# Form-From: A Design Space of Social Media Systems

Amy X. Zhang  University of WashingtonSeattleWAUSA<EMAIL_ADDRESS>0000-0001-9462-9835 ,  Michael S. Bernstein  Stanford UniversityStanfordCAUSA
<EMAIL_ADDRESS>0000-0001-8020-9434 ,  David R. Karger  Massachusetts
Institute of TechnologyCambridgeMAUSA<EMAIL_ADDRESS>0000-0002-0024-5847 and
Mark S. Ackerman  University of MichiganAnn ArborMIUSA<EMAIL_ADDRESS>0000-0001-9727-1664

(July 2023; October 2023; November 2023)

###### Abstract.

Social media systems are as varied as they are pervasive. They have been
almost universally adopted for a broad range of purposes including work,
entertainment, activism, and decision making. As a result, they have also
diversified, with many distinct designs differing in content type,
organization, delivery mechanism, access control, and many other dimensions.
In this work, we aim to characterize and then distill a concise design space
of social media systems that can help us understand similarities and
differences, recognize potential consequences of design choice, and identify
spaces for innovation. Our model, which we call Form-From, characterizes
social media based on (1) the form of the content, either threaded or flat,
and (2) from where or from whom one might receive content, ranging from spaces
to networks to the commons. We derive Form-From inductively from a larger set
of 62 dimensions organized into 10 categories. To demonstrate the utility of
our model, we trace the history of social media systems as they traverse the
Form-From space over time, and we identify common design patterns within cells
of the model.

social media, design space, social computing systems, taxonomy

††copyright: rightsretained††journal: PACMHCI††journalyear:
2024††journalvolume: 8††journalnumber: CSCW1††article: 167††publicationmonth:
4††doi: 10.1145/3641006††ccs: Human-centered computing Social media††ccs:
Human-centered computing Collaborative and social computing systems and tools

##  1\. Introduction

New social media platforms rise all the time. Even in the time we were
finishing this paper, several platforms arose as competitors to the massive
platform Twitter:111Twitter was also recently renamed to X. We refer to it as
Twitter to reflect the time period when the research was conducted. Mastodon
pushed a decentralized approach, Twitter’s co-founder announced Bluesky with a
promise of per-user personalized moderation, Meta launched a competitor called
Threads to leverage their user base, Substack pushed out a platform called
Notes, and still others promise that their designs or moderation strategies
will produce friendlier and more welcoming communities.

What makes these social media designs different from, or similar to, one
another? Considering two popular platforms in 2024, the main Instagram feed is
different from TikTok’s For You feed—but how? Certainly Instagram is focused
on images, whereas TikTok is focused on video, but we could swap Instagram’s
photos for TikTok’s videos and the platforms would still feel and act
differently.

Given the many iterations and continually changing designs of social media
systems, a comprehensive design space and succinct models for that design
space are essential for mapping systems to articulate similarities and
differences, charting the evolution in system designs, and ideating possible
future systems. Through its evolution, CSCW has benefited from design space
models such as the time-space matrix widely attributed to Johansen (Johansen,
1988) and Grudin (Grudin, 1994). Yet as social media has rapidly evolved in
the last thirty years, today nearly all social media systems fall in the exact
same “different time-different place” cell in the time-space matrix. So,
social media researchers and designers are overdue for an update to our design
models that better captures the similarities and differences in social media
design.

In this paper, we pursue a model that supports understanding and design of
social media. Our goal is to describe a design space that separates meaningful
deep differences in social media design, while placing together designs that
may appear separate at first glance but share deeper DNA than might otherwise
appear. We place our focus on social media designs, which we define as systems
that directly facilitate the sharing of user-generated content between users.
Taking a wide lens on this definition, we include traditional social media
such as Facebook, Twitter, and Reddit, but also other user-generated content
platforms for communication such as group chat (e.g., WhatsApp), email, video
discussions (e.g., Zoom), and semi-synchronous chat (e.g., Slack). In these
systems, users engage with others primarily by adding their content to a
growing repository of content in response to others’ content, forming cross-
user dialogue. This definition leaves out systems where users engage by
successively iterating over the same content and overwriting each other’s
work, as in collaborative document editing (e.g., Google Docs). We choose to
focus on differences in features and design because they have influence on
specific classes of behaviors (Kraut and Resnick, 2012) and are directly under
the control of the platform designer, though of course they do not determine
socio-technical outcomes.

Figure 1. The Form-From model describes social media designs along two main
dimensions: (1) The form or shape that the content takes, either flat as in a
chatroom or threaded as in individual posts with linked comments, and (2) from
where a user receives content, as from spaces such as channels, from networks
such as a friend graph, or from a platform-wide commons such as an algorithmic
For You page. For simplicity, examples in this figure are coded as of their
design at their initial launch.

We describe a model of social media designs that we call Form-From (Figure 3).
We derived this model through an inductive method and purposive sampling of
public platforms and social computing research systems. The Form-From model
focuses on two main dimensions of difference. The first, the Form dimension,
describes the principal form (shape) of the main content on the platform: is
the design threaded by emphasizing explicitly linking posts to each other, for
example through replies, or is it flat where the design emphasizes posts that
are not explicitly linked to each other, instead focusing interaction with the
overall stream? The second, the From dimension, describes the principal
process by which content is delivered from one user to another: is content
delivered to the user from spaces (e.g., channels, groups, or rooms) where all
members in the space receive it, from (social) networks where each user
independently curates a set of friend or follow edges that determines what
they receive, or as a sample from the “commons” of all content on the
platform? While platforms can blend elements of different options, they are
often principally designed around one of the six cells in this 2x3 Form-From
model. This model helps make comparative sense of platforms, for example
placing Instagram’s delivery as principally based on a social network, where
TikTok’s is principally sampling from the commons, despite both primarily
delivering visual content. Likewise, we characterize Discord channels as being
principally flat, vs. traditional forums as being principally threaded,
despite both being text-based group discussion platforms that support threaded
replies and comments on posts. In fact, when Discord launched a feature called
“forum channels” that forced all comments to be in reply threads, it flipped
that bit from flat to threaded, deeply changing the social dynamics in those
channels.

We derived Form-From by first generating a larger set of 62 dimensions that
aims to summarize the key types of differences between social media systems
via an inductive process covering a large number of industry and research
systems. We hierarchically grouped these 62 dimensions into ten categories:
Discussion Structuring (simplified to “Form”), Delivery (simplified to
“From”), Temporality, Audience, Membership, Moderation, Governance,
Extensibility, Signaling, and Content. While the Form-From model captures what
we argue are two key dimensions of variation across social media designs,
these ten categories and 62 dimensions serve as a fuller treatment for
comparing similar designs. For example, while Twitter, Mastodon, Threads, and
Bluesky all occupy the threaded-network cell of the Form-From model, they
differ in their approaches to Governance, Moderation, and Extensibility.

We also apply our Form-From model to make sense of the history and possible
futures of social media design. First, we trace popular social media platforms
as they arise over time and observe a pattern of migration from early
“flatspace” systems in the 1970s–1980s, to threaded spaces, to networks—again
first flat, and then later threaded—in the 1990s–2010s, and now toward
threaded commons designs. We also explore design patterns that occur within
cells of the model, for example the prevalence of a highly visible channel
list navigation pane in the flat space cell.

Our contributions in this paper are:

  1. (1)

The Form-From model, which enables comparative analysis of designs and design
patterns in social media;

  2. (2)

A finer-grained description of the design space using ten categories
encompassing 62 dimensions for higher-resolution comparisons;

  3. (3)

Applications of these models to analyzing social computing and social media.

To begin, we will review the history of design space models in HCI/CSCW and
design more generally. We will then introduce our method and our models of the
social media design space beginning from the coarsest (Form-From) to finest
(the raw 62 dimensions). Finally we walk through some applications of the
models.

##  2\. Background

###  2.1. Design Spaces

Models for technical systems have a long history in the HCI/CSCW literature.
As simplifications of the characteristics of HCI systems or of HCI uses,
models have proved exceedingly helpful. Roughly, there are two strands of work
in HCI/CSCW that develop and analyze design spaces and models for those design
spaces. The most detailed ones are based on Simon’s work (Simon, 1996), where
design was viewed as search through a problem space. Researchers following the
Simonian view have largely construed design spaces as parameterized spaces of
characteristics that support algorithmic search. (While design spaces as a
concept were also considered in engineering and operations research, e.g.,
Zwicky (Zwicky, 1967) and Jones (Jones, 1992), Simon’s descendants were the
ones who brought design space exploration to HCI/CSCW.) In this view, a
complete design space is literally the space of all possible designs, where
each dimension of the space is a technical feature or characteristic for some
collection of systems.

The earliest such work in HCI/CSCW that we know of is Card, Mackinlay, and
Robertson’s work on input devices (Card et al., 1990, 1991). There (Card et
al., 1991, p. 101), the authors argue they found it necessary to “represent
the designs as points in this design space [of input devices], some parametric
representation [must be] determined that can represent the central idea of
particular designs.” Similar work has continued in HCI/CSCW, largely in the
area of interaction devices and visualizations. For example, some researchers
laid out the design space of windshield display apps for automobiles
(Haeuslschmid et al., 2016) or conversational in-vehicle information systems
(Braun et al., 2017). Other work laying out design spaces within HCI/CSCW
include head-mounted displays (Hirzle et al., 2019) and electronic health
record visualizations (Belkacem et al., 2017).

Outside of HCI/CSCW, design space analyses of hardware or devices flourish,
tending to consider well-contained sets of characteristics. Example analyses
include multimodality as a systems consideration (Nigay and Coutaz, 1993),
self-adaptive systems (Brun et al., 2010), mobile medical devices (Kulkarni
and Öztürk, 2007), and communication architectures for systems-on-a-chip
(Lahiri et al., 2000). In practice, the feature sets are relatively small and
therefore tractable to this approach.

The second strand of design space work is based more on Schön’s view of design
(Schon, 1984). A Schönian view emphasizes the serendipity and messiness in the
design process. In this view, a design space is, in Gaver’s (Gaver, 2011)
terms, the curated collection of design concepts. “Design space” as a concept
is different than in the Simonian view: “The ideas presented in these
artifacts are said to occupy a metaphorical landscape of design opportunities,
rather than a Cartesian space of possible designs; and their purpose is to
inspire rather than prescribe design choices” (Dove et al., 2016).
Exploration, then, is through a set of design prototypes that loosely define a
space of potential designs. In practice, what people mean when they talk about
this type of design space analysis is often the use of a highly simplified,
conceptual, and emergent model through which they can explore design
alternatives and potentially their rationales (e.g., (Lim et al., 2008)).
While this use of “design space” points to an important consideration in
HCI/CSCW, especially in Research through Design (Zimmerman et al., 2007), in
this paper we take a Simonian view and focus on demarking the technical
features.

We note that some Simonian analyses also focus on a limited number of key
dimensions. For example, Feng, Yao, and Sadeh’s (Feng et al., 2021) purpose is
to “help researchers and practitioners better understand the key dimensions to
be considered when designing privacy choices for their systems.” They
therefore focus on what they consider to be the most important five dimensions
in their design space. Simplifying a complex design space is likely useful for
those designing systems; we will discuss this further below.

