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Multi-Platform Content Creation: The Configuration of Creator
Ecology through Platform Prioritization, Content
Synchronization, and Audience Management
Renkai Ma∗
The Pennsylvania State University
renkai@psu.edu
Xinning Gui
The Pennsylvania State University
xinninggui@psu.edu
Yubo Kou
The Pennsylvania State University
yubokou@psu.edu
ABSTRACT
Online platforms like YouTube and Instagram have enabled the
platformization and monetization of creative work, allowing con-
tent creators to derive revenue and thrive in a creator economy.
While much work has been done to understand content creation
on single platforms, the creative practice often involves content
creators’ agency and practice to interact with multiple platforms
and make strategic decisions to optimize such interactions. In this
paper, we use an interview study with 21 cross-platform creators
to understand how they negotiate with platforms in their creative
practices through the construction of creator ecology. We found
that participants developed priorities among platforms based on
varied criteria, paid attention to cross-platform content synchro-
nization, and stressed managing and converting audiences across
platforms to grow their fanbase. Our ndings highlight the com-
plex interplay between creator agency and labor, as well as yield
implications for future design possibilities of creator empowerment
and support.
CCS CONCEPTS
•Human-centered computing;•Collaborative and social com-
puting;•Empirical studies in collaborative and social com-
puting;
KEYWORDS
Multi-platform content creation, creative labor, creator ecology,
platformization
ACM Reference Format:
Renkai Ma, Xinning Gui, and Yubo Kou. 2023. Multi-Platform Content Cre-
ation: The Conguration of Creator Ecology through Platform Prioritization,
Content Synchronization, and Audience Management. In Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems (CHI ’23),
April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 19 pages.
https://doi.org/10.1145/3544548.3581106
∗Corresponding author.
This is preprint.
CHI ’23, April 23–28, 2023, Hamburg, Germany
https://doi.org/10.1145/3544548.3581106
1 INTRODUCTION
Online platforms such as YouTube, Instagram, and TikTok have
given rise to the “creator economy” [
75
,
99
,
100
], where content
creators could rely on platforms to reach a large audience and de-
rive revenue from content creation. More than 50 million people
across the globe consider themselves as creators, within which more
than two million have become professional creators and make con-
tent creation their full-time job [
55
]. With end-user advocacy and
empowerment as key disciplinary values, human-computer inter-
action (HCI) researchers have long investigated creative practices,
such as digital fabrication and hacking [
5
,
109
] and co-creation of
songs [
102
], as well as technology design for creativity support
[
30
,
35
,
52
,
127
]. It is in recent years that a socioeconomic lens has
become adopted, at the convergence of digital labor scholarship
[
48
] and platform studies [
34
] to explore such issues as digital pa-
tronage [
124
], (de)monetization [
78
], and platform labor [
77
]. The
platformization of creative labor, in this context, has pushed the
conversation further to consider platforms’ dominance over struc-
turing and controlling how content is produced, distributed, and
monetized [
39
,
40
]. In this paper, creative labor refers to what Craig
and Cunningham consider as “commercializing and professionaliz-
ing native social media users who generate and circulate original
content to incubate, promote, and monetize their own media brand,”
online and oine [27].
Indeed, recent HCI research has revealed content creators’ com-
plex interactions and negotiations with online platforms, often-
times invisible to their audiences. For example, content creators
on YouTube need to make sense of moderation decisions such as
demonetization and develop practical knowledge about how the
platform’s moderation algorithms work [
78
]; adult content creators
on OnlyFans have to navigate the platform’s limitations and poli-
cies through community building with their fellow creators [
117
],
and food inuencers on Instagram must wrestle with the stigma of
being an inuencer while utilizing multiple platform functionali-
ties such as targeted hashtags and Instagram Insights [
120
]. While
content creation research is growing in HCI, most has focused on
content creation practices on single, particular platforms such as
Twitch [
80
,
119
], YouTube [
78
,
79
,
126
], and Instagram [
120
], identi-
fying platform-specic creative practices as well as unique creator-
platform interaction patterns. However, practically speaking, it is
common that content creators curate their content and audiences
on multiple platforms simultaneously. For instance, Thomas et al.’s
survey [
110
] found that 96% of their respondents used two or more
platforms, 82% used four or more platforms, and 21% even used
more than six platforms. However, limited attention has been paid
to such situation where numerous content creators must rely on
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
more than one platform for content distribution and revenue gen-
eration [55, 97].
That independent content creators interact with multiple plat-
forms, in essence, resonates with the notion of platform ecology,
which is a user-centric view proposed by Ibert et al. that “puts user
practices and agency centre stage, accentuates the application of
dierent platforms as an integral part of everyday life, and high-
lights the complexities of on/oine practices” [
65
]. While Ibert et
al. discuss platform ecology with a focus on general users exerting
agency to congure multiple platforms, our study builds on this
notion to explore creator ecology by considering content creators
as a unique class of platform users. We are interested in understand-
ing how creators congure their creator ecology in their creative
practices across multiple platforms.
To answer this question, we conducted an interview study with
21 multi-platform content creators who create content on at least
two online platforms. Through inductive qualitative analysis tech-
niques [
26
], we identied three primary practices that our par-
ticipants developed to congure their creator ecology: They (1)
dynamically prioritized their creative labor for selective platforms
over others, (2) creatively synchronized and tailored their content
across platforms, and (3) strategically managed audiences by main-
taining and transferring them across platforms as well as converting
them to dedicated fans. Given these ndings, we discuss and elabo-
rate on the notion of creator ecology, through which participants
dynamically assessed and managed platforms’ aordances and con-
straints, as well as audiences, in order to empower themselves. We
further discuss the labor for maintaining creator ecology and pro-
vide implications for designing better creativity support tools for
multi-platform creators.
We make several contributions to the HCI literature: First, we
connect with and contribute to HCI research on content creation by
empirically documenting content creators’ practices of managing
multiple platforms and conceptually connecting creative practices
and platform ecology. Second, we articulate clear distinctions that
multi-platformness brings to our understanding of creative prac-
tices. Third, our creator-centric account of how content creators
interact with multiple platforms yields meaningful design implica-
tions for creator advocacy and support.
2 RELATED WORK
In this section, we rst review previous HCI literature on content
creation, where our work is grounded and seeks to contribute to.
Then, we discuss how relevant prior work from dierent elds has
conceptualized content creation as digital labor, and nally discuss
in detail the theoretical perspective of platform ecology and its
relevance to our work.
2.1 Content Creation as Digital Labor
HCI researchers have been interested in creative practices in gen-
eral and, more specically, content creation enabled by the Internet
for many years. This tradition, manifest in terms such as peer pro-
duction [
9
], participatory culture [
67
], and user-generation content
[
87
], tends to value the liberating potential of social media and
Web 2.0 in empowering end-users and amplifying their creative
agency. For example, Wikipedia provides the epitome of content
creation as collaborative work, where researchers have explored
topics ranging from Wikipedia editors’ motivations and practices
[
2
,
19
,
61
,
70
,
121
], to their community and social dynamics [
20
,
45
],
to their systems of governance and moderation [
11
,
49
,
50
,
54
].
Besides the view of content creation as collaborative work, HCI
researchers have also explored other aspects of content creation,
such as copyright issues that creators must navigate through in
interactions with platforms [
47
], creators’ identity performance in
front of audiences [
51
], creators’ interactions with content mod-
eration decisions [
68
], and underrepresented groups of content
creators, such as older adult bloggers [
18
], content creators with
disabilities [24, 43] and youth content creators [82].
In addition to the perspectives that celebrate online content
creation as a more open, liberating, and egalitarian cultural indus-
try than traditional ones such as lm and television, recent work
(e.g., [
1
,
16
,
56
]) in HCI, media, communication, and related elds
has started emphasizing that it is an advertising-driven industry,
examining content creation as digital, creative labor and content
creators as neoliberal workers, and paying attention to the precarity
or uncertainty associated with content creators’ labor and work-
ing conditions. Content creators are considered as workers in the
creative economy who frequently engage in online self-branding
[
41
,
101
], which is “a form of aective, immaterial labor that is pur-
posefully undertaken by individuals in order to garner attention,
reputation and potentially prot” [
64
]. For instance, content cre-
ators need to foster and sustain interaction with their followers as a
form of “relational labor” [
6
] to secure nancial support [
16
], such
as responding to the messages and comments from their followers
[4].
The connection between content creators’ digital labor and the
condition of precarity is profound. For example, algorithmic moder-
ation of platforms shapes content creators’ labor conditions through
algorithmic opacity and precarity [
78
]. Content creators need to
tackle such challenges by analyzing, sharing, and applying knowl-
edge about the moderation algorithms, which is considered as “algo-
rithmic labor” [
78
]. In addition, for content creators, their content
being rendered visible or invisible is directly related to their ma-
terial gains [
71
]. Thus, their creative labor is structured “by both
the promise and precarity of visibility” [
39
], not to mention such
visibility might be unfairly deducted [
79
]. Duy et al. found that
the volatile nature of visibility in content creators’ creative labor
is related to unpredictability across three levels: (1) markets, (2)
industries, and (3) platform features and algorithms [
39
]. Moreover,
despite that content creators strive to elevate their visibility, much
of their labor remains hidden and invisible “through its lack of
crediting, marginal status, and incessant demands for “un/under-
compensated” labor [42].
Creators’ invisible and under-compensated labor matters to the
platform economy which relies predominantly on user-generated
content/videos to drive internet trac and thus ad revenue [
78
].
