R3* | Industry Researcher | CS, Math, Eng. | Qual | prior to research
R4 | Faculty | CS, Math, Eng. | Mixed | prior to research
R5* | Faculty | CS, Math, Eng. | Qual | prior to research
R6 | PhD Student | CS, Math, Eng. | Mixed | since research
R7 | Industry Researcher | CS, Math, Eng. | Quant | prior to research
R8 | Faculty | CS, Math, Eng. | Quant | never
R9 | Faculty | Humanities | Mixed | prior to research
R10 | Faculty | Humanities | Qual | since research
R11 | Faculty | Humanities | Qual | prior to research
R12 | Faculty | Humanities | Mixed | prior to research
R13 | Faculty | Humanities | Qual | never
R14 | Faculty | Medicine & Health | Mixed | prior to research
R15 | PhD Student/Industry Researcher | Medicine & Health | Quant | prior to research
R16 | Faculty | Social Science | Qual | since research
R17* | Faculty | Social Science | Qual | prior to research
R18 | Faculty | Social Science | Mixed | prior to research
R19 | Faculty | Social Science | Mixed | prior to research
R20 | PhD Student | Social Science | Quant | prior to research
R21 | Faculty | Social Science | Quant | prior to research
Table 1. Summary of Reddit Researcher Participants. Most participants
indicated that they have published between 1–5 papers on Reddit; * indicates
researchers who have published >5 papers on Reddit.

####  3.2.2. Interview Protocol

Interviews were conducted remotely over Zoom by the two co-first authors.
Interviews ranged from 43 to 73 minutes long and lasted an average length of
56 minutes. Interviews had two parts. The first used a semi-structured
approach with questions regarding the researchers’ mental model of community,
what a strong community might look like, specific
definitions/operationalizations of community, and methods decisions in the
interviewees’ work. (The complete set of interview questions is available in
supplemental section 3.) The second part used a talk-aloud structure (similar
to (Fisler and Castro, 2017; Sripada and Taxali, 2020)) in which we screen-
shared slides, asking participants to reflect on four known antecedents to
SOVC (interactivity, membership boundaries, homogeneity, and norm
enforcement). We defined each antecedent on-screen and asked how researchers
might measure it using any qualitative, quantitative, or mixed methods. A
final slide asked about any additional variables or ideas we had not asked
about. Next, we describe why we chose these four particular antecedents.

####  3.2.3. Selected antecedents to sense of virtual community

Organizational psychologists have demonstrated interactivity, membership
boundaries, homogeneity, and norm enforcement to be fundamental antecedents to
the formation of SOVC (Blanchard et al., 2020). This list is not exhaustive,
however, it does provide a strong theoretical bedrock.444See supplemental
section 2 for in-depth discussion of the theoretical bases for these
antecedents. We opted for more generic rather than overly-specified constructs
to allow for the broadest possible interpretations of user behavior. For
example, we asked about interactivity rather than support exchange or
information sharing, since both of those are more specific forms of
interactivity which may or may not be required for SOVC in different virtual
contexts. We displayed the following four definitions to participants:

Interactivity::

The degree to which users are interacting within the community.

Membership Boundaries::

The degree to which users can be considered members of a community, v.s. not
being considered members of a community.

Homogeneity::

The degree of similarity of users related to their values, attitudes, goals,
or other personal characteristics (e.g., demographics, personality traits,
etc.).

Norm Enforcement::

The degree to which norms are enforced within the community.

####  3.2.4. Interview limitations

One limitation is that we could not interview researchers who directly studied
the same subreddits we surveyed, however, we addressed this limitation by
ensuring that diverse subreddits were included in our surveys, including the
same basic types studied by prior researchers. Standard interview limitations
apply: (1) Our small sample of Reddit researchers may not be representative of
all researchers; and (2) We asked researchers to describe prior work, some of
which was completed years ago, and recall of past events is imperfect.

###  3.3. Surveys of Reddit Users

ID | Description | Size | $N$ | Community Archetypes
---|---|---|---|---
S1 | Posts with niche absurdist humor style | 10-50K | 16 | ContentGen
S2 | Corrections of poor scientific reporting | 10-50K | 28 | Learning
S3 | Discuss fundamental elements of music | 100K-1M | 77 | Q&A; Learning
S4 | Help with job application materials | 100K-1M | 48 | Q&A; ContentGen
S5 | Support for pregnancy-related issues | <10k | 39 | Support
S6 | POC/allies discuss race/intersectionality | 10-50K | 41 | Learning; Support
S7 | Discuss ironic matters regarding race | <10k | 21 | Learning
S8 | Support for a mental health issue | 50-100K | 13 | Support
S9 | A US university and geographical area | 10-50K | 43 | Affiliation; Q&A
S10 | Q&A with experts on an academic topic | 1-5+M | 25 | Q&A; Learning
S11 | Screenshots of social media posts from POC users | 1-5+M | 250 | ContentGen; Learning; Support
S12 | Learning about indigenous spiritual beliefs | 50-100K | 7 | Learning; Support
Table 2. Descriptions of subreddits included in this study. Abbreviations
included in the Community Archetypes column are fully specified in Section
5.2; Archetypes are listed in order of highest to lowest relevance. Note:
“POC” is an acronym for People of Color.

Surveys were run in collaboration with the moderation teams of twelve
different subreddits (See Table 2).555This paper uses the same survey data
collected in (Smith et al., 2022), which performs a purely statistical
analysis on Likert rating data for psychometric scale validation. The present
paper uses and analyzes qualitative data from the surveys in order to
understand new dimensions of sense of virtual community. Complete survey text
is available at https://bit.ly/gov-bots_pdf. In addition to the reasons
mentioned above in sec. 3.1), we selected subreddits using the following
criteria. We avoided NSFW (Not Safe For Work) and hate- or abuse-centered
subreddits for two main reasons: (1) preserving the safety of our research
team; (2) focusing our research efforts on pro-social communities—i.e., the
types of communities that we hope this work can support. We aimed to capture
as diverse a collection of subreddits as possible, with high variability in
size, topic, and observable community activities (e.g., weekly or daily
threads, annual customs, special AMA events, different types of user flairs or
threads, etc.) to ensure that our results are as broadly applicable across
Reddit as possible. We also chose active subreddits in which surveys were
allowed and in which users appear to post every day (rather than those with
obviously inactive userbases) to maximize response rates. We posted surveys to
each subreddit using a method preferred by each moderation team, including
strategies such as:

  * •

Moderators create a post about the survey and pin it to the top of the
subreddit.

  * •

Researchers post the survey in a weekly thread for surveys and/or personal
promotion (and mods “approve” or highlight the post).

  * •

Researchers post the survey to a thread specifically for surveys in that
subreddit.

Survey links were posted from October through December of 2021, and each link
was open to responses for a maximum of two weeks; some surveys were closed
before that time due to satisfactory response rates. Survey participants could
provide their email to opt into a drawing for $10 eGift Cards. In addition to
basic demographics, surveys included three free response questions that
provide insight into users’ experience of SOVC with an eye toward RQ2 and RQ3:

  * •

What motivates you to visit r/[subreddit name]?

  * •

What makes r/[subreddit name] feel like a community to you?

  * •

What (if anything) could be done to improve your experience in r/[subreddit
name]?

####  3.3.1. Survey participants

We manually inspected all survey responses and eliminated those which
contained nonsensical or copy/pasted free responses. In total, we collected
608 valid responses across the twelve subreddits in Table 2. Respondents skew
male (58.8%), white (61.5%), residing in North America (81%), and relatively
young (50.1% of respondents selected 25–34 years of age, and another 21.3%
18–24); these demographic trends are broadly consistent across the Reddit
userbase.