We also recognize the considerable amount of work in HCI/CSCW that has focused
on socio-technical design spaces that include elements of user tasks or
requirements as well as technical features. As one example among many, Lee and
Paine (Lee and Paine, 2015) contribute a model of all coordinated distributed
work. Their model includes technical capabilities, but it also includes the
planned permanence of distributed action and the turnover rate of
participants. While we recognize the importance of this and similar work,
again the emphasis in this paper is on technical features.

In summary, the literature presenting models of design spaces in HCI/CSCW has
been split.222Some researchers have attempted to reconcile the two, pointing
out that Simon also wrote about the “search” in a “design space” in a
metaphorical manner (e.g., in Sciences of the Artificial (Simon, 1996)). We
also note that Simon wrote at the same time as Zwicky (Zwicky, 1967), with his
morphological analysis, which is closer to what we have labeled as Schönian
design spaces. Nonetheless, we find it useful analytically to separate the
Simonian and the Schonian as to highlight the differences. Given we want to be
able to relate different social media designs conceptually against one
another, along with having the additional goal of finding new types of social
media systems, we believe that laying out a detailed description of the
technical characteristics in a Simonian design space analysis will be the more
valuable approach here.

###  2.2. Design Spaces for Social Media Systems

Figure 2. Johansen’s time-space matrix (Johansen, 1988) and Grudin’s time-
space categorization (Grudin, 1994) are two classic models of CSCW systems.
They both split the design space by whether the people are interacting
synchronously or asynchronously, and by whether people are in the same
physical location. Most modern social media systems fall into the same
“different time–different place” cell of these models.

There is scant work examining the design space for social media systems.
Perhaps the most well-known model in CSCW, seen in Figure 2, is what is
commonly known as Johansen’s time-space matrix (Johansen, 1988) for
collaborative systems, consisting of a 2x2 matrix of location (same place-
different place) versus time (same time-different time).333An even earlier
version of this matrix can be traced back to DeSanctis and Gallupe (DeSanctis
and Gallupe, 1984) on Group Decision Support Systems (GDSS). They had a 2x2
matrix of “dispersion of group members” (close proximity or dispersed) versus
“duration of decision-making session” (limited or ongoing). This matrix went
on to inspire both Johansen and Grudin and became popularized as Johansen’s
time-space matrix. Grudin later expanded upon this to be a 3x3 matrix (Grudin,
1994). The Johansen and Grudin time-space matrices could be considered types
of “design space” analyses, albeit relatively simple ones. At the time, these
models were foundational in guiding both research and commercial products by
clarifying the types of systems that might be constructed or considered.

However, we believe they are not well suited to discussing social media
systems, which now encompass a substantially larger range and complexity. When
we consider widely-used systems that facilitate social communication today,
some are commonly used for work within organizations (i.e., Zoom, Slack,
email), which was the focus of early CSCW research. But others, used just as
widely if not more, primarily host activities outside of traditional groupwork
(i.e., Facebook, Instagram, TikTok, WhatsApp). When we do consider this
broader set of systems, a wide range of the most popular social communication
tools—Slack, email, Facebook, Instagram, TikTok, and WhatsApp—which have
significant diversity of function, would all fall into the same cell of
Johansen’s matrix: remote and asynchronous.

There are relatively few other design space analyses of social media systems.
A recent development analyzing design spaces for social media systems is
Rajendra-Nicolucci and Zuckerman (Rajendra-Nicolucci and Zuckerman, 2021).
Rajendra-Nicolucci and Zuckerman present many abstracted “logics” that can
inform the design of social media systems. Each of their illuminating
analyses, which they argue create something similar to a bestiary of types, is
informed by concrete examples. However, there is not a clear distinction of
dimensions of analysis, as one might want in a Simonian design space analysis.
Other analyses focus on examining specific dimensions of the design space.
These include Smith et al. (Smith and Burkhalter, 2000) as well as Gilbert
(Gilbert, 2012) among others. Gilbert characterizes social media systems
through the prism of different kinds of awareness, demonstrating how different
designs about social translucence derive from different decisions about who
can see what between triads of users. Smith, Cadiz, and Burkhalter (Smith and
Burkhalter, 2000) examined new types of conversational organization, such as
what they called threaded chat and conversation trees. These are all valuable
additions to understanding the design space of social media systems, but we
believe it would be valuable to see a more complete representation of the
entire design space.

It is time to develop a model better-matched to systems that incorporate
social communication; we need a new Simonian analysis and design space that
adequately encompasses and distinguishes the major systems of today.

##  3\. Method

Our process of generating a design space was inductive, beginning with a
survey of relevant research and industry systems, continuing with the
generation of a large number of low-level dimensions of variation between
those systems, then refining iteratively to reduce those dimensions to a small
number of central concerns for the design space, and finally holding up the
design space against the actual systems to test its validity and expressivity.

###  3.1. Scope and Definitions

To make our modeling effort tractable, we limit our scope to social media
systems: systems that facilitate the sharing of user-generated expressive
content, or media, from one user to others.444Merriam Webster definition of
social media: forms of electronic communication (such as websites for social
networking and microblogging) through which users create online communities to
share information, ideas, personal messages, and other content (such as
videos). In such systems, social activity revolves around users creating
content ab initio or in response to other users’ content, primarily in an
appending fashion. Such social media systems include systems that would not
fit neatly into classic CSCW/groupware, including systems that are designed to
be used primarily for entertainment (i.e., by surfacing highly engaging
content). Conversely, some CSCW systems, such as Microsoft Excel with its
multi-user cell editing capabilities, or Wikipedia article pages with their
collaborative editing of documents, would not fit into our concept of a social
media system due to the attention to editing over appending and the lack or
paucity of feature support for user responses or conversations. Our scope
covers an enormous number of CSCW/social media systems, but is sufficiently
contained to provide analytical traction.

Consistent with this goal, our analysis focuses on each system’s designs for
sharing, publishing, and communicating content, and gives less attention to
the (possibly sophisticated) affordances for creating the content to be
shared.

Even beyond social media, many CSCW systems do enable some form of back-and-
forth messages in a group or broadcasting of content to a large audience,
since having a communication channel is typically necessary to support
coordination. But importantly, we distinguish the communication portion of
these systems from their central arena for coordination (more detail to come
in Section 3.1.1)—so while Wikipedia Talk pages would constitute a social
media _surface_ , Wikipedia articles would not.

####  3.1.1. Surfaces

Today’s social media platforms are multifaceted and incorporate multiple sets
of system designs for social communication. Due to these complexities, we also
classify distinct “surfaces” within a system instead of categorizing systems
as a whole. Many platforms today incorporate a combination of different
internal subsystems, each with different user-facing system designs, with more
or less integration; for instance, “Facebook” as a whole mashes together
Facebook Groups, Messenger, Pages, and the original Facebook News Feed. It
would be difficult to classify Facebook as a whole given the differences in
how each of the different internal systems work, even though they can all be
accessed from the same website.

Within a product surface, there is a common way that social communication
happens and a common vocabulary for describing social actions and user-facing
objects that is shared among all users. When two product surfaces appear in
the same social media platform, there may be no integration outside of a
common system for user identities and accounts that lets a user easily move
from one product surface to another. Or there may be significant integration,
such that communications that happen in one product surface get transmitted to
or accessed via another.

In our analysis, we spent substantial time trying to determine when systems
had sufficiently changed to be considered separate surfaces. This problem is
important only when considering how systems change over time (e.g., gathering
additional capabilities so as to compete with novel systems) and in mapping
dimensions to systems. Otherwise, determining the exact instance of a long-
standing system (e.g., Facebook after likes and before likes) is not crucial
to the development of our design space; as long as we have considered the
various instances of a system, we have obtained all of the attributes across
the instances to help make our design space more comprehensive.

Overall, in this paper, we have tried to be precise when we mention a system
to name the specific product surface we are analyzing; however, in cases where
we perceive the system to have a primary surface, we use the system name as a
shorthand for the surface. For instance, when we use the term “Instagram,” we
are referring to the Instagram Feed; when talking about secondary Instagram
surfaces, we use more precise names such as “Instagram Stories” or “Instagram
Reels.”

####  3.1.2. Technical System Design

In addition, we choose to focus our taxonomization of product surfaces
according to their apparently intended usage rather than the way they may have
been appropriated by their users for different aims. For instance, while users
may appropriate collaborative editing documents for back-and-forth discussion
by taking turns editing text into one location, they are not using built-in
technical features that were designed to facilitate communication.

####  3.1.3. Principal Uses

Finally, social platforms are complex, oftentimes involving multiple ways for
users to interact with each other, even when focusing on a specific product
surface. For instance, on Slack, a user can interact with others by reacting
with emojis, typing a new message, starting a thread, or several other ways.
Is Slack a flat system with some threaded elements, or a threaded system with
some flat elements? In these cases, we try to determine the central or
principal way that users interact with each other within the system in terms
of the focus of the system design, and then label according to that principal
use, while ignoring the ways that are more peripheral. In the case of Slack,
we chose the social communication that happens in the text chat. Likewise,
while Slack has added support for threads, its principal use in most scenarios
remains a flat stream of messages—except in extremely high membership
channels, most discussion occurs outside of threads. We acknowledge that these
decisions are up for debate, and we note where we make these decisions. In
addition, we are not claiming that these peripheral modes of communication are
unimportant. But by focusing on the dominant functionality we can more easily
classify existing systems into higher level patterns.

###  3.2. Inductive Process

Figure 3. The Form-From model (left), the focus of our paper, is a
distillation of a larger design space that we articulated and synthesized
through our inductive process. We began with a review of systems and platforms
to produce a large, maximal model that articulates 62 dimensions that we
considered salient (right). We then iteratively grouped those dimensions
hierarchically into 11 categories (middle), and finally selected and distilled
them into two dimensions for Form-From.

Our first step was to gather contemporary and historical social media systems
that our design space should describe. We sought to include a wide range of
such systems, including systems that are in industry, community-owned, and
research. Rather than a systematic literature review, we performed purposive
sampling (Palinkas et al., 2015), a standard interpretivist technique in which
we selected research papers and public platforms that we considered were
particularly influential, or had articulated interesting new areas of the
social media design space. Papers were drawn from the CSCW and social
computing literature, e.g., the CHI and CSCW conferences.

Second, we developed a large number of design dimensions that described
differences between the systems. In generating these dimensions, we focused on
differences in the design or technical affordances of the systems. Each
dimension was categorical, requiring zero or more values. Example dimensions
included: are replies broadcast to the entire group, or not? What powers do
administrators have? Are comments required to be anchored to a piece of
content (e.g., photo or video), or not? In creating these dimensions, we
sought “differences that make a difference” in the system design. Our focus
was on technical and design system decisions, rather than sociotechnical
outcomes: in other words, axes could capture what user-visible features varied
across platforms, but not how users of those systems organized themselves
using those features.

We inductively grouped these design dimensions into higher level categories,
for example Audience, Membership, and Moderation. Following a process similar
to thematic analysis, we coded dimensions with potential higher level
categories, grouped them, reflected on the categories, and regrouped. The goal
of this process was to provide a higher level organizational structure to the
dimensions.

We then took these dimensions and categories and used them to identify
additional systems that were not included in our initial set. To do so, we
sampled underpopulated areas in the multi-dimensional space, then sought out
systems that might fill those holes. For instance, the consideration of
structured argumentation systems in both research (e.g., ArguLens (Wang et
al., 2020), MIT Deliberatorium (Klein, 2012), ConsiderIt (Kriplean et al.,
2012)) and industry (e.g., Kialo,555https://www.kialo.com
Arguman,666https://github.com/arguman/arguman.org Pol.is777https://pol.is) led
us to incorporate dimensions that capture when systems have a constrained set
of post types and link types. In addition, we reflected on systems that did
not align with any of the currently existing options for a given dimension and
introduced additional options to cover them.