However, creators oftentimes need to learn by themselves, without
sucient learning resources from platforms, to create content that
follows content policies [
79
]. Although creators can join partner-
ship programs to share revenue with platforms, many researchers
have criticized the power imbalance where creators are unfairly
treated by platforms (e.g., [
21
,
73
,
74
]). For example, platforms as au-
thority usually implement hidden and disproportionate governance
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
structures among creators through creator monetization programs
[
21
] and can distribute career development resources unevenly
across creators [79].
Besides such power imbalance between creators and platforms,
precarity is also evident in creators’ work [
56
]. If a platform closes,
creators might lose everything. Thus, content creators have to
diversify their labor and income streams across multiple platforms
(e.g., YouTube, Instagram) to mitigate risk in a rapidly changing and
unstable context [
56
]. They consider themselves as cross-platform
and multimedia brands. “Not putting all your eggs in one basket” is
a pervasive metaphor in the industry [
56
]. Existing research [
4
,
41
,
76
,
83
] has paid attention to content creators’ cross-platform self
or participatory branding, meaning that content creators promote
on their own or their audiences help such promotion by bringing
more viewers to the site.
However, relatively little work has been paid attention to sys-
tematically elaborating on the workows or practices that cre-
ators develop across platforms. The prior work discussing the labor
of multi-platform branding has broadly focused on how such la-
bor is supported or constrained by certain platform aordances
[
41
,
76
,
83
,
101
] or is eective or not [
4
]. But we have relatively
little knowledge of how such branding practices are organically
arranged by creators. Especially beyond purely branding or pro-
motion practice across platforms, what other creative practices do
creators conduct? How do those practices matter to their careers or
relieve their precarity [
38
] across platforms? Even growing work
has uncovered initial cross-platform practices such as diversifying
income [
56
] or drawing audiences to crowdfunding platforms like
Patreon [
60
]; we haven’t fully understood how dierent platforms
and their combinations are viewed by creators and shape creators’
practices. Thus, building upon this body of relevant prior work,
our work aims to reveal content creators’ creative practices across
platforms.
2.2 Interacting with Multiple Platforms
People nowadays have been increasingly accustomed to an online
life where multiple platforms are available to them, weighing each
platform’s aordances, characteristics, and limits [
107
,
118
,
133
].
Zhao et al.’s interview study with social media users [
133
] found
that their participants would choose which platforms to share con-
tent on based on target audiences and norms around content, ne-
gotiate between maintaining audiences and content on dierent
platforms to separate and allowing certain audience and content
to permeate other platforms, and balance between establishing a
stable pattern of interacting with multiple platforms and embracing
new platforms and emergent practices. Zhong et al.’s examination
of 116,998 user proles on multiple platforms such as Facebook,
Twitter, LinkedIn, and Instagram showed that users adapt their
proles to individual platforms in dierent but still identiable
ways [
134
]. Sannon et al.’s interview study with 19 people with
invisible chronic illnesses (ICIs) reported that their participants
considered audiences on dierent platforms for sharing their ICI
conditions [
98
]. Davidson and Joinson similarly reported that peo-
ple maintained boundaries across social media platforms, such as
using LinkedIn for professional life, while Facebook and Instagram
for social life [
29
]. Clearly, the aordance view of multi-platform
use allows the emphasis on user agency to identify what types of
content and audience a platform aords and to act accordingly.
As users specify dierent platforms’ aordances, they also assign
these platforms to dierent roles in their online social life. Drawing
from media theories, Boczkowski et al. explained how users in
Argentina attribute unique meanings to each platform, such as
WhatsApp as a multifaceted communication domain, Facebook for
displaying the socially-acceptable self, and Instagram for stylized
self-presentation [
15
]. In a similar vein, Karusala et al. discussed
how Indian women carefully chose certain social media platforms
over another based on the meanings they assigned to them (e.g.,
WhatsApp was most private while Twitter was to share opinions)
[69].
While emphasizing the distinctions between dierent platforms,
scholars also become attentive to their interconnectedness and
increasingly take on a holistic, ecological view of how the indi-
vidual user interacts with multiple platforms. Zhao et al.’s work
started to describe social media users’ practices of developing and
maintaining their social media ecology [
133
]. Informed by media
ecology [
114
], social media ecology [
133
], and self-presentation
[
57
], DeVito et al. conceptualized the personal social media ecosys-
tem as “the overlapping set of relationships between an individual
social media user, their presentation-relevant social contexts, the
user’s associated imagined audiences, the platforms these audiences
are imagined to exist on, and the perceived technical properties
(e.g., aordances) of these platforms” [
31
], and stressed aordances
and audiences as two key factors in such personal social media
ecosystem. Using this framework, Nova et al. examined how Hijra
individuals from Bangladesh perform self-disclosure through their
personal social media ecosystems [90].
While the perspective of personal social media ecosystem has a
focus on online self-presentation, Ibert et al.’s conception of plat-
form ecology is more pertinent to our work due to its focus on users’
social practices, and on “the multiple interrelations they create with
their on/oine environments” [
65
]. Ibert et al. view platform ecol-
ogy as a heuristic that “puts user practices and agency centre stage,
accentuates the application of dierent platforms as an integral part
of everyday life, and highlights the complexities of on/oine prac-
tices” [
65
]. The notion of platform ecology has several pertinent
conceptual oerings: It questions ‘user’ as an overly general term
and seeks to address diverse actors such as designers, managers,
and tourists. In this regard, it aligns with our work’s goal to focus on
content creators as a unique user type that seeks to monetize their
content. In addition, while sharing with other ecological approaches
the same goal of addressing the multiplicity in user-platform in-
teraction, it has three extensions worth noting: First, it takes into
account a broader range of platform-based activities to include both
online and oine practices and constraints; Second, it integrates
platforms as actors in a socio-technical assemblage, stressing how
users and platforms both play a role and inuence each other; and
third, platforms are co-existent and interdependent in users’ ev-
eryday practices. Thus, the platform-ecology heuristic provides a
comprehensive framework to tackle multi-platform content cre-
ators’ creative practices, which cut across platform boundaries and
are deeply interweaved into their everyday life. Lastly, the plat-
form ecology situates the user-platform interaction in the platform
economy, where platforms extract value from creators’ audiences.
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
Figure 1: The multiple platforms that our participants work for.
The platform ecology heuristic thus serves as a theoretical start-
ing point as we delve into content creators’ practices and investigate
how their practices uniquely contextualize the heuristic. Combined
with our empirical data, we aim to depict what makes up the creator
ecology where content creators maneuver dierent platforms in
their creative practices.
3 METHODS
We conducted a qualitative study by interviewing creators who
post content on at least two platforms and used inductive thematic
analysis [17] to analyze the data.
3.1 Data Collection
We conducted 21 interviews with content creators who have posted
content on at least two platforms. After obtaining approval from
our institution’s Institutional Review Board (IRB), we used pur-
poseful sampling [
113
] through participant criteria, including (1)
someone over 18 years old, (2) identifying themselves as content
creator making content to audiences, and (3) creating content on
at least two platforms (e.g., YouTube, Twitch, Spotify). We made
and disseminated a recruitment yer on social media such as Twit-
ter, online communities on Discord, which are comprised of either
creators from dierent content categories or certain creators’ fan
groups, subreddits such as r/Instagram, and the authors’ personal
contacts of creators (i.e., convenience sampling). As shown in Table
1, we recruited 21 creators from dierent content categories and
with diverse fanbase. Each participant was compensated with a 20
dollars gift card.
We held all interviews as well as recorded and transcribed them
through Zoom. The duration of each interview ranged from 30
to 88 minutes, with a median
=
54 and average
=
54.9 minutes.
The interviews were conducted from January to March 2022. We
received verbal consent of voluntarily joining this study from each
participant before interview. Also, we informed participants that
their interview data would be anonymized, and they reserved the
right to withdraw from the interviews whenever they wanted.
We created and followed a semi-structured interview/discussion
guide to conduct interviews, which included seven sections: (1)
warm-up questions, (2) platform or account use, (3) content creation
practices, (4) content sharing, (5) monetization or prots-making,
(6) interactions with audiences, and (7) creator moderation experi-
ences. Warm-up questions were mostly aimed at ice-breaking in
conversations with creators by asking about their demographics,
such as what gender and race they identify with, their age, country,
as well as platforms, channels, frequency, and duration dedicated
to creating content consistently.
All the rest six sections of interview questions were designed
based on the fact that participants work on more than two plat-
forms. In the platform or account use section, we asked: “Do you
dedicate your eorts to all platforms equally?” Then, depending on
participants’ answers, we asked, “why did you dedicate more (or
fewer) eorts on [platform]?” We further asked, “do you have one
account or channel per platform?” and depending on their answers,
we followed up with probes. The content creation practice section,
as its name shows, aimed to understand “how did you create content
for dierent platforms?” and could be detailed like “do you script
your content before posting for every platform? If so, how do you
do that? If not, why?” The content sharing section, inspired by prior
work focusing on general social media users’ multi-platform con-
tent sharing [
103
,
133
], included questions asking about procedures
of how creators share their content across platforms. The monetiza-
tion or prots-making section, inspired by work about partnership
programs on platforms [
73
,
93
], involved questions about “which
platform are you eligible for monetization?” and “how do you make
money from dierent platforms? Is there a composition dierence
in terms of income you receive from platforms?” The audience
section, informed by prior work around parasocial relationships
[
95
,
125
] and social support between audiences and creators [
123
],
aimed to understand how creators measure their audiences, interact
with them, or build up relationships with them. The last section,
creator moderation experiences, aimed to understand how creators
experience dierent moderation, such as income or visibility de-
duction, content removal, or account suspension across platforms.