####  3.3.2. Survey limitations

The most important survey limitation lies in our selection of subreddits.
Since every subreddit can have unique rules, norms, interaction styles, bots,
etc., it is impossible to select a perfectly representative set.For instance,
many subreddits do not allow posts on unrelated topics, including research
advertisement, thus a systematic difference might exist between the types of
communities that allow research v.s. those that do not. (For example, perhaps
norm enforcement is more tightly coupled with SOVC in subreddits that do not
allow research.) We intentionally selected diverse subreddits but may have
inadvertently missed certain types of communities; future work should
periodically revisit our research questions to enrich and update our
understanding of the community types we will describe, as well as appending
new types we may have missed or which may emerge in the future.

In order to encourage respondents to finish the survey, we limited survey
length and did not ask many detailed questions—e.g., about each antecedent
individually. We note that respondents provided responses of varying length
and detail and that these responses organically included information related
to antecedents, SOVC, and other community-related concepts. Standard survey
limitations apply, including the possibility of misalignment between self-
reported data and actual behaviors and opt-in selection bias. Our survey
advertisement was posted with different levels of visibility in different
subreddits, ranging from high visibility (e.g., pinned post) to lower
visibility (e.g., survey thread). In general, people who elected to take the
survey may or may not be representative of the user base of each subreddit.
However, respondents from subreddits where there was lower visibility of the
recruitment post might be less representative due to finding our study in a
more niche way, while respondents who found the survey in a more visible
location might be more representative.

###  3.4. Observation of subreddits

We visited every subreddit in our sample to observe how users are currently
interacting. Rather than programmatically collecting top-voted posts, we
scrolled through at least 20 recent posts, along with the comments on those
posts, in their order of appearance on the user interface. This technique
provides us with a sample of the data near the time when surveys were
collected and avoids biasing our understanding of the content toward all-time
popular posts (which may not accurately capture the day-to-day diversity of
content and interactions on the subreddit). We wrote memos and noted the types
of topics users wrote about and whether there were any particular formats or
patterns in the content.666We considered completing a formal, systematic
content analysis of posts and comments, however this degree of precision is
not required for our research questions, which are better addressed through
analysis of survey responses. We also conducted a preliminary analysis of the
types of the rules present in each subreddit to inform our observations; the
details of this tangential analysis are included in the supplemental
materials.

###  3.5. Analysis

We used Grounded Theory Method (GTM) (Muller, 2014) conducted virtually by the
two co-first authors. For the interviews, we used transcripts automatically
generated by Zoom as a basis, and then corrected errors while re-listening to
recordings. We wrote memos and notes during data collection and transcript
correction in order to capture our immediate reflections and ideas about the
data. Sensitized by concepts from prior literature, while also allowing new
concepts to emerge inductively from the data (Charmaz, 2014), we then
systematically open-coded all interview transcripts. Over the course of
approximately two months, the two co-first authors met weekly to analyze and
collaboratively cluster all open codes, continuously discussing any
uncertainties or disagreements until both authors agreed upon a final
clustering arrangement and placement for each individual code. We conducted
this affinity mapping using Miro software to first cluster all interview open
codes into major axial themes, each with several sub-themes. Similarly, we
next open-coded all survey responses and clustered the survey codes in a
separate space on the same Miro board. After clustering the interview and
survey codes independently, we then iterated upon our clusters and rearranged
the Miro board to highlight overlapping ideas between the two groups. Overall,
this process generated a total of 1,149 open codes, which we organized into 45
major axial themes with 83 sub-themes. We report on approximately half of
these themes/codes that directly address our research questions on SOVC by
synthesizing them within the framework presented by this paper.

Importantly, a variety of users’ ideas generated distinct clusters that had
not emerged prominently in our mapping of researchers’ data—thus leading to
our conceptualization of these clusters as five “Community Archetypes” (see
Table 4). Upon closer inspection of these clusters and the participant data
they were derived from, our discussions inductively revealed that there were
clear user groups specific to each archetype for which we assigned the title
of “roles;” these roles directly led to the interaction patterns which
resulted in users’ experiences of SOVC. Finally, we cross-referenced the
archetype clusters with our subreddit observations in order to assign each
subreddit in our sample to one or more archetypes (see Table 2). The two co-
first authors conducted every step of this analytical work together, and then
discussed and edited our presentation of results with all authors for clarity
and concision.777We collected consent to participate in this research
according to our exempted IRB protocol, however we did not request permission
in advance from participants to make interview transcripts and survey data
publicly available. Our researcher data would be impossible to de-identify
given the nature of our questions. If future researchers are interested to re-
analyze these data, our research team will gladly provide assistance in
submitting appropriate IRB protocols for re-use of the data.

##  4\. RQ1: Researcher conceptions of community & how to measure it

This section seeks to address two central lines of inquiry: (Section 4.1) When
can researchers understand whether a particular subreddit is experienced as a
community or not?; (Section 4.2) What types of behavioral data traces may
indicate how and whether users are experiencing SOVC (or not)—either within a
particular subreddit (or across Reddit more broadly)?

###  4.1. Researcher conceptions of Reddit communities

Despite frequently calling subreddits “communities,” our participants have not
specifically analyzed whether users experience subreddits as communities. We
asked whether they draw upon any literature on the theory or praxis of online
communities to inform their understanding and methodological approaches; 8 out
of 21 participants (1 in computer science, 3 in humanities, and 4 in social
science) mentioned at least one paper (e.g., (Preece, 2000)). However, in most
cases, researchers have made a certain set of assumptions to frame and
constrain their studies, and have used anywhere from one to thousands of
subreddits as a source of data for their projects. Although some researchers
preferred terms like “forum” or “discourse/linguistic setting”, consensus
across participants’ responses indicates that it is reasonable for researchers
to refer to all subs as “topical affinity groups” or “common interest groups.”

Moreover, most researchers made it clear that they do not believe all
subreddits are actually communities. As R12 noted, subreddits are simply the
“neutral descriptor of the actual digital space.” In order to qualify as a
community, there must exist some additional set of criteria beyond the content
alone, such as: participation in conversations or communication networks,
formation of meaningful relationships between users (even occasionally
extending into off-platform activities), users’ sense of belonging in what
they perceive as a community space, and willingness to act on shared values.
Many researchers disagreed with a binary approach to whether a subreddit is a
community, instead favoring a spectrum model where “you can have more or less
[community] and there’s no threshold” (R21). Others provided further nuance,
pointing out that a subreddit could be one big community, but it might also
contain “pocket communities” (R12)—i.e., smaller communities within larger
spaces which do not encompass every user. Although researchers agreed that
deciding how to measure or delineate community is quite challenging and
imperfect, the next section offers synthesized insights toward considering and
assessing SOVC antecedents.

Construct | Assessment Strategies
---|---
Interactivity (sec. 4.2.1) |  | • Volume of observable interaction
---
• Degree of conversation occurring
Membership Boundaries (sec. 4.2.2) |  | • Tiers of membership
---
• Prolonged participation
• Linguistic membership boundaries
Homogeneity (sec. 4.2.3) |  | • Demographic
---
• Situational
• Goals or values
• Linguistic similarity
Norm Enforcement (sec. 4.2.4) |  | • Vertical moderation (by moderators)
---
• Horizontal norm enforcement (by users)
Table 3. Assessment strategies for antecedent constructs to sense of virtual
community.