We continued this iterative process until theoretical saturation. In our
iterative process, the additional systems prompted revision of the dimensions,
which prompted revision of the higher-level categories, which then led us to
continue to generate additional systems. In total over a period of around six
months, the authors individually annotated and discussed over 100 systems in
total, with eleven of them receiving full annotations across all 62
dimensions: WhatsApp, 4chan, Quora, Zoom, Slack, OhYay, email to specific
individuals, mailing lists, Reddit, Twitter, and the New York Times comments
section.

###  3.3. External Validation of Dimensions Against Systems

As the dimensions and categories stabilized, we recruited four undergraduate
students at our institutions to apply the full 62-dimensional design space
coding to a series of 15 concrete systems with which they all had some
familiarity: Facebook Messenger, Twitter, GroupMe, Discord, EdStem, Piazza,
Google Hangouts, Google Docs’ inline commenting, Clubhouse, Instagram,
Instagram Stories, YikYak, Reddit, Snapchat, and TikTok. Initially, all four
students coded one system surface very familar to all—Instagram Stories. They
then met as a group with two of the researchers to discuss points of confusion
and disagreement in the codebook. After gaining a better understanding of the
dimensions through this exercise, each student went on to separately and
individually code one system at a time, focusing on systems with which they
were familiar and that seemed different from what had already been coded. We
would then meet as a group to discuss the codes. As these students identified
unclear decision points, or systems that did not fit the existing set of
dimensions, we further refined the dimensions and categories. We stopped after
the students collectively coded 15 systems, as the codebook had stabilized at
that point. We decided that we had achieved theoretical saturation when we saw
no new dimensions or values. This is analogous to theoretical closure in
interpretivism; no additional valuable data suggests that data collection can
end.

Our final full set of dimensions included 62 dimensions along ten categories.
A list of the full dimensions and values appears in Appendix A.

Noting that 62 dimensions, and even ten categories, is a lot to remember, and
that simpler models can be more analytically tractable and powerful, we then
(again iteratively) distilled the ten-category model to two dimensions—From
and Form—that we felt captured the most salient dimensions of technical
design. More so than any of the other dimensions, these were the two we turned
to again and again for discussion and that we noticed having an impact in
multiple places across our 62-dimension space. Finally, through discussion, we
arrived at the 2–3 options within each dimension that best divided the systems
into meaningful and coherent groupings.

##  4\. The Form-From Model

Figure 4. Examples of systems both old and current in each cell of the Form-
From model.

###  4.1. 2x3 Model

Our highest level model of content sharing systems distinguishes between two
major dimensions: _Form_ categorizes on the shape or structure of the
principal unit of content, and _From_ , on how content is delivered to users.
Example screenshots of different systems in each cell of the 2x3 can be found
in Figure 4.

####  4.1.1. Dimension 1: The Form of Content

Our first dimension describes the way that social content is organized, or
what form the content takes at a high level. The two Forms are _threaded_ and
_flat_. In a threaded system, the design emphasizes explicitly linking posts
to each other, for example through replies. Example threaded designs include:

  * •

Twitter, Mastodon, Threads, and Bluesky, which center attentions, replies, and
reshares around individual posts, and where replies are posts that can be
reshared much like top-level posts;

  * •

YouTube, SoundCloud, TikTok, and other video or audio sharing sites, which
privilege individual pieces of media and collect comments and reactions on
them;

  * •

Reddit, which orients its design around upvotes and replies to top-level
posts;

  * •

Traditional forums, which organize top-level posts chronologically and allow
replies to them.

In contrast, in a flat design, the design emphasizes posts that are not
explicitly linked to each other, instead focusing interaction on the overall
stream. Example flat designs include:

  * •

Traditional chatrooms such as IRC, which feature an undifferentiated stream of
chats;

  * •

Instant messaging platforms such as AOL Instant Messenger, Yahoo Messenger,
WhatsApp, and Facebook Messenger, which similarly focus attention on the
overall stream of chats rather than individual posts;

  * •

Initial designs of platforms such as Facebook, MySpace, and LinkedIn, which
showcased a stream of comments on a user’s wall rather than enabling threaded
replies;

  * •

Video and audio livestreams such as Zoom, Twitch, YouTube Live, and Twitter
Spaces, which are uncut and ongoing streams of video and audio;

  * •

Instagram Stories, Snapchat, and other “stories” systems where users view a
stream of video clips or images that disappear after a short period of time.

In some ways, the Form dimension correlates with the synchronous-asynchronous
divide in the “time” dimension of the Johansen matrix, in that systems where
people predominantly interact synchronously tend to appear in the Flat
category, while systems where people predominantly interact asynchronously
tend to appear in the Threaded category. However, we argue that in today’s
social media, time has become a less useful way to distinguish systems, as a
vast number of systems are used today in a semi-synchronous fashion, making
the synchronous-asynchronous divide more of a continuum. For instance, instant
messaging, chat systems, and multi-user editors all appear in the synchronous
side of the Johansen matrix (Johansen, 1988)—yet all of these systems can be
used asynchronously and in many cases are predominantly used asynchronously
today (Wang et al., 2017). Instead of focusing on usage, which can change as
norms change, our focus on the fundamental shape of user-to-user interaction,
as conveyed by the underlying design and data model, enables us to more
cleanly separate systems and also to pinpoint what design choices facilitate
certain kinds of interactions. For example, while both chat and email can be
and are used both synchronously and asynchronously, they seem optimal for
opposite modalities. Chat works well in a synchronous setting: since each post
is typically replying (if at all) to the one immediately previous, there is
little need for presenting complex reply threads and a flat chronological
stream offers a simpler user experience. In contrast, email works well for
asynchronous conversations, because the explicitly represented threading
structure supports navigation of multiple distinct conversations overlapping
in time.888Temporality does emerge as an orthogonal category in our
10-category expanded model, with many facets that go beyond synchronous-
asynchronous interaction. Conversely, if any one channel is going to be used
for multiple simultaneous conversations, some form of threading is generally
required in order to keep those conversations from jumbling together.

We now describe each option in more detail and mention common subspecies
within each.

##### Threaded Systems

In this option, the design emphasizes explicitly linking posts to each other,
for example through replies. Each piece of content can be a post sent by a
user that kicks off a series of posts that are all linked back (possibly
indirectly) to the initial post. Meta’s system Threads, appropriately, is
threaded by this definition.

There are different subspecies within this distinction. For example, threaded
designs often have a form that we call _headed_ , as top-level content is
treated differently from replies. Examples include Reddit and Facebook posts,
where the top-level posts are quite distinct from replies; for instance, only
top-level posts can appear ranked in one’s feed. The “head” that kicks off
discussion can also be its own distinct form of content, such as a news
article, a document, or a video, that receives comments on it. Examples
include YouTube and TikTok, where the initial content is a video, Instagram,
where the initial content is an image, or comments sections of news
organizations such as the New York Times, where the initial content is a news
article. In general for these platforms, the only way to reach the comments
hanging off a particular head is via that head content itself—e.g., the
comments for a YouTube video can only be found on that video’s page. For these
kinds of platforms, a further distinction involves whether there are one or
more sub-threads that branch from individual replies. For instance, Reddit and
Twitter allow for infinite branching of replies into sub-threads, while other
platforms only allow a single thread or one or two levels of branching.999This
distinction surfaces as two of the possible options in the dimension
“Threading/Partition of Replies” in our 62-dimension model (A.1.3).

In contrast, in _headless_ designs, responses to posts are first-class objects
in their own right, with all the affordances of non-replies. Twitter and the
various Twitter-like systems enable this by treating replies as posts that can
be engaged with just like the initial tweet, such as via “retweeting” the
reply, i.e., sharing just the reply into others’ feeds. In contrast to
headless Twitter, Facebook is headed. A reply to a tweet can show up
(initially) by itself in the feed of someone who follows the replier. But if
someone comments on a Facebook post, the _entire post_ (a head) with all its
comments, not just the single comment, shows up in their friends’ feeds. The
system has a built in assumption that heads are necessary to provide context
for the comments under them.

Some systems are hybrid. TikTok’s “Duet” or “Stitch” feature, which permits
quoting or embedding another video in one’s own, constitutes a headless design
as the “reply” becomes its own top-level video post. However, the textual
comments on TikTok videos follow the “headed” design. So it is not clear which
subspecies to assign to TikTok. It is threaded under either consideration,
however.

Yet another _annotation_ subspecies involves systems where comments or threads
of conversation are attached to _a specific location_ in the “head” piece of
content (A.1.6). This includes examples such as adding a comment to a
selection of text in a Google Doc; annotating a song lyric on Genius or a
selection of text from a web page or pdf document using
Hypothes.is101010https://hypothes.is or Nota Bene (Zyto et al., 2012); or
attaching a comment to a particular timestamp in a video on Facebook Live. The
document or video is the central piece of content, and comments are threads
that hang off it but are anchored to a specific passage or point in the
content.

##### Flat Systems

In contrast, flat designs do not connect different pieces of content through
structured links. Instead, the design presents a stream of unconnected posts.

In many of these systems, content organized in a flat way is presented
chronologically in posting order, so that conversations can occur where users
respond to content that appeared recently in time. Importantly, this
referencing of other content happens in an unstructured way via dialogue and
is not part of the data model of the system. There is also the rarer case of
systems where content is not presented chronologically while appearing in a
stream; an example includes Instagram Stories, where disparate images or clips
of video from different users appear one after the other in a non-
chronological order (content from the same user still appears
chronologically).

In terms of subspecies, content within streams can include _discrete_ content
that is broken up into distinct units within the data model, such as a chat
message, an image, or a recorded video, where users can view a stream of units
one after the other. Another _continuous_ subspecies is content that is an
unbroken stream of data shared between participants, such as a live audio or
video stream, where dialogue and turn-taking happen in an unstructured way. It
is not uncommon for there to be multiple interleaved flat streams of different
media types; for instance Zoom offers a live video stream interleaved with a
text chat stream in the same space.

In some primarily flat systems, there may still be affordances for placing
links between pieces of content; however, they often go unused in favor of
simply relying on chronology. For instance, while systems like Slack and
Discord111111More recently, Discord has added a new product surface called
“forum channels” where comments are required to be in reply threads:
https://discord.com/blog/forum-channels-space-for-organized-conversation. This
is similar to the threaded chat system Zulip: https://zulip.com. We would
argue both these systems are examples of threaded systems by our definition.
permit “reply in thread,” one often does not need to do so, as each channel is
organized chronologically. Similarly, one can embed another user’s post within
one’s post in an Instagram Story; the same is true for many chat apps such as
WhatsApp and iMessage, where one may quote another’s post to signal a reply.
In all these cases, quoting is not emphasized or required for participation.
Indeed, while one can attempt to carry on a threaded conversation using
quoting in some of these platforms, it would be challenging. There is also no
option for threaded discourse under a Story in Instagram; instead, replies to
a user’s story appear as a private direct message to that user with a
reference to the story inline in the chat.

####  4.1.2. Dimension 2: Where Content Comes From

What determines which content you see when you visit the platform? Our second
dimension is concerned with how content is delivered from one account to
another. This includes users as both senders of content, where users have some
ability in the system to designate how or to whom content is sent, as well as
receivers of content, where users have some method of accessing content or
determining how to locate a piece of content to which they have access.