This section of questions was inspired by prior work focusing on
creator moderation [
3
,
78
,
132
]. In the process of interviews, once
we found interesting points related to our research question that
needed to be elaborated, we put forward probes, i.e., asking follow-
up questions. Additionally, some participants shared their screens
or sent screenshots to permit our use. This supplemented our data
collection.
3.2 Data Analysis
We used inductive thematic analysis [
17
] to analyze the whole
interview dataset through NVivo 12, a qualitative data analysis
software. The rst author rst read through and familiarized himself
with the dataset. Then, the rst author assigned initial codes to all
ideas expressed in the interview dataset. In the process of assigning
codes to ideas, all authors held weekly meetings to discuss each
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
Table 1: Creators’ prole information. Representative platform refers to one of the platforms that creators work for (regarding
the multiple platforms each participant works for, please refer to Figure 1.). Fanbase is the scale of subscribers on the
representative platform. Category refers to the content genre of the representative platform participants identify with.
[Work] status is self-identied by creators depending on their time spent on content creation. Career refers to how long creators
dedicate their time to create content consistently by the date of interviews. Recruit indicates a participant is either recruited by
purposeful sampling (coded as +) or direct personal contacts for convenience sampling (*). “N/A” means our participants did
not disclose the information.
P#
Representative
Platform
Fanbase
Category Age Status
Country
Race Gender Career
Recruit
P1 Twitch 90 Gaming 24 part-time Nepal Asian Male 1 year +
P2 TikTok 599.4K Entertainment 30 part-time Brazil White Female 13 years *
P3 YouTube 35.1K Music 22 full-time US White Male 5 years +
P4 YouTube 13.4K Entertainment 27 part-time US Asian Female 1.5 years *
P5 YouTube 70.2K Education 38 part-time Mexico Hispanic Female 3.5 years *
P6 YouTube 101K Entertainment 38 full-time US White Male 8 years +
P7 YouTube 2.61M Education &
Entertainment
59 part-time US White Male 12 years +
P8 YouTube 31.3K Gaming 20 full-time US White Male 4 years +
P9 TikTok 829.5K Gaming 18 part-time US Asian Female 8 months +
P10 TikTok 1.3M Comedy 24 full-time US White Male 1.5 years +
P11 TikTok 42 Gaming 25 full-time US Black Male 1 year +
P12 Twitch 48.9K Gaming 21 part-time Canada Asian Male 2 months +
P13 Onlyfans N/A Entertainment 30 full-time US NA Female 7 months +
P14 YouTube 290 Gaming 22 full-time US Hispanic Male 10 months +
P15 Twitch 5.9K Gaming &
Entertainment
18 full-time US Mixed Male 15 months +
P16 Twitch 382 Variety 20 part-time US Black Non-binary 2 years +
P17 TikTok 706.8K Tools &
Technology
27 part-time US White Male 1.5 years +
P18 Instagram 5.33K Fashion 18 part-time Mexico Hispanic Female 1 year +
P19 Facebook 1.601K Gaming 31 full-time UK White Female 6 months +
P20 Instagram 1.66K Lifestyle &
Business
32 part-time US White Female 5 years +
P21 Instagram 2.66K Lifestyle &
Fashion
N/A part-time US Asian Female 3 years +
individual code and its correspondence to each idea – how each
initial code could precisely describe a data unit in the interview
dataset. Here, a data unit could be a sentence or a whole paragraph,
depending on how many details a participant presented to describe
an idea. After this, the authors conducted rounds of coding by
identifying the patterns (e.g., higher-level themes) among initial
codes, grouping similar codes together to be a theme, and further
grouping similar themes to form an overarching theme. For example,
we placed the initial code, “distilling,” under the theme, Platform-
specic Content Curation, and they both conceptually belonged to
an overarching theme, Cross-platform Content Synchronization.
For another example, we assigned “attracting online trac from
one platform to another,” an initial code to “cross-platform audience
transfer,” a theme. Then, this theme was grouped with other similar
themes under section 5.3.
All authors also re-examined (1) the codes that had been al-
ready assigned to certain themes and (2) the connections between
themes and higher-level themes. For example, the quotation, “I
couldn’t just sprout anything; I was really bad at improvisation
. . .
.”
in Content Type was initially coded as “preferring pre-cording
video creation on YouTube over live streaming on Twitch” under
the theme, “content type.” Then, in weekly meetings with all au-
thors, they all agreed that this theme belonged to a higher-level,
overarching theme, “prioritization,” since the participant funda-
mentally prioritized certain platform for content creation due to
content format dierences. This showed our data analysis process
was iterative by moving back and forth among data, codes, and
themes. We ultimately ended the analysis process by acquiring a
sound thematic scheme, including three overarching themes, to
answer our research question informatively.
3.2.1 Researcher Positionality. The rst author has been an ama-
teur video content creator on TikTok, YouTube, and Bilibili, and has
been in personal contact with over 12 other creators across plat-
forms and content categories for over two years. The other authors
do not actively post videos on social media but have generated tex-
tual content. They have been active and long-term viewers of video
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
content of diverse categories such as education, gaming, and enter-
tainment. Our personal interests in and engagement with content
creators motivated and informed this research, supplying sucient
domain knowledge for us to develop engaging interview questions
and make sense of our interview data. However, we strived to
achieve “empathetic neutrality” [
96
], whereby we sought to avoid
possible biases and to be as neutral as possible in the collection,
interpretation, and presentation of the data.
4 BACKGROUND: CONTENT FORMAT,
CREATOR TOOLS, AND CREATOR
ECONOMY
This study focuses on content creators who primarily create video
content. Platforms support dierent formats of creative content,
among which videos are the most popular. A typical format is pre-
recorded videos supported by platforms like YouTube and Vimeo. In
recent years, a short-form, pre-recorded video format (e.g., 15 or 30
seconds or more) has drawn attention from audiences and creators
as well. TikTok, as one of the rst several platforms supporting such
format of content, became popular in the US [
108
] back in 2020.
Later, many video-sharing platforms started supporting short-form
videos. YouTube released YouTube Shorts, a short video platform,
in September 2020. Instagram and Facebook also launched reels (i.e.,
similar short-form videos) in the US back in September 2021 [
84
].
In addition to the pre-recorded video format, many platforms also
support live streaming services, such as YouTube Live, Facebook
(i.e., Facebook Gaming), TikTok, Twitch, and Instagram.
Given these content format dierences, platforms oer content
creation tools with dierent focuses. For short-form, pre-recorded
videos that audiences frequently watch from smartphones, TikTok,
Instagram, Facebook, and YouTube Shorts oers mobile creator
tools with various types of functions, such as lters, media library,
and pre-shooting eects (see an example from (1) in Figure 2), com-
pared to editing tools for longer video (see an example from (2)) that
creators might more frequently edit from PC but not smartphones.
Besides, living streaming services like Twitch [
116
] or YouTube
Live [
105
] oer creators clipping tools to extract short, sharable
clips from their longer live streams. And this function exclusive to
live streaming inherently does not apply to pre-recorded video.
Although there might be some dierences in video editing func-
tions across platforms or content types, platforms oer similar
tools for understanding content performance. Content performance
refers to metrics or analytics that creators could use to understand
how successful they are on a platform, such as their views, the
amount of money (e.g., ad income) paid by the platform, comment
and subscription numbers, and more. As (3), (4), and (5) pictures
shown, the main performance metric stressed on the higher position
of the user interfaces is audience engagement (e.g., viewing, reten-
tion, commenting, sharing, etc.). For example, TikTok (3) stresses
views and watching time (e.g., total or average watching); Facebook
(4) also prioritizes views as well as user comments and sharing, and
YouTube (5), again, presents watching time and views as primary
channel performance metrics to creators.
Those platform-provided content performance tools focus on
audience engagement metrics because many social media corpo-
rations treat monthly active users and time spent on content con-
sumption as their key performance metrics (KPIs) and decide what
amount of money they will distribute to creators by audience en-
gagement scale (e.g., Creator Fund on TikTok [
111
]). For example,
YouTube [
128
] relies on advertising (ad)-supported content in their
revenue models, and such ad income is calculated by audience en-
gagement [
14
]. Before platforms receive ad income, they conduct
internal auctions with advertisers and then place platform-wide
advertisements (ads). This is the time when creators play a role
in having ads placed around their content and then share the ad
income with platform. Platforms standardize this income-sharing
procedure through creator partnership programs, which are similar
across platforms. TikTok’s Creator Fund [
111
], YouTube’s Partner
program [
129
], Facebook [
85
], and Instagram’s Partner Moneti-
zation [
86
] collectively share a similar logic that more audience
engagement (e.g., views, liking, commenting) leads to higher in-
come distributed to creators. Twitch’s aliate or partner programs
focus more on fan subscription than ad income [
106
], and they
emphasize that audience engagement matters to creators to get
better chances of gaining more subscription income. Such demand
for metric quantication is common in the social media entertain-
ment industry [
28
]: Metrics such as liking, disliking, following, and
more have been seen as validation for creators’ success. As Poell
et al. noted, “platform metrics have become central to key elds of
cultural production; they trigger anxieties over the perceived loss
of creative autonomy” [92].
Creators thus need to conduct a variety of business activities
to sustain and grow their creative careers. For example, as audi-
ences accumulate, creators are able to conduct brand collaboration
directly with external advertisers by posting promotion content
for them, selling merchandise (e.g., t-shirts, mugs), receiving fan
donations directly on platforms (e.g., YouTube Giving, PayPal),
and selling subscriptions and exclusive content on crowdfunding,
third-party platforms like Patreon, or more [
66
]. However, in a com-
petition with 50 million people identifying themselves as creators
[
36
], creators need to connect closely with audiences to establish
their brand that can be distinguishable from other creators [
66
] in
order to make their creator career sustainable [32].