###  4.2. Researchers’ strategies for assessing SOVC antecedents

Here, we summarize researchers’ suggested strategies for understanding and
measuring four antecedent constructs to SOVC: interactivity, membership,
homogeneity, and norm enforcement.Although this portion of results is largely
recapitulating known methods, it should function as a compact and useful
reference point for researchers who would like to design methods in more
conscientious alignment with the Community Archetypes we will soon introduce
in section 5.

####  4.2.1. Interactivity

We identified two major ways that researchers can quantitatively
operationalize interactivity, the first of which is more common in the
literature despite being less relevant to users’ experience of SOVC than the
second:

  1. (1)

Volume of observable interaction: “A community is nothing without the
interaction of the users.” (R6) In line with this, researchers often measure
the level at which users participate—e.g., the aggregate volume, rate, and
topic matter of posts, comments, and votes. An important caveat is that the
volume of interactivity alone is an overly blunt instrument. A certain level
of interaction is necessary, yet insufficient, to indicate SOVC.

  2. (2)

Degree of conversation: Researchers agree that there must be “some robust,
sustainable form of communication” (R16) in order for a subreddit to qualify
as a community. People participating in communities are more interactive and
discursive than their non-community counterparts; the way to measure that
difference is to look for people exchanging back and forth, rather than
counting individual comments without interaction between commenters.
Researchers should look to identify nested comments and/or repeated user
interactions across multiple comments.

####  4.2.2. Membership Boundaries

Deciding how to count users as members of a community, versus those who are
not, is often a pivotal methods decision with important implications for
results.

  1. (1)

Membership tiers: Participants often assign users to tiers based on: the
amount of individual actions taken by the user (from complete inaction
(lurking) up to moderating or engaging heavily with the moderation team to
advocate for change); the number of posts, comments, and/or amount of time a
user spends on the subreddit; or directly asking how users view their position
in the community (e.g., in surveys, interviews).

  2. (2)

Prolonged participation: Users who return repeatedly to a subreddit understand
and develop community culture. These core members help a subreddit maintain
its distinctiveness and attract newcomers. Researchers can identify these
users by searching for users that post and/or comment a disproportionate
amount and have been active on the subreddit for a prolonged amount of time.

  3. (3)

Linguistic membership boundaries: Tailored uses of language are also helpful
for differentiating members v.s. non-members. If users can comfortably use the
specific language of a given subreddit, then they have both been there long
enough to be familiar with the vernacular, and they are contributing relevant
content. Users exhibiting markers such as in-jokes, industry- or topic-
specific terms, or particular abbreviations, spellings, or even new words that
do not tend to appear elsewhere, are more likely to be members. Researchers
also may evaluate whether a user adheres to the style of discourse, including
tone, shared opinions, and overall sentiment.

####  4.2.3. Homogeneity

Measurements of homogeneity will be highly dependent on the researchers’
purposes for the project, and the type of subreddit being studied. We discuss
four types of homogeneity in ascending levels of perceived utility or
accessibility for research.

  1. (1)

Demographic: Unless demographics (e.g., race, age, gender, education, etc.)
are directly related to the research question, researchers warned against
collecting or inferring too much information from demographic homogeneity
because there are, likely more often than not, hidden confounds behind
demographics than can lead researchers toward faulty claims if they attribute
results to demographics rather than the underlying confounds.

  2. (2)

Situational: Situational homogeneity considers the user’s shared life
experiences or situational circumstances in relation to others in the
subreddit. Situational homogeneity can sometimes be related to demographics,
but it is a more powerful and unifying concept, and is especially important in
subreddits focused on events in one’s personal life.

  3. (3)

Goals or values: Homogeneity in shared goals/values can illustrate why people
are in a subreddit and what they hope to accomplish, and is of very high
utility for research. It can deviate from other kinds of homogeneity because
it is often better to have people with quite different identities and life
experiences coming together under a certain goal.

  4. (4)

Linguistic similarity: The degree to which a single user’s language use
adheres to group-level patterns may be useful for indicating their own
individual membership status. However, assessing the degree to which groups of
users use similar language may indicate a form of linguistic homogeneity that
can be directly calculated from trace data.

Given the absence of demographic information on anonymous accounts, and an
inability to accurately infer users’ personal situations or goals/values,
surveys or interviews should always remain the gold standards for assessing
those three types of homogeneity. Qualitative methods with smaller sample
sizes can be useful for first deriving the types of personal situations or
goals and values that are most relevant to later include in surveys.
Ethnographic observation of posts and comments over a period of time can also
help to determine what types of situational or shared goals/values exist.
Linguistic homogeneity, on the other hand, does not rely upon users’ internal
states, and may therefore be a convenient metric for studies that cannot
directly query users.

####  4.2.4. Norm Enforcement

Norms vary substantially across Reddit, as do the ways they are enforced.
Researchers described two broad ways of assessing this.

  1. (1)

Vertical: Vertical norm enforcement refers to top-down moderation by appointed
moderators. Researchers suggested quantitative methods for assessing the
degree of vertical moderation occurring, such as counting the number of
moderators and their volume of activity in the sub888Note that moderators can
also participate more casually, such as by adding their commentary to a
discussion, apart from official moderation. A refinement of this strategy
requires classifying the purpose of moderators’ messages., measuring the
proportion of removed content, frequency of banning users, and volume of
activity by governance bots, or observing how often rules are modified.

  2. (2)

Horizontal: Although users have less power than moderators, they can
nonetheless enforce norms horizontally. They can reply to others’ posts or
comments to point out norm violations (e.g., inappropriate behavior or
irrelevant/disallowed content, sometimes even suggesting an alternate
subreddit) or report issues to moderators. One strategy for assessing this
could be to develop classifiers for identifying this behavior and assessing
its frequency. A lack of engagement (e.g., posts receiving no comments) may
indicate content that doesn’t align with subreddit norms. Finally,
up/downvoting might also be an enforcement mechanism, although the meaning of
a vote is subjective and not universal, making it an unreliable measure.

Having described researchers’ concepts of community on Reddit, and their ideas
for evaluating the constructs of interactivity, membership boundaries,
homogeneity, and norm enforcement, we will next explore users’ perspectives.
By juxtaposing researchers’ ideas with users’ experiences, we can refine and
specify assessment strategies for a more human-centered understanding of SOVC.

##  5\. RQ2: User experiences of sense of virtual community

In this section, we present our analysis of our surveys of Reddit users. We
find evidence of several broad qualities of community experience that are
likely to apply across any community. These qualities align closely with prior
work and researchers’ ideas; therefore we start by briefly highlighting this
overlap (sec. 5.1). Importantly, we also find that users attribute their SOVC
to specific aspects of community experiences that are closely related to
specific types of subreddits. Researchers’ comments largely did not capture
these ideas, thus we introduce the framing of Community Archetypes and focus
most of this section on detailed characterizations of the core ways in which
users experience SOVC differently across different archetypes (sec. 5.2).

###  5.1. General qualities of community experience

There are three major ways users’ responses align with approaches suggested by
both our researcher participants, as well as by prior work in the social
sciences and HCI:

  1. (1)

Socializing is a major factor that improves SOVC; users frequently referenced
building meaningful relationships through discussions on threads, iterating on
each other’s ideas or jokes, and engaging in other subreddit traditions,
offline activities, or affiliated online spaces. This user data strongly
validates researchers’ discussions of the importance of conversation between
users rather than volume of interaction alone (sec. 4.2.1).

  2. (2)

Users referenced regular participants, or other users with special flairs as
core members. Aligning with prior work and researchers’ comments on prolonged
membership (sec. 4.2.2), it is clear that the visible activities of core
community members indicate SOVC.