We define three options for the From dimension:

  * •

Spaces, where a user receives content that has been submitted to certain
virtual locations, channels, or groups that the user “joins”, as in Slack,
Microsoft Teams, or group chat;

  * •

Network, where one receives content from specified friends or follow networks,
as in Facebook, Twitter, and Instagram;

  * •

Commons, where the user notionally draws their content from the entire
platform, as in TikTok or Pinterest.

We go into more detail below to characterize each option, mention common
subspecies, and also discuss cases where platforms blend multiple options
together.

##### Spaces

In this option, content is exchanged in a distinct space with a unique
location defined by a name or address. Space-based designs evoke the concept
of physical rooms and buildings. Other offline analogies of physical spaces
that researchers have used to describe social media include bulletin boards
and town halls. In order to post to a space or access content from a space, a
user must designate, navigate to, and potentially join the space’s unique
name, address, or location. Because they are defined by this unique location,
in some platforms, spaces can persist even after everyone has left. Example
platforms that utilize spaces are online news aggregators (e.g., Reddit
subreddits), group chat (e.g., Slack, Discord), and conversations around
individual pieces of non-social content (e.g., New York Times’s article
comments section, Google Docs comments).

Spaces may be open and have a location that is discoverable to the public,
allowing anyone to freely find and enter the space. Spaces may also be hidden
or access controlled. Spaces also often have metadata in addition to a unique
name, such as a description, a set of rules or code of conduct, and
individuals tasked with moderating the space that, together, give the space a
unique set of norms and culture.

For those who have access to the space, once they are at the location, they
can peruse and potentially engage in conversations that occur within the
space. Conversations in the space may be equally accessible to all people who
have entered the space, or there may be further distinctions, e.g.,
_subspaces_ within a space, such as channels within Discord or Slack or groups
within a WhatsApp community, where each subspace also has its own unique
address or location within the larger space. Other systems take the physical
space analogy farther; for instance, in social VR systems, after one joins a
room, distance to other avatars in a 2D or 3D environment determines whether
they can hear a conversation.

One instructive example is group chat: while it may be tempting to consider
group chat as a network-based design since the recipients are specific
accounts, group chat is properly a space-based design because all members of
the group chat “room” receive all messages—and many group chat platforms such
as WhatsApp even allow or encourage naming the group chat like a channel and
assign an admin to manage membership. Similarly, we would characterize
directed mailing, messaging, and calling systems (e.g., email, SMS, Zoom) to
be spaces. For example, although email delivery is typically individual to
individual, suggesting a network model, when a user composes a message to a
group of recipient, followup conversation typically makes use of “Reply All”
to engage all the original recipients. So the original sender essentially
creates a space in which all follow-up messages are typically delivered.

##### Networks

In network-based designs, content flows between people (or other entities with
_accounts_) who have connected to one another. Friend and follow networks are
prototypical examples. In network-based designs, each account must have a
unique way of identifying itself so that other users may find and connect to
them, such as a unique username, address, or phone number. Typically, in such
designs, users make friend or follow connections as they explore, or utilize
discovery mechanisms to find accounts, either in-platform (e.g., YouTube
recommendations) or off-platform (e.g., links to one’s Patreon on another
site).

Traditional social network sites are the classic examples of social network
delivery, as in the Facebook News Feed. Platforms that are centered around
users subscribing to a creator’s or influencer’s content are also networks,
such as Patreon, Substack, or YouTube. Systems that implement “stories” such
as Snapchat121212Though Snapchat’s Group chat surface would be considered a
space. and Instagram Stories are also networks as they involve broadcasting
short disappearing videos and images to one’s friends.

In contrast to spaces, where there are unique shared locations where people
can gather, and any user in the location can access that content, networks
distribute content from account to account. Importantly, this means that while
spaces provide common knowledge and shared context—everyone in the space can
access the content posted in the space—in networks everyone sees a different
subset of content, and it is not always clear what subset others see (Gilbert,
2012).

##### Commons

Some designs feed content to users without requiring any following, joining,
or subscription at all. Instead, users receive content from everyone and
anyone on the platform. Algorithmic ranking is often central to commons-based
designs. The most well known commons design as of writing is the “For You”
feed as popularized by TikTok, but this also includes Instagram’s “Explore”,
Snapchat’s “Discover”, Reddit’s “Best” or logged-out surface, and the
Pinterest feed. To the extent that users’ profiles are the main content to be
shared in a network like Tinder, or that video streams are matched together
randomly on ChatRoulette, we may also view them as commons-based designs. The
algorithms used for distribution can be complex, with many factors and methods
of inference, or simple, e.g., random or chronological algorithms.

##### Blended Delivery Mechanisms.

It is not uncommon for platforms to blend multiple delivery mechanisms (our
“From” dimension) for the same content or in the same product surface,
particularly as algorithmic feeds have become more popular in recent years. In
each case, we identify a primary method of delivery by examining the defaults
in the system.

There are many systems that combine both social networks and spaces. Examples
of platforms where a follow graph and a social network feed has been tacked
onto a space-oriented system include systems such as Quora, Github, and
Reddit. In these cases, the primary orientation is around spaces but users can
additionally follow individuals and see content from those individuals in a
home feed. Another example that is less clear-cut is Mastodon, where one’s
“Home” feed consists of content from the people one follows (a social
network), while one’s “Local” feed consists of content from all the people in
one’s local Mastodon instance, whether or not one explicitly follows them (a
space). Despite having distinct elements of both, we would characterize
Mastodon as having more emphasis on a social network model of delivering
content due to the defaults provided by its current main clients (i.e., less
emphasis on local content, more emphasis on one’s followed content).

Many platforms also blend commons designs with other delivery mechanisms. As
of this writing, Twitter’s For You page is 50% centered around follows and 50%
content from the commons based on a ranking algorithm. TikTok’s For You page
is more skewed—principally a commons design but somewhat influenced by the
follow network. Many social networks are beginning to augment their social
network approach with the commons, claiming that this delivery mechanism
increases engagement and content quality. For instance, Meta’s Threads app
recently launched in mid-2023, with more of a commons-based delivery as it
seeks to engage users who are still building a following graph.

Some platforms combine elements of all three delivery mechanisms. Reddit has
both spaces (subreddits) to which users may visit and subscribe, but also a
home feed showing content from subreddits to which users have not subscribed
and content from users they follow; however, in this case, we would still say
Reddit is still primarily oriented around spaces.

####  4.1.3. Ideal Types for Each Cell in the 2x3 Model

To help conceptualize the Form-From model, we find that it can be helpful to
consider Weberian “ideal types” (Weber, 2017) for each cell. In Sociology,
ideal types are an approach for building abstractions and theory in the face
of the complexities of the real world by creating hypothetical but clear
examples. In our case:

  * •

Flat spaces: a chat room with chats appearing in a traditional chronological
order

  * •

Threaded spaces: an online forum, with a top-level post anchoring each thread
and replies inside the thread

  * •

Flat network: a social network “wall” where friends can leave posts that
render in a reverse-chronological order (e.g., the original Facebook Wall)

  * •

Threaded network: a social network feed that features posts from followees,
where each post can receive comments and reshares

  * •

Flat commons: a site for viewing algorithmically curated content, without the
ability to reply or follow accounts

  * •

Threaded commons: a site for viewing and replying to algorithmically curated
content, without the ability to follow accounts

Ideal types such as these can help us characterize and categorize actually-
existing systems, which are rarely so clear-cut.

Category |  Example Axes
---|---
Discussion structuring |  How are contributions grouped (individual posts a la Twitter, stages or phases of discussion a la deliberation platforms, threads a la forums, or time bounded blocks a la Zoom meetings)? Is there a single channel, or multiple channels? How are contributions anchored (to posts, to timestamps, or to a spatial coordinate system?
Delivery |  How are contributions ordered (chronologically, algorithmically personalized, voted upon by the group, editorial fiat)? What can the user block (people, topics, replies to a post, by score, or nothing)?
Temporality |  Does the platform require synchronous interaction? How durable are the contributions (enforced ephemeral a la 4chan, default ephemeral a la Zoom, default archived a la Slack, or enforced archived a la Wikipedia)?
Audience |  What is the maximal audience one can reach (a bounded group, a networked neighborhood, or public)? What is the typical audience one reaches (an unknown subgroup of platform members a la Twitter, entire membership of the group a la email lists, public within the platform, or a pair or small group a la WhatsApp)?
Membership |  What are the criteria for joining (geofencing, referral from a current member, payment, identity characteristics, organizational membership, open)? What user roles exist (member, moderator, administrator)?
Moderation |  Who is empowered to make decisions (members, paid moderators, volunteer moderators, platform developers)? Are moderation actions transparent (invisible to members, visible to members)?
Governance |  How do members gain access to power (elections, reputation/karma systems, appointment by existing elites)? How are changes to governance determined (direct democracy, elite members decide, platform operators decide)?
Extensibility |  Are bots allowed (conversational bots, operational support bots)? Is there an available API (to extract data, to create bots, to contribute to discussion, to federate with other instances)?
User and Group Signaling |  How does a user convey themselves (profile image, display name, real name, avatar)? Are there availability indicators for users (here/away status, typing (“…”) indicators, location markers)?
Content |  What types of content are allowable (text, image, audio, video, avatar, drawing, emoji, etc.)? What kinds of authoring tools are available (AI-MC, filters/lenses, overlays, accessibility hooks, remixing/stitching)?
Table 1. The ten expanded categories of our Form-From model, and example axes
within each category.

###  4.2. 10-category expanded model

The Form-From model captures two principal dimensions of variation across
social media designs. However, a simple model cannot always capture dimensions
of difference that matter to a particular system. For example, Mastodon,
Bluesky, Threads, and Twitter all fall into the Threads + Network cell of the
From-Form model, but each of them can also be described by important
differences. For instance, Mastodon and Twitter differ strongly in their
models of governance and extensibility, and all four differ strongly in their
moderation strategy (Twitter’s is centralized and increasingly hands off,
Threads’s is centralized and the most adherent to traditional content
guidelines, Mastodon is decentralized to server administrators, and Bluesky
places it in the hands of end users through middleware).

This section describes the 10-category expanded model. This model
hierarchically encompasses all 62 dimensions in the full model. Depending on
the practitioner or researcher’s analytical goals, they might select different
axes than the two we identified above. Form-From captures axes of substantial
explanatory variation for questions of interest, but for some applications,
other axes such as Temporality or Audience may be the more relevant axes of
differentiation.

##### Discussion Structuring

This category most directly aligns with the Form dimension described above but
goes into more detail to characterize the different structures or data objects
that social media systems might take. In particular, whether the system groups
utterances into distinct “post” objects or not and whether these posts can be
linked to each other as chained reply “threads” or not are fundamental
differences in discussion structures. Other dimensions include: What depth of
threaded replies does the platform enable? Reddit has infinitely deep reply
trees, while Facebook displays only two levels of replies. Are some posts
distinguished from typical or generic posts? Q&A sites like Quora have
questions and answers, while Pro-Con systems like Kialo have initial
statements, followed by pros and cons.