5 FINDINGS
We identied three primary practices participants developed to
congure creator ecology, including platform prioritization, cross-
platform content synchronization, and audience management
across platforms (e.g., maintenance, transfer, and conversion).
5.1 Prioritization
While prior research has found creators promote their channels and
content across platforms [
41
,
83
,
101
], we further uncovered that,
although creators use multiple platforms, they do not use them
equally. Rather, creators consciously prioritize certain platforms
based on careful considerations. Prioritization refers to participants’
practice of choosing one out of multiple platforms as the primary
for content creation and dissemination. Participants still value other
secondary platforms but usually spend most of their creative labor
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
Figure 2: Examples of creator tools oered by dierent platforms. (1) and (2) are video editing tools oered by TikTok and
YouTube, respectively. (3), (4), and (5) are examples of video performance analytics tools oered by TikTok (content level
analytics), Facebook (channel level), and YouTube (channel level) sequentially. (1) and (2) are from the authors’ TikTok and
YouTube accounts, while (3), (4), and (5) pictures are from our participants.
Table 2: Personal Considerations of Deciding to Prioritize Labor for Certain Platforms over Others.
Theme & Sub-theme Description
1 Professionalization This describes to what extent a platform can support content creation as a profession.
1.1 Monetization Platforms’ dierent monetization models and which platform oers better income.
2 Audience Connection Scope This describes which platforms oer participants the deepest and widest audience connection (e.g.,
content reach).
2.1 Community Closeness This means which platform can aord participants to build up deeper and closer communities with
peers (e.g., creators, audiences).
3 Content Type This describes what content format that a platform supports primarily.
3.1 Content Quality This describes whether the quality of content on a platform meets participants’ expectation.
4 Cost of Content-related
Work
The cost of creating and distributing content or other content-related work on certain platforms.
4.1 Real-life Availability Participants prioritize content creation on certain platforms that would cost less of their time.
4.2 Labor Cost Participants prioritize platforms by the labor they can handle for content creation.
on and derive most of their revenue from the primary platform.
The primary is thus perceived as the most important platform in
participants’ creator ecology. Participants detailed four primary
dimensions that they considered when prioritizing a platform, as
summarized in Table 2.
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
5.1.1 Professionalization. Professionalization describes the extent
to which a platform could support its creators’ career path to be-
come a professional. Participants prioritize certain platforms given
whether they can eectively aord participants’ content creation
as a profession. For example, P14 said:
I want to promote [my] YouTube [channel], not just
because I want to have everything in one main plat-
form but because there are a lot of oers and oppor-
tunities to make this not only a hobby but a career
path, and with TikTok, I do make some money o,
but it’s nowhere near what YouTube can bring you,
especially later on to the future. [P14]
P14 prioritized YouTube over TikTok. The fundamental reason
that he thought TikTok cannot be like YouTube to support his career
is the number of business opportunities he can leverage to earn
money.
Like what P14 did, P20 prioritized Instagram because it can better
support her merchandise-selling business: “I feel like TikTok and
YouTube are just more like views sites but Instagram is more like
connecting with customers. I could post a beauty product [and] I
test it out.”
Monetization. If P14 and P20’s prioritization reasonings speak
about general business conditions for professionalization, other
participants prioritize certain platform by the amount of money
they could directly earn from the platform’s partnership programs
(e.g., ad income on YouTube [
129
], Subscription income on Twitch
[106]). For example, P6 said:
My main primary income sources are from YouTube.
There’s a limitation [of] YouTube [that] we tend to
release longer form content, but it’s impossible to do
that on TikTok, unless multiple parts, and if you post
multiple parts on TikTok, you’re gonna hit diminish-
ing returns. [P6]
At a surface level, P6 deprioritized TikTok because it cannot
host longer videos. More fundamentally, his prioritization action
depended on the amount of income he could earn on which platform.
Since YouTube allows more ads in a video longer than eight minutes
[
130
], leading to more ad income, P6 prioritized content creation
on YouTube.
5.1.2 Audience Connection Scope. Participants prioritize certain
platforms based on which ones oer a larger scope of audience
connection. For example, P9 prioritized TikTok over other platforms
because, as P9 said, “I have the most following on TikTok, and most
people, when they nd me on Twitter or YouTube, they know about
my TikTok rst.”
Community Closeness. Participants also decide the priori-
tized platform by which platform can support deeper and closer
communities with audiences and other creators. For example, P8
said:
You’re posting a one-minute video on TikTok; they’re
not going to have that connection with you as a com-
munity, such as Twitch or YouTube. (
. . .
) I have people
come into me and said they were going to commit
suicide, and I talked them out of it. But for TikTok,
Instagram, Twitter, stu like that, you’re not going to
have that connection, where you cannot talk to them,
one on one. [P8]
P8 prioritized YouTube and Twitch over TikTok, Instagram, and
Twitter. His reason was that the platform aordances on Twitch and
YouTube (e.g., comments, longer content format) could motivate
more intimate audience engagement, while the audiences on other
platforms were less likely to engage with him interpersonally.
5.1.3 Content Type. Content type refers to the primary content
format that a platform supports. For example, participants viewed
YouTube as primarily a place for sharing long video content, even
if it (i.e., YouTube Shorts) also supports short videos like what
TikTok does. Our participants explained in detail how they made
prioritizations based on content type. For example, P1 said:
I couldn’t just sprout anything; I was really bad at
improvisation [on Twitch]. It’s more comfortable for
me to script videos because I feel like I haven’t missed
anything to sit in that video. So, it’s more of making
sure that I’m saying everything my point and not
misunderstanding. [P1]
P1 prioritized YouTube over Twitch for content creation because
he preferred pre-recording video over live streaming, where he
was afraid that he cannot explain things completely in a short-time
frame and real-time.
Perceived content quality. Participants also prioritize certain
platforms if they aord and support their desired content quality.
For example, P4 prioritized YouTube for more content creation
over TikTok, as she said: “I wouldn’t really be proud of my TikTok
videos; [they] are short. I feel like there’s no eort.” In contrast,
some participants prioritize certain platforms because they desire
to create the perceived lower quality of content that those platforms
support. For example, P17 said:
If someone makes a comment, and I think it’s worth
making, then absolutely I’ll do that on [TikTok], [but]
YouTube is not as easy as it is on TikTok because Tik-
Tok is very casual, whereas YouTube is not as casual.
If you post a video, it has to be a good video every
time. You can’t post a terrible video of you just saying
hey, how you doing, buddy. [P17]
P17 prioritized TikTok over YouTube. The underlying reasoning
behind such action was that P17 mentally connected the impression
of a casual style of content with TikTok while connecting the im-
pression of higher-quality content with YouTube and he preferred
lower quality of content creation on TikTok. While he did not dis-
close why he developed such impression dierence, it impacted his
prioritization actions.
5.1.4 Cost of Content-related Work. Participants’ prioritization
strategy was also informed by the cost of creating, distributing con-
tent, or other content-related work expected on dierent platforms.
For example, P19 said:
When I rst started out [on Facebook], I’d already
hit my hundred follows. So, I had more contacts on
Facebook than I did anywhere. Right now, I am getting
more contacts on Twitch, and I’m not far from being
aliated with Twitch, but my contacts were always
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
on Facebook, and that is why it’s my primary platform.
[P19]
P19 prioritized Facebook over Twitch because she already had
built more connections with creators and audiences on Facebook.
And she personally did not want to perform more work, such as
attracting new audiences and building up subscribers on Twitch.
Real-life Availability. Particularly, participants choose to pri-
oritize content creation on certain platforms that would cost less
of their time. For example, P20 said:
The only reason I don’t do it [on YouTube] is I just feel
there’s not enough time, and I have two kids. So that’s
the reason why I do the short videos [on Instagram]
because it’s just easier to make a two-minute video
than to make a 15-minute video. [P20]
P20 prioritized Instagram over YouTube because she did not
have as much time for content creation on YouTube as on Insta-
gram. The reasoning behind such action was that P20 associated the
time-consuming impression of content creation with YouTube and
connected the one of fast and short video creation with Instagram.
Labor Cost. Participants prioritize platforms by the extent of
labor they can handle for content creation. For example, some partic-
ipants prioritize platforms that require fewer intellectual eorts to
make content more attractive. As P5, an education content creator
who has prioritized YouTube over TikTok, said:
I have noticed the younger the person, the shorter
their attention span. So as a consequence of this dif-
ference in age groups, you have less chance to retain
users on TikTok unless you are entertaining enough;
you need to be very emotional, which usually isn’t
the way I do. [P5]
Here P5 assumed that TikTok has a much younger user base
than YouTube, and thus a dierent dynamic for user retention. So,
P5 deprioritized content creation on TikTok because she assumed
that making videos more entertaining and emotional is a typical
way to acquire longer audience retention, and she usually would
not make such eorts.
5.1.5 Dynamic Prioritization. Participants’ prioritization practice
is not static. They dynamically shift their prioritized platforms
because their needs and personal interests change over time, usually
based on creator channel growth, fanbase, and income. For example,
P17, who changed his prioritized platform from YouTube to Twitch,
said:
Originally, I only streamed on YouTube because I
thought YouTube would be easier to get audiences on.
I was kinda right, but there’s a problem with YouTube,
[where] I don’t know if they (YouTube) socially en-
gineered, but on Twitch, it’s so much easier to nd
people to collaborate with, and it’s easier to grow
on Twitch after you start getting established if you
can network with other smaller streamers. (
. . .