  3. (3)

Users pointed to shared experiences, identities, mindsets, values, or views
that bind them together as an in-group. Their comments closely mirror
researchers’ discussions of homogeneity (sec. 4.2.3) and suggest that
demographics are not especially relevant, however shared situational
circumstances, and shared goals or values, are essential.

We also found that users’ perspectives highlighted how the specific
configurations of affordances, rules, norms, and behaviors within specific
types of subreddits caused them to experience SOVC in unique ways, leading us
toward the concept of Community Archetypes.

###  5.2. Community Archetypes

Archetype |  Roles |  Content Patterns
---|---|---
| Sec. 5.2.1
---
Topical Q&A
(Q&A)
| • Expert
---
• Novice
|  Posts: questions
---
Comments: answers, discussions and enrichments of others’ answers
| Sec. 5.2.2
---
Learning &
Broadening
Perspective
(Learning)
| • Insider
---
• Outsider
|  Posts: news, events, publications, personal stories, questions on a
particular topic
---
Comments: celebrating or disagreeing, making jokes, contrasting or similar
personal stories, elaborative ideas
| Sec. 5.2.3
---
Social Support
(Support)
| • Support seeker
---
• Supporter
|  Posts: personal experiences, sensitive disclosures, health questions,
milestone announcements, reflection, venting, resources
---
Comments: support, validation, reflection, commiseration, resources
| Sec. 5.2.4
---
Content
Generation
(ContentGen)
| • Producer
---
• Consumer
Posts: original content from a sub member, or contributions from other
users/platforms that exemplify a specific content style Comments: Opinions on
the content, extra information, commiseration
| Sec. 5.2.5
---
Affiliation with
an Entity
(Affiliation)
| • Current affiliate
---
• Prior affiliate
• Future affiliate
|  Posts: entity-specific news, events, and questions
---
Comments: Feelings about news or events, answers or advice about the entity
Table 4. Summary of Community Archetypes. We occasionally refer to these
archetypes by the (parenthetical abbreviations) included in the first column.

Prior works such as (Zhang et al., 2022; Seering et al., 2022) have introduced
the language of archetypes to describe conceptual frameworks for how an
abstract set of qualities may apply across individual instances. Rather than
hard classification categories, boundaries between archetypes are fuzzy, yet
each nonetheless retains a unique flavor that may manifest across various
identifiable subreddit features and user behaviors. Whereas the authors of
(Zhang et al., 2022; Seering et al., 2022) apply the term archetype to
individuals, we use it to reference communities. This terminology thus helps
us to understand how abstract community characteristics lend themselves to
certain roles and functions in a user’s content consumption or other forms of
participation. Given that subreddits are the selected unit of analysis for
this paper, we apply these archetypes as labels to individual subreddits,
while also suggesting that future work should apply them to other units of
analysis on Reddit—e.g., groups of subreddits, or subgroups of users—or
moreover, on other platforms that host online communities.

Our work reveals five community archetypes (Table 4). Multiple archetypes may
overlap in any subreddit, however, there is typically a more distinct focus on
one over others. For example, Table 2 designates which archetypes best match
each subreddit in our study, and shows how some embody one major archetype,
whereas others have multiple. Next, we describe each of these archetypes in
detail, specifying important user roles, content patterns, and rules
associated with each. Table 4 includes an overview of archetype roles and
content patterns.999Section 4 in the supplemental materials includes more
detailed results of our analysis of subreddit rules.

####  5.2.1. Topical Q&A

Some subreddits are explicitly set up with strict rules to create a Q&A format
around specific topics. For example, P335 declares that S10 is “the best place
on the internet to find reliable, well-sourced, in-depth information on
various [academic discipline] topics.” In our sample of Q&A subreddits, 55.2%
of rules relate to content/behavior (mainly around respectful engagement with
others and following the Q&A format), with advertising and commercialization
(which allows for users to ask for help without being targeted as consumers),
low-quality content (which encourages generating content that is useful for
the subreddit), and off-topic (which keeps content relevant) rules being the
next largest categories, accounting for 10.3% each. We observe the content
pattern that most top-level posts are questions, while most comments are
answers, or discussions and enrichments of others’ answers. There are two
clear user roles: novices (those who ask questions) and experts (those who
answer them). These groups may possibly overlap—i.e., a user who usually
answers questions might occasionally ask one, or vice versa. Our survey
respondents typically indicated their motivations for using the subreddit in
an either/or manner; the distinction between these two roles appears to be
rather sharp. Expert users enjoy helping people and sharing their knowledge
about a subject they are passionate about, while novices sincerely appreciate
the special access to experts and the information they can provide. For
example, P333 visits S10 to “provide answers where I have specific knowledge
or expertise,” whereas in S3, P53 enjoys that “questions can always be
answered constructively.”

Users explain that SOVC arises when the community is effectively fulfilling
its Q&A function, providing rich, multi-faceted discussions on current topics,
while also generating a useful archive of past inquiries and information. For
example, P337 appreciates the collaboration and diversity on S10 when “people
with different training and background will often read the same question in a
different way and bring different perspectives.” However, some users, such as
P163 mentioned that they don’t feel SOVC in S4 because it isn’t “used in a way
that could foster a community…it’s closer to a help or Q&A board.” In general,
we posit that each user experiences her own affinity for particular forms of
interaction or community; there is no such thing as a one-size-fits-all online
community, and it is impossible to claim that SOVC will always arise for every
user when $X$ phenomenon occurs. However, across many users, our results
suggest that SOVC is more likely to arise when $X$ phenomenon occurs, if $X$
is consistent with its associated archetype.

####  5.2.2. Learning & Broadening Perspective

As our most commonly observed archetype, users frequently mentioned learning
or broadening their perspective as a primary reason for visiting a subreddit.
Content patterns are less formulaic than Q&A subs, however top-level posts are
often pointers to current events, publications, or relevant news, or relatable
personal stories, experiences, and questions. Comments tend to elaborate upon
the ideas raised, for example by celebrating or disagreeing, making jokes,
providing contrasting or similar personal stories and experiences, or adding
new ideas and references into the mix. These subreddits have a central focus
around a particular culture—not only for people within that culture (who
benefit from shared experiences of it), but also for users who are not part of
that culture themselves, yet appreciate it and want to know more. For example,
P433 (S11) said, “I want to hear new and/or different opinions on various
topics important to the [redacted] community. It helps me grow my allyship.”
Given this cultural focus, we refer to two roles as insiders and outsiders.
For example, on S2, insiders are scientists or people with extensive
scientific background and training, whereas outsiders are curious and engaged
members of the lay public. On S6, insiders are members of historically
marginalized racial groups, whereas other users (mostly white) self-identify
as outsiders who want to learn how to be better allies. It may be that this
less formulaic, diverse structure is the reason that this category of subs has
one of the highest instances of content/behavior rules (62.5%). Guidance
around treating others well and generating content that serves the purpose of
the subreddit allows for the content to be more free form while still
cultivating a desirable space. They also have higher rates of rules about
harassment and hate speech (15% each) which allows for insiders and outsiders
to interface respectfully and in a way that fosters a learning environment.

Users in these subs report feeling SOVC because of shared mindset or values.
For example, P609 (S12) said, “I think the users of this subreddit are a
community because of similar ideas of approach to spirituality.” Having one
big similarity that holds users together allows users to learn from each
other’s differences. For another example, in S2, users share a value system
that places high importance on correct reporting of academic subjects. S2
users have differing expertise and levels of subject familiarity. Therefore,
they can inform others who may not have the same background, but who also care
deeply about information accuracy. However, some users do not feel SOVC on
these types of subreddits because their “interest in the subreddit is in the
content and information more than the interpersonal relationships” (P341,
S10). Such users may not develop SOVC, even if others do, because they are
simply not looking for it.