##### Delivery

The delivery category most directly aligns with the From dimension described
above but is more broadly interested in different aspects of how content gets
delivered to users and the degree to which users can specify aspects of what
gets delivered to them (or more specifically, what they can see, find, or be
made aware of). Examples of dimensions we cover include: Is there a feed? If
so, how are posts ranked in it, and how are replies to a post ranked?
Messaging platforms like WhatsApp often partition content by spaces and have
chronological ranking within each space, while most social networks have a
personalized algorithm based on engagement metrics. Can users decide what
kinds of posts they want to see (i.e., subscription) or what kinds of posts
they don’t want to see (i.e., muting)?

##### Temporality

The temporality category parallels the time axis in Johansen’s time-space
model but with additional nuances. Social media systems can differ based on
the assumptions they make around synchronous interaction, as well as their
retention policies. The temporality category contains dimensions such as: Does
the platform require synchronous interaction? Platforms meant for real-time
conversations, such as Clubhouse and Zoom, do while many others such as
Soundcloud and YouTube do not. Likewise, how ephemeral is the content? Zoom
chats and 4chan posts disappear, while traditional internet forum submissions
are retained indefinitely.

##### Audience

Platforms often give users options about the audience they wish to reach with
their posts, but their defaults are also meaningfully different. For example,
for the past decade Facebook’s default audience has been friends-only, whereas
Mastodon, Twitter, and Bluesky are world-public by default. This category
contains dimensions such as: What is the maximal audience one can reach (a
bounded group, a networked neighborhood, or public)? What is the typical
audience one reaches? For example, an email list or WhatsApp group typically
reaches _all_ and _only_ members of the group, whereas platforms such as
Twitter and TikTok typically reach an unknown subgroup of platform members.

##### Membership

Platforms sometimes define themselves by who is present, or how people earn
membership. For example, the Metafilter community famously required a nominal
$5 fee to join, in large part to keep the community self-selecting to those
who cared enough about it to put in a little money. Others, such as YikYak,
require geofenced physical presence to join. The membership category contains
dimensions such as: What are the criteria for joining? (Options include
geofencing, referral from a current member, payment, identity characteristics,
organizational membership, and no requirement.) Or, what user roles exist
(e.g., member, moderator, administrator)?

##### Moderation

Moderation can be a differentiator between platforms: as Tarleton Gillespie
argues, “moderation is, in many ways, the commodity that platforms offer”
(Gillespie, 2018). The moderation category addresses questions about how
content moderation is performed on the platform. For example, this category
contains a dimension naming non-exclusive options for who is empowered to make
moderation decisions (e.g., members, paid moderators, volunteer moderators,
platform developers).

##### Governance

Who makes decisions about how a platform operates? Governance can lead to
differences in designs and socio-technical outcomes, for example whether a
platform provides tools and autonomy to groups or subsets of its users. How do
members gain access to power: elections, reputation/karma, or appointment by
existing elites? How are changes to governance determined: direct democracy,
elite members decide, or platform operators decide?

##### Extensibility

Platforms can be extremely customizable, if the developer allows it. Discord,
for example, is known for its variety of community-developed extensions,
whereas Microsoft Teams is more a walled garden. Extensibility includes axes
such as whether bots are allowed, including both conversational bots and
operational support bots. Reddit, for example, commonly allows bots, whereas
Twitter has been increasingly cracking down on bot accounts. Likewise, is
there an available API to extract data or to contribute to the platform from a
custom client?

##### Signaling: Users and Groups

How do accounts and groups signal their identity and availability to others?
Reddit allows groups to deeply customize the look and feel of their spaces,
whereas Facebook Groups are much more restrained. The Signaling category
articulates design decisions platforms make that shape how we project our
identity and behavior out to others on the platform, focused on aspects aside
from the main content itself. For example, how does a user present themselves
(profile image, display name, real name, avatar)? Mastodon profiles focus on
an image, display name and avatar, whereas Facebook has a real-name policy,
and 4chan offers no facilities for a profile or continuous identity at all.
Likewise, platforms offer different availability indicators: Slack uses
colored circles to convey a user’s presence or away status, but iMessage does
not; iMessage conveys typing (“…”) indicators, whereas WhatsApp does not.

##### Content

To the mass media, the type of content often defines a platform. Instagram was
“the photo platform”, while Twitter was the “microblogging platform” and
TikTok or Vine were the “short-form video platforms”. While we would argue
that the content type is less determinative than these portrayals might imply,
it still matters. What subset of content types are allowable on the platform:
text, images, audio, videos, avatars, drawings, emojis? Platforms may also
provide facilities for certain types of content, for example Instagram’s
filters and TikTok’s stitch creation tool.

###  4.3. Full model: 62 dimensions

Our full model consists of 62 dimensions, the representation of the complete
design space. Appendix A contains the full set of 62 dimensions that comprise
the ten categories above. Sixty-two is not a magic number, and we expect that
these dimensions, and their options/values, will need to evolve as social
media design does.

The full list of dimensions should be helpful as a checklist for designers:
every social media design must make decisions on each dimension. Not making a
decision means that the design does make a de-facto decision to go with the
typical or default value of the dimension. (For example, by default, platforms
permanently keep archives.) However, defaulting can lead to mismatched
expectations between users and platforms, and give rise to conflict later. So,
we advocate that one practical use of our full 62-dimension model is as a
design checklist for designers in the early stages of design. The full model
is also useful for deep-dive inspections of closely related systems. For
example, for research focused on social identity (Seering et al., 2018),
comparative analyses across platform design decisions related to Membership
and User and Group Signaling may be useful: What are the criteria for joining
the platform or a group? Can a single person hold multiple accounts?

In the following, we walk through a deep dive into just one of the dimensions
to explain each of the options, how we arrived at those options, and decisions
we made along the way. This serves as an example of the reasoning we used to
define the dimensions in our 62-dimension model.

####  4.3.1. Dimension: Main Location of Discussion

This dimension lives under our Delivery category and focuses on high level
questions about where social content resides, how it arrives there, and how a
user accesses that content on a platform. Given the name of the dimension,
spatial metaphors highly inform the options. Note this dimension can take on
multiple values.

For many systems, the way to access content is simply to navigate to a single
address, or a center, where all discourse is hosted and available. Further
dimensions in the Delivery category address how to find specific content, or
be notified of or subscribe to content, or in what order content is presented.
All of these questions have significant importance when a system is a center,
and thus has a large body of undifferentiated content in one place that needs
further ways to characterize and distinguish content. Indeed, most systems
with this option operate some sort of feed for readers.

A second option we observed for the dimension Main Location is rooms—a special
case of spaces in the From dimension—where instead of one center where all
discussion takes place, the space is first divided further into distinct
addressable partitions, where users can enter the room to peruse content or
post to it. Examples include Slack channels and Facebook Groups. One debate we
had around this option is whether hashtags as popularized on Twitter, or tags
in general, should count as “rooms”. Eventually we decided that while tags and
rooms have some similar practical implications, they also have important
differences, namely that while a piece of content may have many tags, they
typically only have one main location (though can possibly be reposted or
shared elsewhere). This discussion is also what led us to title this dimension
“Main Location of Discussion”, recognizing that the same content can reside in
different places. Instead, tags and hashtags appear in separate dimensions in
this category such as Subscription (A.2.4) and Findability (A.2.6). A second
debate we had was whether profile pages on systems such as Facebook and TikTok
could be thought of as rooms, or whether these systems are centers. We
eventually agreed that while one’s profile page has some properties in common
with rooms, namely a unique address where content can be found, we still
classify these systems as center-based because their design is not primarily
organized around navigating to profile pages in order to see content. However,
a caveat is that early versions of some social networking systems like the
Facebook Wall before the feed and their predecessors such as Blogger were
room-like—one had to navigate to someone’s blog or wall to access any content.

Next, we separated out cliques, which are like rooms, but instead of having an
addressable name, cliques are distinguished by the membership of the shared
space. An example of this distinction is found in Slack, where there can be
channels with names like “#general”, but there are also multi-party direct
messages, which have no name but are distinguished by the participants. An
important difference between how rooms and cliques work is shared history—as
users get added or removed from a channel, the historical content stays
intact, much like one would expect of objects in a physical room where people
can walk in and out. Indeed, a room still exists even if it is entirely empty
of people. In a clique, however, if one adds a new user to a multi-party
message, it is now a new clique with its own distinct history. We have found
this subtle distinction leads to some interesting and unexpected behavior in
different systems. For instance, we found through investigation that Facebook
Messenger’s multi-party messages are closer to rooms than cliques, e.g.,
members of a multi-party message can add new people to the chat who can see
history from before they were there, and members can also rename the shared
chat as well as appoint admins. Facebook’s user experience confuses this
issue: if a user composes a message to the group by listing their names, and
they have messaged the exact same group previously, then Facebook drops them
into the preexisting group channel where previous messages can be seen. This
creates the illusion that communication is based on the clique. However, if
the group’s old room gets renamed, then messaging the same group results in
the creation of a new, different channel for the new conversation. From our
investigation, almost all group chat systems actually are designed more like
rooms.

The final option is mailboxes, where there is no shared location for
discussion but all content gets delivered to personal inboxes. Email is the
canonical example, where even if an email is removed from one participant’s
inbox, it is still available on all other participants’ inboxes, as they each
have their own copy. In addition, each user can choose to customize and use
their desired email client as opposed to the shared experience of a common
center or room. We discussed among ourselves how technically, most email
systems are now hosted on a centralized service via the cloud as opposed to
physically on a client device. Nevertheless the design of some email systems
is still reminiscent of physical mail deliveries. There are also increasingly
systems that would traditionally act like mailboxes, such as direct messaging,
that now have room features in their designs.

Our iterative discussions helped us isolate the important dimensions that made
their way to the 2x3 model. In the case of this dimension, we determined that
the design distinctions today between rooms, cliques, and mailboxes are
overall subtle, so when we went to summarize our dimensions in our higher
level 2x3 model, all of the systems with these values actually end up in the
Spaces option of the “From” dimension. In contrast, systems that have one
single center end up not having much of a spatial metaphor, due to the primacy
of algorithms curating a central feed reducing the need for much navigation.
Instead, these systems became spread out between the options of Network and
Commons according to their placement on lower level dimensions such as Ranking
of Posts (A.2.2), or in what order posts appear, combined with Maximal
Audience (A.4.2), or to what posts do users have access.

As can be seen, while some dimensions were relatively straightforward, others
such as Main Location required significant discussion and investigation. Our
discussions also heavily influenced how we formulated our 10 categories and
the 2x3 model. While a full accounting of each of the 62 dimensions would be
far too long to delve into here, this deep dive provides an example of how we
came to the possible options for each dimension, how we investigated their
values in different platforms, and the debates we had in shaping each
dimension and relating it to other dimensions.

##  5\. Application and Extensions of the Design Space

What can we do now that we have a design space? What questions can we answer?
We apply our model to a series of case studies in both product and research
systems.

###  5.1. Evolution of Social Media Over Time

The Form-From model allows us to explore how social media designs have evolved
over time. What path took the field from the early days of BBS to today’s
platforms?

To trace this path over time, we began with a timeline of major social media
platforms sourced from
Wikipedia.131313https://en.wikipedia.org/wiki/Timeline_of_social_media This
timeline named 77 social media platforms and the year of each platform’s
launch. We then deductively applied the Form-From model to each of these
platforms. Since the platform designs evolved over time, we applied a From-
Form code to the design as of the launch of the system: for example, when
Facebook launched, it was a flat-network system rather than a thread-network
system, since users could post on each others’ walls but there was no
functionality to reply or comment on each others’ posts. Our method involved a
single author coding all 77 systems in a first round through historical and
archival research on the web, then the remaining coauthors reviewing the codes
in a second pass with a discussion to resolve any disagreements. A full table
reporting the systems and codes is available in Appendix B. This historical
analysis was reliant on both primary and secondary sources available on the
internet about these platforms, and so we stress that future work may refine
our work here.