) I still
stream on YouTube, but now I’m streaming on twitch
more. [P17]
P17 shifted his prioritized platform from YouTube to Twitch
while still streaming on both of them. His reason for such shift was
not only about the easiness of creator collaboration but essentially
about his assumption that collaborative content creation can attract
more audiences compared to individually created content. And P17
can more easily verify his assumption on Twitch than on YouTube,
so he prioritized Twitch to get “easier to grow.”
Besides strategically focusing on growing fanbase or content
reach, some participants consider their preference of content types
as one of the determinants in choosing prioritized platforms. For
example, P8, who has prioritized content creation on YouTube, said:
I wanted to be mainly a Mixer streamer, but everybody
on there like, why me not go to YouTube and do better
scripted videos. So, I switched over to there, and it
was so much easier, time-wise; you can edit out inter-
ruptions going on, and I wanted to go back streaming
but just time kills you in that kind of industry. [P8]
Mixer was a live streaming platform owned by Microsoft, and it
was discontinued in 2020 and redirected to Facebook Gaming. P8,
in the above case, shifted the prioritized platform from Mixer to
YouTube because of content type dierences. And essentially, P8
thought scripted content creation oered him better time exibility
and user agency in creating content rather than live streaming, a
content format that he was unable to edit.
Taken together, our participants conducted dierent decision-
making on personal interests to prioritize certain platforms over
others in their creator ecology. Conducting such decision-making,
including professionalization, the scope of audience connections,
content types, and cost of content-related work that platforms
aord or require, directly impacts whether they could receive the
extent of investment return they want from content creation. So,
participants felt the necessity of prioritization, and they also might
change prioritization given their personal interests at the moment.
5.2 Cross-platform Content Synchronization
Cross-platform content synchronization describes participants’ cre-
ative action of synchronizing their content across platforms. They
seek to publish variations of content on multiple platforms at the
same rate. Such content can be newly created, curated from their
old content, or identical across platforms. Participants created vari-
ations of content because of platforms’ aordance, characteristics,
or limitations, as well as catering to platform-specic audiences’
interests.
5.2.1 Sequential Workflow based on Creator Tools. Prior work has
found creators on certain platforms such as Twitch [
80
,
122
] and
YouTube [
79
] leverage creator tools such as Twitch Analytics and
YouTube Creator Studio to better understand their content perfor-
mance. Beyond using these creator tools on one particular platform,
we found participants created a sequential workow of content
synchronization from one platform to others based on the creator
tools they can use. That is, they specied which platform should
be used before which platform. For example, P5 described:
YouTube [Studio] tells you which areas of your video
are mostly viewed and where you lose people. So, [for]
where I see people leave in the video (lower retention),
I will cut it [o] for Facebook and Instagram. [P5]
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
P5 leveraged historical video analytics oered by YouTube Studio
dashboard to synchronize shorter videos on Instagram and Face-
book. That is because she believed popular video clips (i.e., with
higher audience retention) on one platform could do a better job of
maintaining audience engagement and retention on another plat-
form compared to non-popular clips. So, such information guided
her content curation for other platforms.
Beyond attracting and retaining audiences, participants de-
scribed that certain platforms’ aordance or limitations shaped
their workows to be sequential. For example, P21, who actively
creates short videos across platforms, said:
TikTok is actually more user friendly because I can
mix video and pictures together [in] reels, [but] on
Instagram; I cannot do that. I can either only do the
picture or I can do videos. So, I still use TikTok to
create [reels for Instagram]; after I create it, I auto-
matically post [it] to Facebook because it’s integrated.
[P21]
P21 described her sequential workow: creating and distributing
videos rst on TikTok, then on Instagram, and nally on Facebook.
Her reason for such workow was that TikTok oers video edit-
ing tools that can better suit her needs for content creation than
Facebook and Instagram. And since the videos across these three
platforms were the same, the sequential workow streamlined her
content synchronization.
Other participants also stressed that sequential workow among
platforms can be dynamic, depending on which platform’s cre-
ator tools suit their particular videos the best. For instance, P20
mentioned: “Sometimes, you can also see I’ll post TikTok rst and
Instagram later, [because it] just depends on what kind of video
because like Instagram, video editing oers one thing and then
TikTok editing oers another, so that’s the reason why switch back
and forth.”
5.2.2 Platform-specific Content Curation. Platform-specic con-
tent curation refers to our participants’ action of distilling or seg-
menting their content to synchronize on dierent platforms to suit
platform-specic audiences’ interests. Before this synchronization
action, participants gauged platform-specic audiences’ interests.
They described two ways of gauging: (1) distilling “highlight” mo-
ments from longer videos for short video platforms and (2) seg-
menting content into dierent subset content topics for audience
interests across platforms.
Distilling. Participants empathized with audiences and knew
what kind of content would be liked by what audiences, so they dis-
tilled highlight clips from their long content that had been created
on other platforms. For example, P8, who creates long videos on
YouTube and also creates short videos on TikTok and Instagram,
said:
What I’ll do is to nd a good area to break it (video)
up; it’s a brand new video like I just posted ve min-
utes ago. [So,] I’m not going to really know [from]
the dashboard [about] when it’s really funny [in the
video], where it’s not like in the middle of somebody
saying something. [P8]
P8 distilled the “highlight” clips he considered would intrigue
audience interests on short video platforms from longer videos
on YouTube. Since he conducts a fast-paced content synchroniza-
tion, creator tools like YouTube Studio dashboard cannot, in time,
support him to curate funny or entertaining content from longer
YouTube videos. So, P8 empathized with and predicted his audi-
ences’ interests to make fast-paced content synchronization on
short video platforms possible.
P12, who streams on Twitch and posts videos on TikTok, also
supplemented this content curation procedure in detail:
You just have to be honest with yourself and just say,
like oh, will this somebody enjoy this if I saw this on
TikTok; would I enjoy watching this; will I stay and
watch this video, or would I just scroll past it. I like
going through all my clips because I’ll clip something,
and then I’ll look back at it later and be like, this is
not funny I do; I will not use this. [P12]
P12 carefully distilled certain clips from his live streaming record-
ing on Twitch and posted them on TikTok. He made dierent cog-
nitive eorts such as speculating and empathizing with audience
interests on TikTok, identifying what moments in his live stream-
ing might meet such interests, and deducing audiences’ scrolling
behaviors for his curated video. P12 also made behavioral eorts to
review both longer live streaming recordings and clipped content
to make sure his highlighted clips could successfully attract more
audience retention.
Segmenting. Participants also segment their content into various
topics to cater to cross-platform audiences’ interests. The dier-
ence between segmenting and distilling is whether participants
recognized the topics of curated content diered from the topic
of original content. For example, P9, who streams on Twitch and
posts videos on TikTok, said:
Since I’m a girl playing video games, if someone is be-
ing very misogynistic and toxic to me [in my stream-
ing], that’s something I might clip because I can show
other people, and they’d be interested in watching.
[P9]
P9 noticed certain parts of her Twitch content as suitable for
a TikTok audience, so she clipped it. Such clip depicting a toxic
moment is not directly associated with her streaming content topic,
gaming, but she assumed it would be entertaining and attractive
enough for audiences on TikTok. P9’s case showed how she cu-
rated subset content topics from one platform to cater for certain
audiences on another.
Beyond individual content curation, we found participants even
curated their channel’s content category with dierent granularity
across platforms. For example, P14, who makes gaming content,
explained:
All channels are just gaming channels, but they’d
have their own smaller category, depending on the
platform it got with. On YouTube, people like a little
bit longer content, so I’d say it probably is a lot of
gameplays or a walkthrough. But then I’ll take target
TikTok with a really short funny video that that can
go viral as just depending on the algorithm. (
. . .
) On
Instagram, a lot of people like informational videos,
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
so I’ll go on Instagram to post any game information
I know about it because those seem to do better on
that platform. [P14]
P14 understood that his audiences across platforms have dierent
interests, so he curated dierent granularity of gaming topics of
video content for each of his channels. He sensed such platform-
specic audiences’ interests by observing which topic of videos
received more views than another topic across platforms. P14’s
case showed he exerted agency to make customized content that
can maximize his investment return of content creation on each
platform.
In sum, we found participants creatively crafted their sequential
workows of synchronizing content due to dierent platforms’
aordances, characteristics, or limitations. They further gauged
audience interests cross-platforms to curate customized content for
each of them.
5.3 Multi-platform Audience Management
Audience management describes strategic processes of interacting
with and managing audiences from dierent platforms. Extending
prior work that has uncovered audience management on Only-
fans [
117
], Twitch [
124
], or dierent live streaming platforms [
123
],
we found how creator participants strove to grow their audiences
across multiple platforms by using three specic strategies: (1)
maintaining the current audiences across platforms, (2) transferring
audiences across platforms, and (3) converting current audiences to
dedicated fans. These three strategies are not mutually exclusive be-
cause either audiences or dedicated fans could help our participants
sustain the creator ecology.
5.3.1 Maintaining cross-platform Audiences. No matter what plat-
forms our creator participants choose to prioritize, they maintain
their consistent presence and connections with audiences. For ex-
ample, P15, who creates content on Twitch, TikTok, and YouTube,
said:
If I have these TikTok videos, so, like on days, where
like if I can’t stream or I didn’t upload a YouTube
video, I can at least get something out there to keep
the engagement going, because one thing you don’t
want to do is having nothing coming out for a long
period of time, because it’s the Internet, people can
forget about you and move on to a new creator if they
think you’re done. [P15]
P15 would create new videos on TikTok even though he was not
active in streaming on Twitch or creating a new video on YouTube.