####  5.2.3. Social Support

Much prior work has examined social support behaviors in online communities
(Smith et al., 2020a; Andalibi et al., 2016, 2018). At a higher level, our
work suggests a social support community archetype, in which socially
supportive behaviors are the main purpose and organizing principle for
gathering. Many users are seeking spaces that can help them navigate their own
difficulties or illnesses; others are seeking information and resources to
help a loved one who is struggling. For example, P189 said that S7 helps them
“get a better insight as to how to support [their loved one] and what not to
do.” Rules in these subs seek to protect users and keep the conversation
focused on the issue, with 61.7% regulating content/behavior to create a
subreddit that is safe and focused, 14.9% warning against off-topic content
(esp. content that is unhelpful or triggering), and 10.6% disallowing hate
speech in order to create a safe space. Top-level posts are often specific
personal experiences or sensitive disclosures, questions about a health issue,
announcements of milestones, reflections or venting, or sharing of resources,
artwork, and encouraging thoughts and memes/jokes. These subs often contain
flairs that specify content warnings, symptoms, or labels of users’ intent
(e.g., “seeking support” or “venting”). Comments generally offer support,
reflection, commiseration, resources, and validation. In this archetype, roles
include support seekers and supporters, yet the distinction is fuzzy and
inconsistent, with heavy overlap in how users occupy them. In one post, a user
might seek support. In the next, she might become a supporter, returning the
kindness she received from others. It is certainly possible to build
classification schemes that label individual posts and comments as support
seeking or supporting (and sometimes both), however it would be difficult to
accurately reflect the role in a static manner per user; it shifts
continually.

Users report SOVC when they see their own hardship reflected in other’s posts
and the community provides true kindness, support, and validation. P238 (S8)
said, “it feels like my experience was real and valid.” P189 also said their
support community, S5, offers “sympathy that no one but them can provide
properly and meaningfully” due to other users’ intimate familiarity with the
issue. Users call these subs a safe space to express feelings, be themselves,
and get meaningful help. A few respondents also recognized that they didn’t
always use these subreddits in a healthy way. P277 often visits S8 for
support, but sometimes they “want to force myself to get over it by reading
about people who have it worse,” a behavior they do not view as healthy. It is
possible to stay too long in support subs, thus promoting lingering in painful
memories rather than recovery (unless the user is intentionally returning to
help others). It is important to understand when SOVC is beneficial to well-
being versus when it could be holding users back.

####  5.2.4. Content Generation

Users who visit ContentGen subreddits are interested in particular types of
content with a certain sense of humor, viewpoint, format, purpose, or type of
expression. For example, P4 appreciates that S1 has “the expectation that you
will ‘act oddly.”’ In this archetype, user roles include producers and
consumers, where producers create top-level posts that exemplify that sub’s
specific content style, and consumers are specifically there to view and
respond to it. Top level posts might be created by original posters
themselves, or they may be contributions from other users or platforms that
the poster would like to share or discuss on the subreddit. Comments often
include peoples’ opinions on the content, extra information about the content,
or sometimes commiseration with the original poster. As a result of this
content-focused culture, content/behavior is the subject of 62.5% of rules,
low-quality content is 16.7%, and hate speech and advertisements &
commercialization are 12.5% each, both of which serve to focus users solely on
the content rather than things like ads or hate speech.

Some users expressed feeling SOVC due to the niche nature of the content which
brings people together because, as P271 (S11) states, “We share the same sense
of humor and appreciate the same content.” Another user, P6 illustrates how
niche subreddits like S1 provide a kind of camaraderie that doesn’t exist for
them in real life: “It’s like finding out there’s a ‘splashing ice-cold water
in people’s faces’ club after years of failing to convince your family and
friends that being splashed in the face with ice-cold water is fun.” However,
similarly to the Learning & Perspective Broadening archetype, users may not
feel SOVC if they are simply using the subreddit as a resource rather than
looking for community. P142 (S3) illustrated this point, saying, “It feels
much more transactional to me… I do not frequently see updates or multiple
posts from a given user.”

####  5.2.5. Affiliation with an Entity

Some subreddits have explicit affiliations with particular entities, such as
geographical places or organizations (cities, universities), popular media
(book or fan series, TV shows), sports teams, etc. In our limited data set, we
only observed this archetype once, in affiliation with a particular
geographical area and university campus (S9). Consequently, we observed user
roles of current, prior, and future campus residents. However, we will refer
to these roles more broadly as “affiliates.” In our usage of Reddit beyond
this sample, we have informally observed these affiliate roles across
different types of entities; broadly, they can be defined as a user’s level of
active, ongoing knowledge and investment in a particular entity. Current
affiliates have a good grasp on up-to-date “local” knowledge by virtue of
ongoing attention to or residency within the affiliated entity. Future
affiliates are more like “prospective community members” who are curious,
while prior affiliates enjoy retaining a connection even though they are no
longer focusing as much resource or attention. In terms of content patterns,
posts often feature local or breaking news and events related to the entity.
Future and current affiliates often pose questions, while past (and other
current affiliates) tend to answer them. In the comments, users generally
express their feelings about news or events, and answer questions or offer
advice and opinions about the entity. There is notably less regulation on
content/behavior than in other archetypes (40%), possibly because it is
already constrained by the geographical affiliation (however, this may also be
because only one subreddit in our sample fits this archetype).

In S9, the specific tie to a geographical entity makes users feel SOVC. P286
(S9) said, “[City] as a whole has always been a community to me and this is a
microcosm of that community.” Users such as P307 (S9) also appreciate “people
sharing their experiences and advice in a genuine and helpful way,” because
it’s likely to be applicable due to users’ geographic closeness. Some users
felt it was easier to get good information from the subreddit because the city
and university administration did a poor job of circulating it. Thus, the
subreddit seems to supplement the offline space it is tied to. As P297 put it,
“everything posted there [S9] has to relate to [US university], which helps me
feel more connected to the offline campus community.” In fact, P305 also
noticed that “some memes from [S9] leak onto campus every now and then.”
However, several users stated that this kind of subreddit isn’t the place they
would go to feel SOVC. P297 (S9) said, “If I wanted to feel like part of a
community, I wouldn’t be looking for it on a subreddit for a large state
school.” Another user, P310, remarked that a subreddit like S9 is “more like a
collage of experiences” than a community.

##  6\. Discussion

In this paper, we set out to understand researchers’ mental models of
community, juxtapose these models against users’ experiences, and produce
recommendations for refining research methods to better align with users’
experiences of participating in online communities. Using the Reddit platform
as a case study, we found that researchers tend to view subreddits as topical
affinity groups, however each subreddit individually sits somewhere along a
spectrum of “community-likeness.” This result evokes work by Bruckman, who has
suggested that groups be categorized as communities on a spectrum based on how
closely they resemble a baseline or “prototypical” concept of a community
(Bruckman, 2006, 2022). However, that introduces the problem of what exactly
the baseline idea of a community should be. Our work suggests that there is no
single prototype that can describe all communities. Rather, our analysis
reveals at least five Community Archetypes that can be embodied by online
communities to varying degrees: Topical Q&A, Learning & Broadening
Perspective, Social Support, Content Generation, and Affiliation with an
Entity (see Table 4). Each archetype is distinguished by specific behaviors
involving user roles, content patterns, and group rules, and users are most
likely to experience a sense of virtual community (SOVC) when these behaviors
are strong and frequent. Although we will use Reddit-specific terms and
affordances throughout much of our discussion, we will conclude with
reflections on how we expect these archetypes to persist across other online
social platforms, given the fundamental psychological and social needs shaping
human behavior.