Figure 5. Social media systems followed a pattern over time of moving from
flat spaces (e.g., Talkomatic), to threaded spaces (e.g., Usenet), to flat
networks (e.g., the original Facebook), to threaded networks (e.g., Reddit),
to threaded commons (e.g., TikTok). This path does not describe every
system—it describes an overall pattern.

We can characterize social media’s evolution over time through five eras.
Figure 5 lays out the path of this evolution, and we discuss example systems
below.

##### 1970–1984: “Flatspace”

The 1970s and early 1980s were firmly rooted in space-based designs, and
especially flat designs. PLATO Notes (1974) was the only listed system in this
period to feature threading. Even email and Usenet, which were not included in
the Wikipedia dataset but qualify according to our definition, did not begin
to make widespread use of threading until the late 1980s: until then,
typically any context had to be added manually through quoting text, and
messages were interleaved in a chronological stream rather than grouped as
threads. Early flat, space-based systems included Talkomatic (1973) and BBS
(1980).

##### 1985–1994: Threads Help Manage Scale

In the earliest days, threading was less necessary because communication
volume was relatively small. However, beginning in the late 1980s and early
1990s, as these platforms grew, threading became more popular to manage the
load. Eudora, the email client that would introduce email threading to a large
audience (in part through the use of a “In-Reply-To” mail header), launched in
1988, and the trn threaded Usenet reader was launched in 1993. This era gave
rise to the model of the threaded discussion forum that we are familiar with.

##### 1995–2004: The Rise of Messaging and Networks

Beginning in the 1990s, social networks as we conceive them began to arise.
However, the first step was a snap back to flat space designs through the rise
of instant messaging systems, including AOL Instant Messenger (1996), ICQ
(1996), Yahoo! Messenger (1999), and MSN messenger (1999). These platforms
could be flat because small group conversations are much smaller in volume and
easily tracked without threading (especially when synchronous).

Starting in 2000, network-based platforms began a rapid ascent. In fact, while
network-based systems were relatively rare up through 2001, ten of the eleven
systems introduced in 2002-2004 were network based. Early network systems
included Classmates.com (1995), SixDegrees.com (1996), and LiveJournal (1999).
Then, there was a large crush of familiar network-based platforms launching
starting after 2000: Friendster (2002), LinkedIn (2003), MySpace (2003), and
Facebook (2004). Each of these were still flat designs, however, as (for
example) friends’ walls on Facebook were a flat stream of posts and could not
support commenting or linking.

##### 2005–2014: Networks Rediscover Threads

Through this entire period of 1970–2004, flat was the dominant system design.
While there were occasional thread-based platforms such as PLATO Notes (1974),
Bolt.com (1996), and LiveJournal (1996), only 22% of the systems coded in
1970-2004 were thread-based. But then, starting in 2005, and roughly
contemporaneously with the launch of the first Facebook News Feed, social
media platforms began introducing threading. Quickly, nearly all new platforms
switched from flat to threads: 80% of systems 2005-2023 are threaded. This
inversion from 20% thread / 80% flat to 80% thread / 20% flat happened quite
suddenly in 2005, starting with Reddit (2005), YouTube (2005), Twitter (2006),
and Tumblr (2007), and continuing with platforms such as Pinterest (2010),
Instagram (2010), Snapchat (2011), and Musical.ly (the predecessor of
TikTok—2014). Flat-based outliers continued to exist, but they were the
minority.

##### 2015–current: Algorithmic Commons

While the systems up to this point required users to explicitly specify what
they wanted to receive, progress in machine learning eventually reached the
point where it became feasible for systems to learn user preferences
implicitly and select content for them from the commons. There are only four
platforms in our dataset that delivered from the commons at launch. Two of
them occurred before 2015, which we are marking the start of the commons era:
Pinterest in 2010, and Tinder in 2012. However, it is in 2017 that TikTok
brought to the fore the idea of a platform sourcing content from the commons,
and algorithms became a major focus in media and research—so we place the
start of the era around here, at 2015. However, six years after the launch of
TikTok, only Meta’s new platform Threads in the dataset we would argue is
primarily algorithmic—perhaps users still want to maintain some degree of
explicit control over what they see. Other platforms since 2015 remain purely
or predominantly space and network based, including Discord (2015), Mastodon
(2016), BeReal (2020), Clubhouse (2020), Gab (2016), Parler (2018), and Truth
Social (2021).

Instead, many platforms remained in their existing cell, but fused elements of
the commons into their existing design. For example, Twitter’s For You feed is
now 50% content from followed accounts and 50% content recommended from the
commons.141414https://blog.twitter.com/engineering/en_us/topics/open-
source/2023/twitter-recommendation-algorithm Likewise, Instagram increased the
proportion of posts from the broader platform in its feed, then reduced it
again after
backlash.151515https://www.theverge.com/2022/7/28/23282682/instagram-rollback-
tiktok-feed-recommendations-interview-adam-mosseri So, despite multiple
proclamations of the death of network-based
designs,161616https://www.newyorker.com/culture/cultural-comment/tiktok-and-
the-fall-of-the-social-media-giants,
https://www.nytimes.com/2023/04/19/technology/personaltech/tiktok-twitter-
facebook-social.html, https://www.axios.com/2022/07/25/sunset-social-network-
facebook-tiktok few platforms have fully converted to a commons-based model
even years after the rise of TikTok.

###  5.2. Reflecting on Social Media Designs using Form-From

####  5.2.1. Does Scaling Drive Movement in the Design Space?

For a small group engaged in just a single conversation, a flat space design
is certainly the simplest. The other cells of our matrix can all be seen as
additional complexity introduced to cope with scale. As the number of
conversations grows too large, spaces allow a participant to pay attention
only to conversations within sub-spaces of interest (e.g., channels). Networks
let participants focus attention on people they care to hear from. Threads
partition overlapping conversations so that a participant can attend to one at
a time in its entirety instead of being forced to keep all of them in mind at
once. Even if we get to the point of algorithms doing a perfect job of
selecting the content we should see, selecting specific spaces, following
people, and attending to specific threads still provide valuable organizing
infrastructure to help people think about one thing at a time.

While these changes alleviate the pressures of scale, they also create
unfamiliar social configurations that result in unexpected behavior and
eventually new social norms and designs. For instance, Marwick and boyd
(Marwick and boyd, [n. d.]) were the first to point to context collapse in
social media: the situation where a post aimed at one of a user’s multiple
audiences was delivered to all of them. Context collapse tracks the move of
social platforms from spaces to networks and the commons. Spaces align well to
contexts, as a user aiming to reach a particular audience can post to a space,
where the audience is a defined set. The move to networks and commons designs
eliminates that easy path for posters to target their posts to an appropriate
audience.

As content volume continues to scale up, we can compose multiple “layers” of
delivery designs—which reduce content volume—in order to cope. Slack’s
channels within workspaces are a spaces-in-spaces design. Another alternative
to handling a space—such as a subreddit—that got too big would be to use a
network design within it, delivering to a user only the content in that space
that was posted by someone they follow. This combination of space and network
designs would protect participants from context collapse, as they would only
see content from a given friend _on a specifically chosen topic_. Instagram’s
Close Friends list could be seen as a network-in-network design to further
filter down from a large following list. As another example, we see Facebook
applying algorithms—which naturally apply to the commons model—to filter
comments on a single post down to only the “most relevant,” as opposed to
showing all replies.

####  5.2.2. Headed Content as Spaces

We’ve discussed the “headed” subspecies of the threaded form, in which comment
threads hang off some initial privileged type of content. This is quite
reminiscent of the way content is delivered from spaces, if we simply imagine
that each head defines a space whose participants are the people interested in
discussing that particular piece of content. This creates some design
opportunities which have not yet been extensively explored: some YouTube
videos have as many as 500,000 comments, far too many for any individual to
consume. Considering that video as a space, one could imagine applying
alternative From models to the content of the space: perhaps a network model,
in which users only see comments from people they follow or a partition of the
comments according to multiple defined subspaces. Or, treating the space as a
commons and using algorithmic techniques to select comments to show (as
mentioned above, Facebook offers this method as “most relevant” comments). In
addition, many of the moderation features that are common in space-based
designs, such as approving members, appointing moderators, and removing or
promoting comments, could be introduced. For instance, we have seen some
networked platforms introduce the ability to moderate replies to one’s “head”
post, such as Twitter’s Hide Reply feature in
2019.171717https://techcrunch.com/2019/11/21/twitter-rolls-out-its-hide-
replies-feature-to-all-users-worldwide/amp/

####  5.2.3. Abandoning and Reclaiming User Control

Earlier systems used spaces and then network designs; it is only recently that
the commons design has emerged. One possible reason for this timing is that
the commons demands highly sophisticated algorithms that have only recently
been developed. On a large social platform, there has to be some way to
deliver a tractably small subset of messages or posts to an interested
consumer. Historically, limiting delivery to a small, user-selected collection
of spaces or people of interest could do that. Today’s sophisticated machine
learning algorithms instead offer the possibility of learning a user’s
preferences and selecting a small amount of content suited to that user from
the pool of posts from the commons. This approach could not succeed until the
algorithms developed sufficient precision to ensure that most of what they
delivered was interesting to a user. It also works best in situations such as
entertainment where no one piece of content is essential to deliver to a user,
but any sufficiently interesting content will suffice. Using algorithms
instead of spaces or networks means that users cede control over what they
will receive to those algorithms. However, as we’ve seen with the backlash to
Instagram’s change to their feed algorithm and with the recent interest in
decentralized Twitter-like systems like Mastodon and Bluesky, users are also
interested in control. In the future, we may see new designs that aim to
innovate in both providing users some control, yet leverages the power of
algorithms to reduce user labor in articulating their preferences (Feng et
al., 2024). One example is Bluesky’s recent introduction of custom feeds,
where users can create their own algorithmic feeds and choose from a suite of
feeds published by
others.181818https://www.theverge.com/2023/5/26/23739174/bluesky-custom-feeds-
algorithms-twitter-alternative

###  5.3. Identifying Design Patterns within Form-From Cells

As first articulated by Alexander (1977) and then translated into HCI by Van
Duyne et al. (2007), a design pattern is a design that is commonly used to
solve a given problem. When we find a cluster of well-known systems in the
design space with similar values for many of the dimensions, we can consider
it to be a design pattern.