The competition for obtaining audiences’ attention and consump-
tion between creators, especially those who are in the same content
genres as P15, was the main reason for him to actively maintain his
presence with audiences.
Besides actively posting content, participants also described the
importance of responding to audiences. For example, P16, who
streams on Twitch and creates content on TikTok, mentioned:
I always try to be proactive and engaging; if someone
leaves a comment on TikTok, I’m going to leave a
comment as well for them to realize, “oh, you actually
recognize that I’ve left a comment on your video.” (
. . .
)
I try to make sure to almost multitask to both play
the game, or have a conversation with whoever I’m
streaming with or the chat as well [on Twitch]. I feel
like I’m a big proponent that you have to engage with
your chat in order to build community. [P16]
Either on Twitch or TikTok, P16 actively responded to audiences
and other creators. Although P16 mentioned such active interac-
tions were mainly for creating his own audience community, what
he actually did was to sympathize with the reactions of other cre-
ators and audiences who would receive his responses. After they
have such social interactions, P16 can maintain his closeness with
audiences across platforms.
Sometimes, participants customized responses to maintain their
connections with audiences across platforms. As P20, who is a small
business owner on Instagram, said: “Once I see someone comments
on my video, I’ll click their prole a little bit on Instagram [with
more professional responses], and then on TikTok, I just be like
“TY” for Thank you.”
5.3.2 Cross-platform Audience Transfer. Cross-platform audience
transfer refers to a practice of how our participants strategically
transfer audiences who consume their content on one platform
to their potential audiences on another platform. Such action not
only helped audiences streamline the consumption of our partici-
pants’ cross-platform content but also grew participants’ careers.
Audience transfer thus becomes an important operation triangu-
lating online trac for sustaining participants’ creator ecology.
Sometimes, our participants conducted selective audience transfer
between platforms, but oftentimes, they embraced the possibility
that audiences freely choose whatever platforms. For example, P9
said:
I always put my socials on TikTok; I have like a Link-
tree. That links them (audiences) to all my socials.
And the same with my Twitch and on my YouTube.
Another thing is you have to have everything with
the same name, so it’s very easy for viewers to nd
you. [P9]
P9 made two actions to transfer audiences across platforms: (1)
posting her Linktree, a reference page compiling multiple links
everywhere, and (2) keeping her channel names the same across
platforms to streamline audience transfer.
Participants also mentioned that Linktree cannot always be ef-
fective for audience transfer. For example, P12 said:
What I would do is, at the end of my videos, (
. . .
)
say[ing] I’m live over on Twitch because the more
buttons you have to press to get to a place, the less
likely someone will pay attention; because on TikTok,
your attention span is like really small. If you have
to go through a Linktree like sleep on your prole,
it’s really a pain to go to all [socials]. So, then I’ll try
to add it (promotion) to the end of my video or my
comments section. I also have it in the caption of my
videos that I stream on Twitch. [P12]
P12 tried to transfer his audiences from TikTok to Twitch by
actively promoting his Twitch channel in his content on TikTok.
The reason why he integrated such audience transfer actions in
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
content creation was to streamline the ways that audiences can use
to consume P12’s content across platforms instead of exclusively
relying on Linktree.
Although creators tried to promote themselves across platforms
as much as possible, resonating with prior work about multi-
platform self-branding [
41
,
76
,
83
], participants also recognized
the constraints of doing so. For example, P3 said:
If I was to put an [YouTube channel] advertisement
in a Spotify release, that would be intrusive to the
listener experience. So, I promote my Spotify mostly
through my YouTube, so if they’re already listening
to my Spotify, then they probably already follow me
on YouTube. [P3]
The reason for selective audience transfer, as P3 mentioned,
is to t the typical user experience of content consumption on
Spotify. And essentially, he wanted his Spotify prole to be purely
for music content distribution while functionalizing YouTube for
both audience transfer and music content sharing, as he implied that
YouTube’s platform aordance (e.g., video with comment sections)
is more appropriate for such mixed purposes.
5.3.3 Dedicated Fan Conversion. What our participants have been
consistently doing is to convert their audiences to become dedicated
fans who could provide more social and nancial support, no matter
which platform those fans originally come from. These audiences
are not limited to users who generally consume content but also
peers who create content. Along the way, when participants strove
to cultivate dedicated fans, their audience communities will start
to snowball and thus help sustain participants’ creator ecology.
One common initial practice of dedicated fan conversion is to
intrigue audience engagement across platforms. For example, P18,
who creates content on TikTok, Instagram, and YouTube, said:
I have colleagues on my Instagram, and I put them
on my stories; if they can share and save the posts
or share them on their stories, Instagram is gonna
to show them to more people. (
. . .
) Because all the
platforms have the same algorithms, if they (other
creators) share it with people or save it for later, then
there will be more likes and comments. [P18]
P18 asked her peer creators to share, save, and comment on
her content across platforms. Such creator collaboration has been
tested to be eective for both creators in attracting subscribers and
gaining popularity [
72
]. P18’s case further detailed the reason for
this eectiveness: more audience engagement would act as a posi-
tive signal to recommendation algorithms to improve her content
visibility to correspondingly gain more dedicated user engagement.
However, obtaining engaged audiences is not enough for cre-
ators because their career growth is not exclusively supported by
platform income (e.g., ads revenue on YouTube [
129
] or creator
fund on TikTok [
111
]), which is calculated by audience engage-
ment performance. What participants further aimed for was to
convert audiences to be more nancially supportive. Resonating
with a recent work describing how creators on Onlyfans draw au-
diences from social media platforms like YouTube or TikTok [
60
],
we found that P13 strategically conducted audience conversion for
her Onlyfans channel:
It’s just that Onlyfans gives you the information of
what percentage you are among [all creators] in terms
of how much money you make. (
. . .
) They (audiences
on TikTok) can only know your percentage, but you
cannot say the word of it (Onlyfans), then they’ll prob-
ably search on you. [P13]
Instead of saying the keyword “Onlyfans” in videos, P13 implic-
itly advertised her Onlyfans channel on TikTok to draw audiences
to nancially support her on Onlyfans. Due to the platform and
content sensitivity of Onlyfans, P13 implied the importance of fan
donation to her career and, at the same time, avoided content mod-
eration to conduct audience conversion.
Converting and acquiring dedicated fans are not the ending
point for conguring creator ecology. Rather, we found participants
strove to maintain the activeness of dedicated fans, like what P6, a
full-time cross-platform creator, said:
I’m more available to audiences on Patreon, like [who]
give a message to me; I’ll respond immediately. If I
have like an idea, I’ll ask them for input, or I’ll tease
what’s happening to them versus anywhere else, like
Instagram, Facebook, or Twitter; that’s all like promo-
tion. But with Patreon, it’s like making them be more
integrated into the [creation] process where I’ll say
I’m thinking about making a video about this, what
do you guys think. [P6]
P6 involved audiences on Patreon in his content creation pro-
cesses and more frequently and proactively engaged with them
than he did on other platforms. The primary reason for such dis-
crepant treatment is that the fans who nancially support him are
critical to sustaining his full-time creator career, while platform
income calculated by the audience engagement is not. So, he tried
to maintain his dedicated fans.
Taken together, our participants sustained their career growth
and creator ecology by maintaining audiences across platforms.
Then, they strategically transferred audiences from one platform
to others in order to triangulate online trac and improve their
content performance metrics. Eventually, participants aimed to
convert active audiences to dedicated fans who can consistently
support them. So, participants’ creator careers can grow sustainably.
6 DISCUSSION
Our study detailed content creators’ practices in navigating multi-
ple platforms to benet from the platform economy and advance
their creator careers. They do not view platforms available to them
equally but perceive and maintain priorities based on a set of plat-
form aordances and characteristics. They strive to encourage au-
dience and content to permeate other platforms through content
synchronization and audience conversion, which marks a clear
distinction from social media users who seek to balance between
separation and permeation [
133
]. This set of ndings allows us to
reect upon what constitutes the creator ecology for multi-platform
content creation, as well as the labor for maintaining it. Last, we
derive design implications from our ndings for creator empower-
ment and support.
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
6.1 Conguring the Creator Ecology for
Multi-Platform Content Creation
Prior literature has accumulated much understanding of how
users interact with multiple platforms for the purpose of sharing
[
62
,
98
,
133
]. The notion of sharing is social and interpersonal and
communicates a sense of community. The emphasis on sharing
originated from the early promises of social media platforms to
connect people and build online communities. In the context of
content creation, however, this communal ethos has given way to
the business logic that is metric-driven and prot-driven. In the
interviews, our participants attached much importance to the audi-
ence engagement performance of their content, and the number of
views directly correlates with their ad revenue. The platformization
of creative labor, in this sense, is also driven by creators themselves,
who exert agency to congure their interactions with multiple
platforms.
Against this backdrop, our ndings help conceptualize the notion
of creator ecology, which captures a creator-centered ecosystem
where content creators’ creative practices involve the exible, dy-
namic management of multiple platforms and their associated aor-
dances and audiences in order to empower themselves through such
cross-platform management. In creator ecology, creators’ practices
are dierent from the practice of a general social media user who
shares information across platforms. For example, prior work has
found that a general user either disregards the boundaries between
platforms by sharing similar, if not exactly the same, content across
platforms [
103
] or reinforces such boundaries by selectively sharing
content [
133
]. Our creator participants are dierent in their deliber-
ate eorts to dismantle such boundaries by promoting content and
transferring audiences across platforms. Especially depending on
platform-specic audiences’ interests, participants curated dierent
versions of the content or even channels per platform.