Figure 2. Screenshots of banner images and community descriptions at
r/wallstreetbets (left) and r/aww (right). Retrieved in December 2022.

Figure 3. Hypothetical spiderplot comparing Community Archetypes in
r/wallstreetbets (blue) v.s. r/aww (orange) for purposes of illustration only.
Rather than factual graphs, these plots are suggestive illustrations based on
our estimated observations of the proportional frequency of visible content
patterns and user behaviors. The outermost ring represents 100% frequency,
with concentric rings descending to an innermost ring of near 0% frequency.
Future work could systematically calculate such plots by tabulating the actual
frequency of posts and user behaviors in each archetype.

Our results suggest that subreddits can embody one or two archetypes, however,
no single subreddit can fully embody all archetypes. To illustrate this, we
will compare two popular subreddits: r/aww and r/wallstreetbets.101010We
picked large and well-known subreddits in order to illustrate how our
framework can impact methods decisions in future studies. We chose these
examples because they were not included in our sample and they are already
well-known among Reddit users, reducing possible exposure of less well-known
subreddits to unwanted attention (Smith et al., 2020b; Dym and Fiesler, 2020).
Figure 3 depicts screenshots of the subreddits, while Figure 3 contains a
hypothetical spiderplot depicting how Community Archetypes may be present
within them to differing degrees. r/aww was a “default sub” until 2017 when
Reddit discontinued this automatic subscription mechanism for new users
(Reddit, 2017) and is described as “a place for really cute pictures and
videos.” Most rules are restrictive against any other content, and every
single post features a picture or video of a cute animal. Thus r/aww appears
to be well-described almost exclusively by the Content Generation archetype
(possibly with a hint of Learning & Perspective Broadening). In
r/wallstreetbets, on the other hand, we observe a dominant affiliation with
finance and stock trading.111111“Wall Street” is a metonym using the road in
Manhattan where the New York Stock Exchange is located to refer more generally
to financial markets. r/wallstreetbets was became influential in 2021 due to
driving massive upswings in prices of “meme-stocks.” (Lopatto, 2021; Banerji
and McCabe, 2022) The community description (“Like 4chan found a Bloomberg
Terminal”) is more cryptic than r/aww, and the rules focus on a variety of
activities that are tolerated (or not) related to stock purchases. Content
patterns are strongly evocative of the Affiliation and Learning archetypes,
often revolving around discussions of specific stocks and whether users should
“bet” by buying risky stocks for humor, thrills, or financial gain. There is
also an edgy flavor of Social Support in which users who lost large amounts of
money on failed bets are simultaneously made fun of, consoled, and glorified.
Although a few posts resemble Q&A or ContentGen, these are not nearly as
consistent as in r/aww. These examples highlight how some subreddits have a
single or dominant Community Archetype, whereas others have more hybrid,
flexible, or nuanced Community Archetypes.

###  6.1. Implications for community-centered data science

We turn our attention first to translating our findings for a data science
audience. We will present three reflections on prior work, pointing to
implications where Community Archetypes can help to better leverage insights
from organizational psychology and more closely align data science with users’
SOVC.

####  6.1.1. Rethinking “community size” as a success metric

HCI papers often use community size as a success metric (e.g., (Tan, 2018;
Cunha et al., 2019; Kairam et al., 2012)) on the assumption that the larger a
community has grown, the more successful it is. This metric does indicate how
effectively an online group has recruited users, and the recruitment of a
“critical mass” of users is a necessary pre-condition and known challenge for
community survival (Kraut and Resnick, 2012). However, our work suggests that
community size is too blunt of an instrument for assessing SOVC. Rather, users
often participate in small subreddits to meet their preferences and needs
(Hwang and Foote, 2021) and if subreddits grow too large, studies have noted
user complaints that they may have been ““better when they were smaller.” (Lin
et al., 2017) As in Usenet’s “Eternal September” (Grossman, 1997), influxes of
new users may seriously disrupt cherished community dynamics, unless there are
sufficient sociotechnical affordances and policies to maintain established
practices (Kiene et al., 2016). For example, r/wallstreetbets rapidly
attracted several million new users after its meme-stocks disrupted the market
in 2021 (Alcantara, 2021). By 2022, longtime members felt that
r/wallstreetbets lost its distinctive character, since new users’ discussions
mostly revolved around old, tired topics (Banerji and McCabe, 2022). On the
other hand, Lin et al. showed that when subreddits were added to default
subscription lists by Reddit admins (as was the case in r/aww), there were
momentary fluctuations in community activity following newcomer influxes, but
the subreddits more-or-less returned to pre-established patterns following the
disruption (Lin et al., 2017). Newer work similarly explores community
resilience after a subreddit is featured on r/popular (Reddit’s replacement
for default subreddits), finding that community behaviors were differentially
and strongly impacted in smaller rather than larger subreddits (Chan et al.,
2022). These examples show that community size does not predict SOVC, even
though it can impact SOVC.

Our work suggests that alternative metrics may be better aligned with users’
SOVC. As reported in Section 5.1, data scientists should consider community
success metrics derived from behavioral traces such as: (1) the frequency and
depth of conversations occurring among users; (2) the interactions between
well-established community members and newer community members; or (3) the
persistence of users’ engagement over time. These strategies should be widely
applicable across online communities beyond Reddit as forms of interactivity
and membership that are antecedents to SOVC. This concept is supported by
prior work demonstrating that greater thread depth on Reddit is associated
with language markers indicating positive community outcomes like stability,
cohesiveness, and sociability (McEwan, 2016). However, the Community
Archetypes framework also suggests that more specialized forms of
interactivity are expected within specific archetypes, and that other
antecedents like membership boundaries, norm enforcement, and homogeneity may
be quite distinct between archetypes. Our next reflection explores how to
operationalize this specificity by re-purposing methods proposed by prior
work.

####  6.1.2. Rethinking “SOVC antecedents” as universal

The Community Archetypes framework suggests that users form SOVC as a
consequence of community behaviors that differ across archetypes. In other
words, different archetypes have specialized antecedents, and this impacts how
researchers should interpret different categories of behavior across different
communities. For example, Bao et al. describe computational measures for a set
of eight pro-social behaviors expected in healthy online communities:
information sharing, gratitude, esteem enhancement, social support, social
cohesion, fundraising and donating, mentoring, and the absence of anti-social
behavior (Bao et al., 2021). Although these all seem beneficial at face value,
they are not necessarily required antecedents of SOVC across all archetypes.
For instance, social support is fundamental to the Support archetype, however
it may not be expected, needed, or even appropriate in others. If researchers
assume that all healthy communities are marked by high levels of social
support, they risk inaccurate conclusions that community spaces where social
support is low or absent also lack a sense of virtual community. Therefore,
for computational metrics related to community features such as pro-social
behaviors (Bao et al., 2021) or types of relationships between users (Choi et
al., 2020), researchers need to choose which measures align with users’
normative experiences, needs, and values in that particular community and
archetype. In r/wallstreetbets, for instance, the edgy style of social support
could easily be mis-categorized or perceived as anti-social if the researcher
was unfamiliar with the norms of the community. On the other hand, in r/aww,
the purpose of the subreddit is to generate cute memes; social support is not
the point. Therefore, low levels of social support would not necessarily
indicate poor SOVC in this “ContentGen” Community Archetype.