Design patterns can arise when certain dimension values work better with or
directly imply other dimension values. For example in the case of group chat
systems with multiple channels (e.g., Slack, Discord, Microsoft Teams), real-
time awareness in the form of signaling of real-time user activity and status
is useful for enabling synchronous conversation, similar to other chat and IM
systems. A number of other designs come bundled with the high level choice of
supporting synchronous messaging, such as emphasizing short text messages and
chronological sorting. However, unlike group chat systems for smaller groups
such as GroupMe or Snapchat, systems like Slack and Discord can scale to
larger groups through enabling many parallel conversations via channels.
Hence, it is not surprising to see WhatsApp recently introducing a Communities
surface enabling over 20,000 members with the feature of multiple groups, as a
contrast to their Groups surface with no subgroups and a limit of 1,024
members.191919https://blog.whatsapp.com/communities-now-available

Other common design patterns that we observe within specific cells of the
Form-From model:

  * •

Threaded Spaces: upvote/downvote ranking feedback (e.g., Reddit, Quora);
overview pages allowing users to skim top-level posts or recently updated
threads (e.g., phpBB, 4chan);

  * •

Flat Network: to post content to a person on the network, visit the person’s
page rather than a centralized posting interface (e.g., MySpace, early
Facebook Wall, early LinkedIn);

  * •

Threaded Network: common social network design patterns such as
likes/favorites, shares, comments; vertical scrolling feed with intermixed
content (e.g., today’s Facebook NewsFeed, Twitter);

  * •

Threaded Commons: content from the entire platform is algorithmically
recommended based on user feedback, and users are encouraged to comment on the
content (e.g., TikTok).

Many of these design patterns arise commonly within a given cell but would be
nonsensical in other cells, supporting the internal consistency of our model.

##  6\. Discussion

###  6.1. Implications for Designers and Researchers

While our main effort was to develop theory, we aim for our models to provide
levers for practitioners and designers as well. One strategy for applying our
model is to find design inspiration and explore alternatives. Designers might
start with their proposed new design, then identify its attributes on our
model’s dimensions. Having thus determined the location “coordinates” of their
system in our design space, the designer can then use our model to identify
other systems that are located in the same or nearby cells in the model. By
doing so, they can begin to observe related design patterns, understand how
those patterns played out in actual usage, and pick and choose elements of
those designs that they want to consider for their own design.

Our larger models can also serve in a practical capacity as checklists. Every
new system must implement a value for essentially every dimension in our
model. If we don’t design with intent, we default. In other words, it is far
better that designers walk through the model’s dimensions and think
critically, up front, about their design decisions, than to make them
implicitly or inherit them unthinkingly from whatever technical framework they
are using.

As researchers, we investigate social media design, yet there is still much to
learn. Our taxonomy may be helpful for mapping out productive lines of
research. For example, what influence do individual dimensions, or
intersections of these dimensions, have on the sociotechnical outcomes of
platforms and communities? We view these models as launchpads for research
agendas to better underpin our understanding of social computing design.

We also hope that this effort helps sharpen our language and thinking around
social media systems. There has been an inherent fuzziness to terms such as
“algorithmic feed”, because these feeds can combine elements of several
different dimensions. However, by staking out whether we are talking about an
algorithm being run over a commons, or over a network, or (as in Twitter) a
50–50 combination of both, we can have more focused discussions. Going
forward, these dimensions can also give rise, we hope, to more robust design
pattern languages as well.

###  6.2. Current Limitations and Future Work

Like all researchers, we would like to believe that our work is “future
proof”—but we doubt it is. The goal for a scientific model is an understanding
that is true regardless of historical circumstance. An engineering model,
particularly a Simonian model, must change as new devices or systems are
constructed, for example because of new capabilities in the environment (e.g.,
recently, the low cost of distributed cloud storage or ubiquitous networking).
Thus future work is likely. We foresee five lines of future work we believe
will be profitable.

Adding detail to dimensions. We believe over time, new systems and system
capabilities will require additional nuance and detail for some dimensions.
One example of this is with algorithms. One of our challenge was how to
conceptualize algorithmic ranking as part of our design space, as algorithms
have become more and more salient parts of the social media experience. An
earlier version of the model replaced “commons” with “algorithm”. However,
essentially every modern threaded system, from threaded spaces such as Reddit,
to threaded networks such as Twitter, to threaded commons such as TikTok, uses
algorithmic ranking. Ultimately, we decided to focus the From axis on the
potential sources of content, and view algorithmic ranking as orthogonal.
While not including “algorithm” in the top-level model downplays the
visibility of algorithmic ranking, we argue that the From axis better explains
existing variation in platform design.

However, we expect to see the trend of algorithmic commons continue until the
use of algorithms to make content visible or detectable to users will be a
component of many more platforms. At that point, it may become important to
distinguish between different kinds of algorithmic delivery, much as we’ve
needed new terms beyond asynchronous and synchronous to understand how
platforms use time today. In our full 62-dimension model, under the Delivery
and Audience categories, we start to separate out how algorithms are
used—whether for determining audiences versus ranking of content versus
ranking of replies. But in the future, additional consideration may be needed
to distinguish different kinds of algorithms, or the different kinds of
signals they incorporate.

New dimensions. We do not claim that our model captures all variations in
platform design. A Simonian design space aspires to do so, and so our full
62-dimension model is a useful effort. Nonetheless, the long tail of designs
argues that our 62-dimension model will be incomplete for some analytical
goals, and that some of the categorical options for its dimensions can be
refined. We welcome this improvement.

Understanding the relationships between dimensions. Our design space was
defined as a hierarchy (i.e., a tree). Our models all have a tree structure,
which is useful for taxonomic purposes and allowed the problem to be
tractable. Trees, however, cannot easily represent necessary or preferred
relationships between dimensions. Now that we have delineated the models,
future work may consider relationships between attributes (dimensions) and
even values, thus forming a network structure. This will be important for
considering subspaces of the design space for design patterns. It will also be
important for fully examining dynamics in social media systems, such how the
social networks form (Jacobs et al., 2015).

Design patterns and operators. Card, Mackinlay, and Robinson argued for the
inclusion of operators in a design space analysis (Card et al., 1990). For
them, operators provided for the composition of applications of the design
space. Our work has examined the analytical dimensions upon which applications
are constructed; future work should consider how they are used together in
compositions. This would allow one to broaden from examining one product
surface at a time to consider how platforms combine multiple points in the
design space together into one experience. The combination of multiple
dimensions with specific values, along with the composition operators, would
lead to design patterns for social media systems, as we touched upon in
Section 5.3.

As mentioned in the previous subsection, future work could also consider
complementing our inductive effort with quantitative studies. For example, are
there correlative or even causal relationships between design decisions in one
part of the design space and design decisions in others? For example, is
blocking plausible for flat-space systems? Comparative analysis across
platforms or via natural experiments might likewise answer, what are the
impacts of each decision on user behavior or emergent platform norms? For
example, do differences in governance lead to differences in the impacts of
disputes on community social capital? Empirically-based hypothesis-testing
studies could lead to a stronger understanding of how these dimensions could
best be combined.

Sociotechnical dimensions. In order to make the modeling problem tractable, we
simplified it by examining only technical characteristics of what are
necessarily socio-technical systems. A future line of work could try to also
include dimensions of social behavior, i.e., not just how systems are designed
but how they are appropriated by users in different ways for certain social
aims. We believe the required effort for this will be significant, but it is
likely to be useful for creating design patterns.

We believe the current state of our models will be helpful to both
practitioners and researchers. As mentioned, the current models are capable of
guiding system designs as well as research efforts. The future work we have
just described here enhance the utility of the models as well as maintain the
models over time.

##  7\. Conclusion

When it comes to social media design, which differences make a difference?
CSCW theory has placed nearly all social media into a “different time-
different place” cell of its traditional time-space matrix (Johansen, 1988;
Grudin, 1994), and yet clearly social media express many meaningful
similarities and differences. We introduce Form-From in this paper, offering a
synthesized model derived from a larger 62-dimensional design space. Form-From
offers a set of distinguishing features shared by designs in the same cell. We
apply this model to demonstrate its analytical benefit, and invite the
community to continue to extend and propose design space models of social
computing systems going forward.

###### Acknowledgements.

We would like to acknowledge Andrea Holber, Ricardo Jonathan, Simran Malhi,
and Sumedha Kanumuri for their assistance with annotating platforms according
to our 62-dimension model and helping to revise our codebook and definitions.
We would also like to thank Steve Harrison, Jonathan Grudin, and Joseph
Seering for their feedback and guidance on aspects of the literature review,
model, and paper.

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##  Appendix A Full 62 Dimension Model

We list the entire 62-dimension model below grouped by the 10 high level
categories.

###  A.1. Category: Discussion Structuring

This category describes how discussion data (the utterances that make up the
discussion) is structured as whole into a data model, i.e., contained within
data objects and the named relationships between data objects. In our
distillation into the 2x3 Form-From model, this category is summarized into
the dimension of “Form”.

####  A.1.1. Grouping of Utterances

This dimension describes how the utterances that make up a single discussion
are grouped into data objects or containers within a system. Utterances can be
grouped into multiple nested containers.

Options (select all that apply):

  * •

Each user contribution forms a post, e.g., an email, a forum post, a comment,
a message.

  * •

Stages/phases of a discussion, e.g., a system designed with explicit rounds of
deliberation.

  * •

Grouping of posts into threads of conversation. Threads are groups of posts
that form “turns” in a conversation, where one replies to the other; this
doesn’t include quoting or linking to other posts.

  * •

Splitting long posts by a user into smaller connected chunks. Users on any
platform with threads can do this by replying to themselves but on some
platforms like Twitter, a thread of posts by the same author are incorporated
into the system design and user experience.

  * •

Blocks of contiguous discussion time periods, such as meetings on Zoom.
Usually for video/audio conversations that become a single data object storing
the entire discussion time period.

####  A.1.2. Multi-channeling

Is all of the discussion focused into a single mode of interaction? Or are
there parallel modes of interaction for the same discussion?

Options (Exclusive):

  * •

Single channel: only one channel for interaction, e.g., Messages (iOS) only
has text chats.

  * •

Multiple channels: multiple parallel channels for the same discussion, e.g.,
Zoom meetings have a simultaneous video and chat channel, and Twitch and
Instagram Live have a simultaneous video and chat channel.

####  A.1.3. Threading/Partition of Replies

Many platforms offer a “reply” affordance and capture a relationship between a
post and the post to which it replies. If you record the exact post to which
each post replies, the result is an infinitely deep tree. But some platforms
ignore the reply structure entirely, yielding a flat structure (often ordered
chronologically). In an intermediate form, some systems capture reply links to
a certain depth, but stop differentiating below that depth, yielding a fixed-
depth tree. For example, on Facebook today, a post has a first level of
replies, but all posts descended from a particular reply are listed
chronologically beneath that reply.

Options (exclusive):

  * •

Fixed-depth reply tree, e.g., Slack (1 level of replies), Facebook (2 levels
of replies).

  * •

Infinitely deep reply tree, e.g., email, Reddit, Twitter.

  * •

No replies, i.e., flat structure.

####  A.1.4. Link Types

Other than the common “reply” link for creating threads, which is covered in
the above dimension, some platforms also provide structured mechanisms for
other forms of linking, so that previous posts can be referenced as part of
the current discussion.

Options (select all that apply):

  * •

Citation: ability to cite another post within the discussion platform, e.g.,
GitHub issues enable cross linking other posts, 4chan enable references to
another post number, StackOverflow questions enable linking to another
question.

  * •

Embedded linking: ability for a post author to create their content in the
context of another piece of content. This is commonly known as “quote-
posting”, such as Twitter quote-tweeting, Instagram embedded stories, and
Tiktok duets and stitching.

  * •

Pingbacks: displaying on a post a record of what other posts have cited that
post; for instance, Blogger blogposts, Quora links when another question
references a question, and Twitter links to all quote-tweets of a tweet.

  * •

Argumentation links: a specific type of citation link that specifies the type
of argument relationship between the two posts, i.e., “refutes” or “supports”.
This is often used in argumentation mapping systems such as the MIT
Deliberatorium (Klein, 2012).