These dierences in practices between general users and cre-
ators hinge on their dierent purposes in sustaining an ecosystem
of online platforms. General social media users engage in multiple
platforms to fulll their gratications of self-presentation [
107
],
while creators do so to stabilize and grow their careers. General
social media users consider content and audience traits for selective
content sharing [
133
], but creators like our participants must make
other decisions to construct their creator ecology, such as prioriti-
zation or content curation, as well as consider platform aordances
(e.g., creator tools, recommendation, and monetization algorithms)
and social relationships with audiences.
Thus, our participants make sure to take advantage of what they
can utilize from dierent platforms (i.e., technical aordances) to
congure creator ecology (see Figure 3). Aordance refers to not
only the materiality of design features from devices or platforms but
also the “imagined” [
88
] or “perceived” [
89
] part of it, where users’
expectations about technologies shape “how they approach them
and what actions they think are suggested” [
88
]. This aordances
perspective centering on user agency in human-technology inter-
action has been increasingly adopted to understand the practices
of creative labor like content creators (e.g., [
76
,
83
,
101
]). Resonat-
ing with this line of work, our ndings showed that participants
chose to leverage certain technical aordances, such as the content
format or the perceived content quality on platforms [
101
]. But
instead of leveraging aordances on individual, separate platforms
to support creative practices across platforms [
76
], our participants
detailed how they orchestrated dierent combinations of sociotech-
nical aordances from platforms, non-platform tools, and human
actors [
44
]: they (1) dynamically prioritized content creation eorts
for platforms that align with their personal interests relatively the
best, (2) crafted sequential workows based on creator tools on
one platform to lower cost of content synchronization on multiple
platforms, and (3) selectively promoted certain creator proles or
content due to dierent designs for content consumption across
platforms. Sometimes, such action of dynamically leveraging so-
ciotechnical aordances can be an outcome of compromise. For
example, P5 prioritized YouTube, Instagram, and Facebook over
TikTok due to the mismatch between her eorts of creating edu-
cational content on the former three and the eects of how such
eorts translated to short videos and retained audience attention
on TikTok.
Then, our participants navigate and leverage the social relation-
ships or aordances that they built up with audiences to further
sustain creator ecology. Prior work has uncovered processes of
how audiences, or broadly speaking,. peers, including audiences
and other creators, oer social support to help creators grow their
careers on Onlyfans [
117
], Twitch [
124
], Patreon [
60
], or dierent
live streaming platforms [
123
]. Instead of viewing such process
from the perspectives of support providers, our interviews detailed
how creators, from the support receiver’s perspective, strategically
motivated and maintained such support across platforms. These
strategies showed in a funnel style applied to multiple platforms
(see Figure 3): (1) building audience awareness of creators and
their content, (2) retaining audience attention across platforms, and
(3) leading audience dedication. We could view building audience
awareness as how participants utilized aordances on dierent
platforms (e.g., content formats, content reach inuenced by al-
gorithms) to create a “mirror” of interpersonal relationships with
audiences (i.e., parasocial relationships) [
58
,
76
,
95
] as many as
possible on dierent platforms. Then, for audiences who chose
to consume content, participants tried to retain their attention by
creatively synchronizing and curating content for them across plat-
forms. Eventually, participants aimed to develop a relatively smaller
number of dedicated fans from audiences. This aim can be shown in
how creators synergistically draw audiences from dierent video-
sharing platforms to Patreon for fan donation [
60
], as done by our
participants. But beyond that, our participants more proactively
built intimate, interpersonal relationships with dedicated fans (e.g.,
receiving their feedback for content creation), no matter which
platform they are from, to sustain creator ecology.
Thus, the ways that participants creatively leveraged sociotech-
nical aordances, which are comprised of technical and social com-
ponents across platforms, as shown in Figure 3, empowered them
to boost the economic or social outcomes that they might receive to
what they expected. As such, the creator ecology is individually de-
veloped and maintained by each creator, pertaining to their unique
way of conguring the aforementioned sociotechnical aordances.
It shifts its key components (e.g., which platform to prioritize) their
interdependencies mostly due to economic and professional rea-
sons. Creators are eager to construct the creator ecology based on
the performance metric oered by the platforms [
81
]. Ibert et al.
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
Figure 3: Conguring creator ecology. Creators situate in the center across platforms that are comprised of dierent sociotechni-
cal aordances, such as creator tools and audiences. These aordances shape creators’ practices in conguring creator ecology,
and meanwhile, creators can exert agency to increase the economic or social outcome of their creative practices.
[
65
] are concerned that an ecological thinking might lack critical re-
ections on the power asymmetries in user-platform relationships
and overstate the user agency when general platform users may
have a partial understanding of the business models underlying the
platforms. In the case of creator ecology, as presented in this study,
we start to see creator agency and platform power not just as op-
posite forces but also a somewhat cooperative pair, where creators
strive to be incorporated into and benet from the platformization
process.
6.2 The Labor for Maintaining Creator Ecology
While creators like our participants empower themselves by con-
guring the creator ecology, it is not easy to maintain. As the char-
acteristic power asymmetry between creative labor and platform
exists [
8
], the ways platforms govern or even “exploit” creators are
apparent. For example, partnership programs do not indicate a pure
“partner” relationship but a governance structure that privileges
some users by dierent sets of rules and resources over others (e.g.,
YouTube [
21
,
73
]). Or platforms oer better resources to these mon-
etized partnered creators than unpaid amateur ones [
39
,
79
]. In a
similar vein, even though our participants can prioritize certain
platforms over others for more diverse prot-making possibilities,
they still tried to maintain their presence and retain audience atten-
tion on those less prioritized platforms. There could potentially be
two reasons to explain such practice. First, participants did not want
to miss chances of increasing audience engagement on multiple
platforms since platforms (e.g., [
85
,
86
,
111
,
129
]) generally consider
engagement performance important for distributing income (e.g.,
ad income, Creator Fund). Second, as our ndings showed, partici-
pants desired to obtain more supportive, dedicated fans to directly
receive money from them (e.g., subscription income on Twitch
[
106
], Onlyfans [
117
], Patreon [
60
]) instead of depending heavily
on the income paid by platforms (e.g., partnership programs). Thus,
maintaining creator ecology indicates labor of both serving tradi-
tional platform economy models such as ad-supported content and,
meanwhile, fueling creator or creative economy through connect-
ing with individual fans or external advertisers across platforms
[22, 60, 99].
Much prior work has also distilled another theme of labor, self-
branding, from creators’ practices (e.g., [
23
,
53
,
101
]). It speaks
to a self-promotional strategy, which is necessary but oftentimes
uncompensated [
37
,
53
,
63
], to acquire reputation from people to
secure employment in the freelance-based creative labor market
[
25
,
53
]. This labor is apparent in creator ecology: participants pro-
moted their proles and content as well as transferred audiences
across platforms. Beyond exclusively promoting by participants
themselves, participants needed to intrigue other audiences to con-
duct participatory multi-platform branding [
76
,
83
] and further
asked peer creators’ help for such promotion to sustain creator
ecology.
However, due to the piecemeal, precarity nature of creator ca-
reers [
39
,
56
,
91
], our participants practiced more than branding.
They needed to maintain audiences to sustain creator ecology,
which speaks to relational labor, “an investment toward building
and maintaining” audiences that helps sustain creator career [
7
].
Prior work has explored how microcelebrities practiced relational
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
labor to construct intimacy with audiences on particular platforms
such as YouTube [
10
,
94
], Patreon [
59
], and TikTok [
104
]. Our
interviews further supplemented: creators practice relational la-
bor on multiple platforms. Participants oftentimes engaged with
cross-platform audiences in a consistent way, but sometimes, they
needed to customize interactions with audiences to advance their
relationships on certain platforms, like what P20 did.
Thus, the labor of maintaining creator ecology is plural; it in-
volves participants’ interactions with platforms’ sociotechnical af-
fordances under dierent platform economy environments. And
one facet conspicuously reected by such plural labor is partic-
ipants’ cognitive eorts. For example, they needed to creatively
synchronize content across platforms by empathizing and research-
ing platform-specic audiences’ interests, sometimes along with
the learning cost for technical aordances such as creator tools
and algorithms [
12
,
13
,
78
]. Even, participants needed to conceive
ways of avoiding content moderation to obtain dedicated fans from
multiple platforms due to content or prole sensitivity, as what P13
did.
But if we view the plural labor of maintaining creator ecology as
ideally manageable for each creator, then our participants’ creative
practices tell the opposite of this view. Prior work has explored
how content creators measure their perceived return on labor in-
vestment in self-branding [
101
] and relationship maintenance [
60
].
This is also similar to a trade-o process between labor investment
and monetary gains that digital workers (e.g., Amazon Mechan-
ical Turkers) need to go through [
46
]. Our participants’ creative
practices helped elaborate on such trade-o process – weighing the
benets gained between selective labor investment and practicing
labor as much as possible to maintain creator ecology. For instance,
participants chose to selectively build up intimate relationships
with a smaller number of audiences instead of trying to convert
them all to dedicated fans. This practice might be explained by
their personal availability or a mindset pre-dened by them, such
as “TikTok, Instagram, Twitter, stu like that, you’re not going to
have that connection,” as said by P8. Also, participants prioritized
platforms they were more familiar with, instead of all, due to the
predicted labor of drawing audiences on platforms they were rela-
tively newer to. Hence, maintaining creator ecology is not easy not
only because of the plural labor in it but also because creators need
to go through the trade-o on investment return between selective
labor across platforms and practicing labor as much as possible.