####  6.1.3. Rethinking “community boundaries” across Reddit

As in (Smith et al., 2022), we acknowledge that a single subreddit is not the
best unit of community. Rather, there exist mutualistic, overlapping groups of
members across subreddits (TeBlunthuis and Hill, 2022) with similar topics or
genealogical relationships (Tan, 2018) and norms or rules (Chandrasekharan et
al., 2018; Fiesler et al., 2018). News events such as the #RedditBlackout in
2023 due to the introduction of new API fees (Mehta, 2023) and academic
studies have also examined migratory events in which groups of users moved
between different spaces on Reddit (or off of the platform entirely) due to
moderation conflicts (Newell et al., 2016; Chandrasekharan et al., 2017;
Davies et al., 2021; Trujillo and Cresci, 2022). For example, when Reddit
banned the hate-based subreddits r/fatpeoplehate and r/CoonTown, many users
left the platform, whereas others moved to new subreddits but mostly did not
bring their hate speech with them (Chandrasekharan et al., 2017). These
studies highlight how the same communities of users exist beyond the
structures of individual subreddits and how these communities may or may not
reconstitute themselves elsewhere after major events.

The Community Archetypes framework provides new strategies for delineating
communities that may help to describe these phenomena and better match users’
experiences of SOVC than individual subreddits. For example, community
detection methods that rely upon network structure (Fortunato and Hric, 2016;
Olson and Neal, 2015; Datta and Adar, 2019) may benefit from incorporating
archetypal community features. One possibility is a “top-down” strategy in
which researchers could assess and label the archetypal composition of each
subreddit in their sample, next analyzing the interaction and relationships
specific to each archetype separately. This strategy will allow researchers to
look at similar behaviors across multiple different archetypes, but to
interpret them more coherently within the context of each archetype. On the
other hand, a “bottom-up” strategy could de-emphasize subreddits as units and
instead look primarily to users’ interaction patterns and styles to delineate
Reddit communities. For example, researchers could define communities by
setting participation thresholds or criteria guided by questions like: Which
users of which subreddits are regularly interacting with each other (and/or
longer term super users) in nested discussions, regardless of which subreddit
the interaction takes place in? What types of roles (Buntain and Golbeck,
2014) and norms (Chandrasekharan et al., 2018) do these behaviors indicate?
Within this community of users interacting across subreddits, what
archetype(s) best explain their community’s activities? Although this strategy
may be computationally taxing, it should enable a more fine-grained analysis
with better alignment to users’ SOVC.

Finally, it is also important to highlight the temporal boundaries of
communities (Barbosa et al., 2016). Rather than looking at “all time”
activities across a subreddit, researchers may better align their methods with
users’ SOVC if they choose time frames that align with members’ actual
engagements with the community (Geiger and Halfaker, 2013). Users often flow
in and out of online spaces, taking their ideas and norms with them. This
creates “cohorts”—i.e., groups of people who entered the community near the
same time, often as the result of an event that brought their attention to the
topic or to the specific subreddit (Barbosa et al., 2016). For example, in
Affiliation subreddits (e.g., with a sports team or television show) the
community may be healthy and thriving, with a great deal of activity during
events related to the entity (e.g., live games or the release of new
episodes), even if it is dormant during the “off season.” Alternatively, even
if activity is quite consistent in a given subreddit, our participants also
observed that content may start to feel repetitive when discussions return to
the same topics over time. For example, in the Learning archetype, outsiders
may have similar questions that regularly percolate to the surface, causing
cyclical patterns in the content. Given these types of considerations, we
suggest that analyzing cohorts of users who were active during the same period
will be more aligned with their SOVC within a given archetypal community.

###  6.2. Applying the Community Archetypes framework in research

In cases when researchers hope to study, model, or support users’ experiences
of SOVC in online communities, the Community Archetypes framework can help
researchers to structure and justify their selection of methods. This section
synthesizes a workflow for doing so (Fig. 4).

Figure 4. Workflow for applying the Community Archetypes framework.

####  6.2.1. Identify archetype(s)

Researchers should first consider which archetype(s) best fit the purposes of
their study. (Alternatively, if subreddit(s) are already identified, assessing
which archetype(s) those subreddit(s) embody is necessary to analyze them
effectively.) Data science models built across many different subreddits
without specification of archetype should only rely upon broadly applicable
analytical strategies (as in Sec. 6.1.1 above), whereas more sophisticated
techniques can be designed if researchers have conscientiously selected
particular archetype(s). For methods with smaller sample sizes (e.g.,
ethnography, interviews, surveys, content analysis, or mixed methods),
researchers can use the framing of archetypes, content patterns, and user
roles to help structure and scope their protocols, questions posed to users,
analytical codebooks, etc., using the archetypal category as justification for
asking about certain types of norms, behaviors, etc., and not others.

####  6.2.2. Delineate community

Once archetype(s) have been chosen, researchers can delineate community
boundaries at the intersection of those archetype(s), the topic of interest,
and a sensible timeframe. This can be done through examination of groups of
candidate subreddits or groups of interacting users across subreddits (sec.
6.1.3). Researchers can choose qualitative or quantitative techniques for
analyzing content patterns, user roles, and rules/norms (see Table 4)
depending on the size and scope of their study. However, in order to ensure
that assumptions hold across their sample, they should select communities of
similar archetypal composition.

Most researchers in our study opted to study the largest or most popular
possible subreddit(s) on their topic of interest. However, large subreddits
have distinct moderation and governance challenges relative to smaller
subreddits. The question of what size subreddit is “most representative” or
“more of a community” is difficult given that long-tailed distributions have
no central tendency: are many small subreddits more representative of a user’s
typical experience or a few large subreddits? The largest subreddit may not
always be the best choice, particularly when it is important that a community
exists or a specific kind of data is needed. For example, one researcher
described how many fandoms have multiple Affiliate subreddits, the largest of
which is often for more general information, casual fans, and people just
discovering the fandom (i.e., future affiliates), whereas smaller ones are
dedicated to more specific sub-topics for much more highly invested current
affiliates or have more stringent moderation rules. More community-like
behaviors may exist in these smaller, more specific subreddits than in the
larger “on-boarding” subreddit. Therefore looking at smaller, more specialized
subreddits or groups of users interacting across the whole family tree of
fandom Affiliate subreddits may be valuable tactics.

####  6.2.3. Define membership boundaries

The next step is to identify how users should be classified as members. Our
framework suggests a tiered membership classification scheme because a binary
view of membership may be overly simplistic and provides an incomplete
interpretation of actual community dynamics. An ideal way to determine
membership in qualitative and mixed methods is to first observe what user
roles exist, and then ask users directly about how they view their own
membership and role in the community. In quantitative and data science-based
methods, rather than a simple activity threshold (e.g., “members” are defined
by making $X$ number of posts), researchers should identify markers of
expected user roles within the specified archetype(s). Prior work on
identifying social roles in online communities (Buntain and Golbeck, 2014;
Zhang et al., 2022; Müller-Birn et al., 2015; Jin et al., 2007; Zhu et al.,
2011) and techniques that combine behavioral and content features (Liu et al.,
2019) will be especially useful in this regard.

####  6.2.4. Evaluate remaining antecedents

The first three steps set the stage for more effectively assessing the
remaining antecedents (interactivity, homogeneity, and norm enforcement) with
respect to the appropriate archetype.