  * •

None.

####  A.1.5. Post Types

Are some posts distinguished from typical/standard posts?

Options (select all that apply):

  * •

Head post distinguished from replies: besides just being the first post in a
thread, a system may incorporate additional designs to distinguish the first
post, such as differences in appearance, format, post type, and
discoverability compared to reply posts. Many of the below options go hand-in-
hand with this option.

  * •

Q&A: some posts are questions, some are answers, e.g., Quora, StackOverflow.

  * •

Argumentation System: posts have argumentation-related types, such as
“argument” or “evidence”.

  * •

Pro-Con System: The initial post is the post to be debated, and subsequent
posts are either of type “pro” or “con”, e.g., on
Kialo202020https://kialo.com/ and ConsiderIt (Kriplean et al., 2012).

  * •

Summarizations of a post/thread: a special post type that is a summary of one
or more other posts, e.g., Wikum (Zhang et al., 2017).

  * •

Annotation: a specific post type that serves as an annotation to another post.
One example is Twitter’s Community Notes
feature,212121https://twitter.com/i/communitynotes which annotates
misinformation posts with corrections created by community members.

  * •

None, i.e., only undiferentiated, generic posts or no concept of posts.

####  A.1.6. Anchoring of Posts

What are posts anchored to? In other words, what are they posting on, if
anything?

Options (select all that apply):

  * •

Anchoring post to text: posts are made on spans of text content. e.g., Google
Docs, Medium, Nota Bene (Zyto et al., 2012), and Hypothes.is have comments
anchored to specific spans of document text or at a specific PDF location.

  * •

Anchoring posts to a particular point in time, e.g., SoundCloud has comments
on song timestamps, YouTube has video comments anchored to a timestamp, most
livestream apps like Twitch have chat messages anchored to specific
timestamps.

  * •

Anchoring comment to space, e.g., virtual reality apps or physical spatial
apps such as Foursquare anchor discussion to a specific virtual/physical
space.

  * •

None.

####  A.1.7. Enforced attachment of discussion to content item

To what, if anything, are the discussion spaces as a whole attached? Here, we
focus on enforced attachment, i.e., that conversation must be under some
primary piece of content and cannot exist alone. We exclude the more generic
case of discussions attached to a starting post that has a similar media
format as discussion posts. This dimension also differs from the prior
dimension in that attachment is to the content item as a whole.

Options (exclusive):

  * •

Attached to an article or document, e.g., the comments sections under many
news articles.

  * •

Attached to a video, e.g., YouTube comments or TikTok comments.

  * •

Attached to an image, e.g., Instagram comments.

  * •

None.

###  A.2. Delivery

How are discussion items in the system delivered from user to user in the
system and then presented? In our distillation into the 2x3 Form-From model,
this category is summarized into the dimension of “From”.

####  A.2.1. Main Location of Discussion

Where does discussion within the system reside and how does it arrive there?

Options (Select all that apply):

  * •

Center: All discussion gets delivered to a single central globally addressable
place, e.g., Twitter.

  * •

Rooms: Multiple distinct globally addressable places, e.g., Slack channels,
forum boards.

  * •

Cliques: Multiple distinct places characterized only by membership and without
a global address, e.g., direct messages, multi-party direct messages in Slack.

  * •

Mailboxes: Discussion gets delivered to personal inboxes without there being a
shared central location to access all content, e.g., email, SMS.

####  A.2.2. Ranking of Posts

Given the set of posts that can be received by a user, how are those top level
“posts” ranked, ordered, or selected?

Options (Select all that apply):

  * •

Chronological, e.g., Slack, WhatsApp.

  * •

Personalized algorithmic according to engagement, e.g., Facebook, Twitter.

  * •

Algorithmic according to collective voting, e.g., Reddit.

  * •

Editorial decision-making, e.g., NYTimes Editors’ Picks.

  * •

Unclear, i.e., it is unknown or opaque in some way.

  * •

Other.

  * •

None.

####  A.2.3. Ranking of Replies to a Post

When a single “post” is followed by a set of comments associated with that
item, how are the comments ordered?

Options (select all that apply):

  * •

Chronological, e.g., Slack replies.

  * •

Algorithmic according to collective voting, e.g., Reddit.

  * •

Unclear, i.e., it is unknown or opaque in some way.

  * •

By thread structure, e.g., Slashdot.

  * •

Other.

  * •

None, i.e., the system does not have comments underneath posts, instead
everything is at one level, or does not have posts.

####  A.2.4. Subscription

How can a user select which particular subset of posts they want to see from
the body of content they are able to see? Selection could be a one-off event
or something more like a subscription, where a user always wants to see
content from that subset.

Options (select all that apply):

  * •

Posts from certain people, e.g., following people on Twitter, Facebook
friends.

  * •

Specific topics/tags, e.g., following hashtags on Twitter.

  * •

All posts made to a group or channel, i.e., subscribe to a Facebook group,
Discord channel.

  * •

Specific threads of conversation, i.e., subscribing to receive any new posts
made to that thread.

  * •

Other.

  * •

None, i.e., subscription is not possible.

####  A.2.5. Muting

How can a user specify what or who they do not want to see from the body of
content they are able to see? In other work, this has been characterized as
personal moderation (Jhaver et al., 2023b).

Options (select all that apply):

  * •

Posts from certain people, e.g., muting an account on Twitter even if
following them. Note that blocking is covered under the Audience category, as
it is concerned with both not seeing someone’s content and also not allowing
them to see your content.

  * •

Specific topics/tags/phrases, e.g., muting specific phrases on Twitter.

  * •

Replies to a post, e.g., the ability to mute a conversation that one was in on
Twitter.

  * •

Score or degree related to some content metadata or characteristic. This
involves the ability to set a threshold on a score of some kind, where posts
below the threshold are hidden. Examples include post scores on an older
version of Slashdot (Lampe and Resnick, 2004) or personal moderation settings
for sensitive content on Instagram (Jhaver et al., 2023b).

  * •

Other.

  * •

None, i.e., users are not able to choose what they do not want to see.

####  A.2.6. Findability

How can users find content they want to see if they have a particular kind of
content in mind or if they want to go back to some specific content they’ve
seen?

Options (select all that apply):

  * •

Text search, e.g., email clients, etc. If the platform is indexable, sometimes
this search happens outside the platform, such as the common strategy of using
Google Search to find Reddit threads.

  * •

User search, e.g., looking up a particular user in mind and then navigating to
their page or their list of content.

  * •

Tags attached to content or hashtags embedded within textual content.

  * •

Navigating to specific boards, threads, and/or pages, e.g., 4chan, phpBB,
Wordpress. Paginated forums typically have fixed size pages.

  * •

Scrolling back through one’s feed, including with infinite scroll, e.g.,
Facebook Newsfeed.

  * •

Star/bookmarking of content for one’s self, or personal saving of URLs to
specific content.

  * •

Pinning, bookmarking, or saving of specific content for others, e.g., on Slack
anyone can pin, while on Reddit mods can pin, and on Instagram, post authors
can pin top replies.

  * •

None, i.e., there is no way to find particular content or go back to content.
This can happen for instance in the case of systems with disappearing
messages.

####  A.2.7. Notifications of Content

What controls the types of notifications that are received by the user in
terms of notifications that point them to specific content? For this
dimension, it’s most helpful to only select the one that is most prevalent as
we’ve found that many platforms have a mix of both.

Options (exclusive):

  * •

User choice dominant.

  * •

Platform choice dominant, i.e., the platform pushes content into one’s
notifications that the user did not ask for.

  * •

No notifications.

###  A.3. Temporality

This category deals with all dimensions that relate to the timing and
sychronicity of conversations.

####  A.3.1. Requires Synchronous Interaction

Does the discussion require that all participants be present live and interact
synchronously? Many systems that are asynchronous can be used synchronously
(e.g., instant messaging, Slack); here we are not interested in those, only
ones that require the synchronous interaction.

Options (exclusive):

  * •

Requires synchronous, e.g., Zoom, Skype, FaceTime, OhYay.

  * •

Does not require.

####  A.3.2. Discussion Endpoint

Are there formal endings to a discussion? Or can it go on indefinitely?

Options (exclusive):

  * •

Automatic closure by time, e.g., We Are Dynamo (Salehi et al., 2015),
Precision Conference reviewer discussion period, timed Zoom meetings.

  * •

Automatic closure by activity level, e.g., 4chan threads close after
inactivity.

  * •

Manually close the discussion, e.g., GitHub issues can be manually closed,
Google Docs comment threads can be manually resolved, Zoom calls can be ended
by the owner.

  * •

No ability to end a discussion, e.g., email (Grandhi and Lanagan-Leitzel,
2016), Twitter.

####  A.3.3. Archiving

What happens to the discussions afterwards? Are they saved and archived, or do
they disappear? Some platforms offer both options, in which case: which is the
default?

Options (exclusive):

  * •

Enforced ephemeral: content by default is always deleted (from the view of
users). This includes 4chan, Snapchat, and Instagram Stories.

  * •

Enforced archived: content by default is always saved. Note: This combines
platforms where you can delete comments manually and ones where you explicitly
cannot, i.e., it is always recoverable. Examples include Wikipedia talk pages
(cannot delete), Google Docs comments (cannot delete), Instagram comments (can
delete).

  * •

Default archived: saved by default, but can be switched to ephemeral, e.g.,
Mailman lists.

  * •

Default ephemeral: ephemeral by default, but can be switched to archived,
e.g., Zoom (can record the call), Slack (switching from the unpaid version to
the paid version), Twitch (streams automatically get deleted but certain
streamers can keep them around longer).

####  A.3.4. Mutability

Can posts be edited, moved, or deleted by the creator of the post after
creation?

Options (select all that apply):

  * •

Editable after posting, e.g., Slack and Facebook.

  * •

Moveable/retargetable after posting.

  * •

Deletable after posting.

  * •

None.

###  A.4. Audience

This category is concerned with the flip-side of the category regarding
Delivery, which is the audience of one’s posts or discussions. While the
“From” dimension in the Form-From model comes most directly from the Delivery
category and this category is more associated with an idea of “To”, decisions
regarding what is possible to do as a sender oftentimes mirror or constrain
what is possible as a receiver, though they are not always the same. Thus,
some of the concepts described within “From” have some overlap with dimensions
in this category as well.

####  A.4.1. Discussant Matching

What is the process by which users get matched up with other users to have a
discussion? In many platforms, this is via users, where users have control
over who they want to talk to and can specify specific discussants or explore
discussants in a shared space. The other option is some sort of algorithmic
process for matching users or recommending users to each other. Many platforms
have elements of both.

Options (select all that apply):

  * •

User-led exploration or selection.

  * •

Algorithmic matching of users or recommendation of users.

####  A.4.2. Maximal Audience

The upper bound on how many people can see a canonical post. That is, if every
person spent infinite time looking at all the content they could see in the
platform, how would we describe the set of content they can see?

Options (exclusive):

  * •

Bounded Group

  * •

Networked Neighborhood

  * •

Public within the Platform

####  A.4.3. Delivery Guarantee/Typical Audience

While a maximal audience could tell us theoretically how many people could see
a comment if they wanted to, in practice, the audience of a comment is
smaller. Who actually is made aware of the comment or guaranteed delivery?

Options (exclusive):

  * •

All members of group or recipient list, i.e., guaranteed delivery to all