Interactivity. Different forms of interactivity are more essential in
different archetypes (sec. 6.1.2). Prior work provides examples of ways to
assess some antecedents (e.g., (Bao et al., 2021)). New methods can be
tailored to distinctive archetypal content patterns:

  * •

Q&A: Researchers can assess whether almost all posts are formatted as
questions, whether the majority are receiving answers, and how in-depth and
varied the answers are.

  * •

Support: Support exchange is fundamental and SOVC is likely when users
regularly switch between support-seeking and support-providing roles.

  * •

Affiliation: Frequent references to entity-specific terms, people, and topics
encourage SOVC; the community should become more active during important
entity-affiliated events.

  * •

Learning: SOVC is likely when in-depth, respectful discussions feature many
contrasting perspectives between insiders and outsiders.

  * •

ContentGen: Posts and in-jokes should adhere to expected content formats and
not be repetitive re-posts. Posts that refer to and build on prior content
should indicate SOVC.

Homogeneity. Participants suggested that demographic homogeneity (race, sex,
age, etc.) is less salient than other forms of homogeneity, such as shared
interests, experiences, and goals. Even these latter forms may be
differentially important across archetypes. In qualitative studies,
researchers may want to focus on shared interests for Q&A and ContentGen
communities, shared experiences for Support and Affiliation communities, and
shared goals in Learning communities. In quantitative studies, linguistic
homogeneity is important to community cohesion and should be useful across all
archetypes.

Norm Enforcement. There are macro norms (universal to much of Reddit), meso
norms across particular groups of subreddits, and highly specialized micro
norms within individual subreddits (Chandrasekharan et al., 2018). Community
Archetypes offers a new lens for interpreting meso norms: we expect that
subreddits of similar archetypal composition are likely to be marked by
similar meso norms, and that users’ SOVC is related to how these norms are
vertically and/or horizontally enforced. Future work should explore this
question in more depth, particularly by using the taxonomy of Reddit rules by
Fiesler et al. (as in our cursory rules analysis included in the supplemental
materials) (Fiesler et al., 2018). We expect differences across archetypes in
terms of: what types of rules/norms exist; how strictly users want norms to be
enforced; and how strictly moderators enforce norms in actual practice. For
instance, users of Support subreddits especially emphasized how their SOVC is
contingent upon stricter enforcement and mods’ commitment to keeping the space
safe; these subs may need more rules overall and higher enforcement both
vertically and horizontally to protect users’ SOVC. Q&A and ContentGen
subreddits may need stronger adherence to prescriptive content rules, whereas
Learning and Affiliation subreddits may need better mechanisms to govern
behaviors like harassment, hate speech, and influence operations.

####  6.2.5. Build models and community support tools

Community Archetypes can sensitize our research methods, data models and
community support tools to meaningfully nourish the human need for sense of
community in our modern world. For example, users told us that strategies like
increasing the activity of moderators, keeping subreddits small but fostering
more activity and content, and structuring regular activities both on Reddit
(e.g., routine threads for catching up with other users) and off Reddit (e.g.,
on Discord) would contribute to improving SOVC. They also highlighted
opportunities for better governance bots (Smith et al., 2022) for patrolling
hate speech and troll activities, specific tasks in that subreddit’s interest
area, answering repetitive posts on common questions, or responding to posts
that never received any comments.

##### Tailoring governance support to Community Archetypes

Similarly to how rules and norms vary by archetype, our work suggests that
there is no “one-size-fits-all” set of moderation strategies, policies, or
tools that are likely to work equally as well for all communities. Styles and
types of moderation labor should differ in reliable ways across different
archetypes; thus, a useful future contribution could be baseline tooling
tailored appropriately to each archetype. Rather than one u/AutoModerator to
rule them all, subreddit mods could be provided with options configured and
tuned to more sophisticated patterns of activity in the subreddit—not only for
enforcing rules restrictively, but also for spurring positive antecedent
behaviors, interactions, and communication that lead to SOVC in that
archetype. For example, imagine an u/AutoSupportMod for Social Support subs,
an u/AutoMemeMod for ContentGen, etc.

###  6.3. Community Archetypes beyond Reddit

####  6.3.1. Archetypes across platforms

Reddit is now a prominent platform studied by academics, however that could
easily change given the ever-shifting landscape of social media. The
archetypes uncovered in our study relate to basic human needs for information,
entertainment, socialization, and support, and that they likely feel familiar
to readers. Entire platforms with more constrained affordances and contexts of
use than Reddit exist specifically for Q&A (e.g., StackOverflow, Quora),
Social Support (e.g., CaringBridge, PatientsLikeMe), Content Generation (e.g.,
Imgur, MemeGenerator), etc. At the same time, platforms like Facebook,
Instagram, Twitter, or analogous Fediverse options can support similar
Community Archetypes depending on how users and/or moderators appropriate
them. Reddit communities likely learned a thing or two from their
predecessors, just as future platforms will iterate on today’s models. We
expect this Community Archetypes framing to remain fundamentally relevant to
research because the underlying community needs will remain relevant to users.
Yet future work will need to account for new sociotechnical circumstances,
such as issues raised during the #RedditBlackout in June 2023.

Although Reddit’s API was free and open for many years, new API rules have
restricted access to data, impacting researchers, developers, and users.
Moderators of thousands of popular subreddits made their subs private (and
therefore inaccessible to millions of members) or participated in other forms
of protest such as bizarre or humorous content restrictions (Mehta, 2023).
Although Reddit granted mods a limited tier of free API usage for running
moderation bots (Help, 2023), it also threatened to remove mods who refused to
re-open their subs (Peters, 2023). Issues like this may cause users to abandon
the platform, migrate to other subreddits, or alter their community
participation behaviors. Amdist the instability of shifting platform
incentives and migrations, Community Archetypes offers a promising way to
conceptualize, study, and support real human communities—regardless of the
platform they currently inhabit.

####  6.3.2. Future work

Given the rapid rate at which algorithms, affordances, behaviors, and
governance of tech platforms continue to evolve, the Community Archetypes
framework is certainly not complete. We did not specifically set out to find
these archetypes, thus different archetypes could exist or emerge on other
current or future social media platforms. Future work may identify additional
archetypes missed by this study, as well as methodological considerations that
could differ accordingly. Additionally, there is a need for case studies in
which we can empirically use and validate the Community Archetypes framework
not only on the level of individual “digital containers” of communities (e.g.,
subreddits) but even more especially across groups of such containers that
provide users with SOVC. Moreover, trace data can easily be misinterpreted and
can never tell the whole story of users’ internal states or feelings. In order
to truly understand how users are feeling, it will always be necessary to ask
them directly, thus future work should continue engaging directly with
community members to understand their experiences and perceptions as platforms
change.

###  6.4. Conclusion

In this study, we interviewed 21 Reddit researchers and surveyed users of 12
subreddits to explore SOVC. We identified five Community Archetypes with
distinctive forms of activity, user roles, and content patterns. Accordingly,
we contribute methodological recommendations for identifying and studying each
of these archetypes. This work will support researchers to understand, select,
and justify methods that best suit their projects, as well as to design
community support tools that can promote healthy community formation and
provide members with nourishing SOVC.

###### Acknowledgements.

We thank our participants for sharing their insights and experiences. We are
also appreciative for the supportive feedback from Stevie Chancellor, Loren
Terveen, Charles Chuankai Zhang, Sanjay Kairam, and the dedicated anonymous
reviewers who supported us to improve this manuscript. Undergraduate
assistants Tamer Shahwan, Sahand Setareh, and Vikki Wong helped us to collect
our survey data and were also instrumental to the success of this project.
This work was supported by NSF Award #1910225.

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