6.3 Design Considerations for Creator Support
Our ndings suggest that our participants empowered themselves
by conguring a creator ecology while platforms have dierent
structural characteristics (e.g., governance [
28
], aordances [
65
]).
However, their creative practices, such as content synchronization
or audience management across platforms, did not simply indicate
that every creator can exert agency to do so. So, we call for designs
that can empower and support creators to continue working across
platforms.
First, designers from single, particular platforms could consider a
mindset of creator ecology in oering features for content synchro-
nization across platforms. For example, our participants segmented
their longer videos or live streaming recordings into several pieces
to be posted on short-form video platforms such as Facebook (reels)
or TikTok. To better support such practice, platforms can not only
oer functions that can help split videos [
105
,
116
] but also suggest
which moments within videos are appealing for segmentation. De-
signing this feature is creator-centered and, meanwhile, benets
platforms with more audience engagement by motivating creators’
passion for content synchronization and re-creation. Third parties
like researchers could also design tools to support advanced cre-
ativity in content synchronization. Such tools could oer content
re-creation suggestions by emotional, engaging keywords detection
on creators’ speech content, higher audio frequency in audio spec-
trum visualization, or platform-specic audience interests inputted
by creators beforehand. Then, similar to how artists distribute music
content across platforms through Distrokid [
33
], content creators
can distribute their curated content across platforms all at once. This
design ideation could potentially streamline content synchroniza-
tion processes and decrease creators’ labor of maintaining creator
ecology.
Second, creativity support tools could be designed to help cre-
ators know better about their audiences across platforms. To sup-
port creators to thrive in the creator economy [
56
], platforms like
YouTube [
131
], Twitch [
115
], TikTok [
112
], and more make eorts
to oer content performance analytics as useful and sucient as
possible. However, our participants showed their desire to under-
stand the eects of audience transfer and conversion across plat-
forms. First, this speaks to a design opportunity of showing trac
source compositions leading to the content. As we notice from the
creator tools in the market so far, YouTube and Twitch provide
such feature, while Facebook, TikTok, and Instagram have not done
so yet. Second, creator tools could consider informing creators of
which audiences are their dedicated fans by analyzing engagement
rate or nancial support level (if there is a direct fan donation model
on particular platforms like YouTube Giving, or subscription on
Twitch) to support creators. This design could benet platforms
as well. If platforms design a “super fans” badge that allows audi-
ences to associate it with creators, it can potentially intrigue more
audience engagements on platforms.
Last, our participants’ creative practices indicate possibilities of
creator career training to support creators. If platforms advance
creator tools, while creators are not aware or lack the knowledge
to use them, creators can hardly exert their agency to maintain cre-
ator ecology. Also, if platforms want to gain more audiences’ time
spent on consuming content while oering creators low-quality
onboarding experiences to understand social engagement designs,
creators (e.g., P19) might not make a try to draw new audiences on
certain platforms. Thus, oering creator career training, namely
more information provision on how to thrive on platforms, can
help stabilize creators’ income and careers.
7 LIMITATIONS AND FUTURE WORK
Our qualitative study aims to uncover creative practices of how
creators congure creator ecology. Thus, the 21 creators we inter-
viewed cannot represent all creators in terms of practices across
platforms. We also do not aim to make claims about how creative
practices are associated with whatever fanbase size, career length,
partnership programs, and content genres. But beyond a qualitative
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
perspective, we did see the possibility for future work on under-
standing how creators work across platforms through surveys or
large-scale analysis on metadata of the content.
Also, we do not aim to specify whether content synchronization
violates platform-specic policies or ethical norms across platforms.
Even though our participants barely discussed this as an issue, as
researchers, we do acknowledge it might be tricky and intricate
to view or dene the legitimacy of content synchronization. Thus,
future work can explore how creators, platforms, and policymakers
conceptualize the content synchronization phenomena.
8 CONCLUSION
Our study enunciates a notion of creator ecology through creators’
practices on multiple platforms. This creator-centered notion de-
scribes an ecosystem where creators conduct dierent creative
practices through the exible, dynamic management of multiple
platforms and their aordances and audiences to empower them-
selves through such cross-platform management. Due to the pre-
carious or uncertain nature of creator careers, conguring creator
ecology could help stabilize creators’ income by prioritizing certain
platforms over others, synchronizing content across platforms, and
managing audiences, and converting them to dedicated fans. How-
ever, creator ecology conguration does not refer to a one-time
manner practice but the consistent labor for maintaining it. And it
further requires creators to go through a trade-o on investment
return between selective labor across platforms and practicing labor
as much as possible. In the case of creator ecology as presented by
our study, we start to see creator agency and platform power not
just as opposite forces but also a somewhat cooperative pair, where
creators strive to benet from the platformization process. We thus
call for more designs that can empower and support creators to
continue working across platforms.
ACKNOWLEDGMENTS
This work is partially supported by NSF grant no. 2006854. We ap-
preciate all anonymous reviewers’ constructive feedback to make
this work rened and improved. We also thank 21 cross-platform
content creators’ support and participation in this study. The rst
author thanks Dr. Patrick Doyle for the mentorship and internship
in SiriusXM and Pandora that allowed him to gain better under-
standing of digital streaming platforms and creator-fan relation-
ships.
REFERENCES
[1]
Crystal Abidin. 2016. “Aren’t These Just Young, Rich Women Doing Vain Things
Online?”: Inuencer Seles as Subversive Frivolity: Soc Media Soc 2, 2 (April
2016). DOI:https://doi.org/10.1177/2056305116641342
[2]
Judd Antin and Coye Cheshire. 2010. Readers are not free-riders: reading as a
form of participation on wikipedia. Proceedings of the 2010 ACM conference on
Computer supported cooperative work (2010), 127–130. DOI:https://doi.org/10.
1145/1718918.1718942
[3]
Carolina Are. 2021. The Shadowban Cycle: an autoethnography of pole dancing,
nudity and censorship on Instagram. Fem Media Stud (2021). DOI:https://doi.
org/10.1080/14680777.2021.1928259
[4]
Lauren Arnett, Robert Netzorg, Augustin Chaintreau, and Eugene Wu. 2019.
Cross-platform interactions and popularity in the live-streaming community.
Conference on Human Factors in Computing Systems - Proceedings (May 2019).
DOI:https://doi.org/10.1145/3290607.3312900
[5]
Jerey Bardzell, Shaowen Bardzell, and Austin Toombs. 2014. "now that’s
denitely a proper hack": self-made tools in hackerspaces. In Proceedings
of the 32nd annual ACM conference on Human factors in computing systems -
CHI ’14, ACM Press, New York, New York, USA, 473–476. DOI:https://doi.org/
10.1145/2556288.2557221
[6]
Nancy K. Baym. 2015. Connect With Your Audience! The Relational Labor of
Connection. The communication review 18, 1 (January 2015), 14–22. DOI:https:
//doi.org/10.1080/10714421.2015.996401
[7]
Nancy K. Baym. 2018. Playing to the Crowd. New York University Press (2018).
DOI:https://doi.org/10.18574/NYU/9781479896165.001.0001
[8]
Nancy K. Baym. 2021. Creator culture: An introduction to global social media
entertainment.
[9]
Yochai Benkler and Helen Nissenbaum. 2006. Commons-based Peer Production
and Virtue*. Journal of Political Philosophy 14, 4 (2006), 394–419. DOI:https:
//doi.org/10.1111/j.1467-9760.2006.00235.x
[10]
Rachel Berryman and Misha Kavka. 2017. ‘I Guess A Lot of People See Me as
a Big Sister or a Friend’: the role of intimacy in the celebrication of beauty
vloggers. J Gend Stud 26, 3 (May 2017), 307–320. DOI:https://doi.org/10.1080/
09589236.2017.1288611
[11]
Ivan Beschastnikh, Travis Kriplean, and David Mcdonald. 2008. Wikipedian
Self-Governance in Action: Motivating the Policy Lens. In In Proceedings of the
2008 AAAI International Conference on Weblogs and Social Media.
[12]
Sophie Bishop. 2019. Managing visibility on YouTube through algorithmic gossip.
New Media Soc 21, 11–12 (November 2019), 2589–2606. DOI:https://doi.org/10.
1177/1461444819854731
[13]
Sophie Bishop. 2020. Algorithmic Experts: Selling Algorithmic Lore on YouTube.
Soc Media Soc 6, 1 (2020), 205630511989732. DOI:https://doi.org/10.1177/
2056305119897323
[14]
Blue Meta. 2020. How Do Bidding Strategies Work for Social Media Ads.
Blue Meta Measurable Marketing. Retrieved January 24, 2023 from https://
wearebluemeta.com/how-do-bidding-strategies-work- for-social- media-ads/
[15]
Pablo J. Boczkowski, Mora Matassi, and Eugenia Mitchelstein. 2018. How Young
Users Deal With Multiple Platforms: The Role of Meaning-Making in Social Me-
dia Repertoires. Journal of Computer-Mediated Communication 23, 5 (September
2018), 245–259. DOI:https://doi.org/10.1093/JCMC/ZMY012
[16]
Ross Bonifacio, Lee Hair, and Donghee Yvette Wohn. 2021. Beyond fans: The
relational labor and communication practices of creators on Patreon. New Media
Soc (August 2021). DOI:https://doi.org/10.1177/14614448211027961
[17]
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychol-
ogy. Qual Res Psychol 3, 2 (January 2006), 77–101. DOI:https://doi.org/10.1191/
1478088706qp063oa
[18] Robin Brewer and Anne Marie Piper. 2016. “Tell It Like It Really Is”: A Case of
Online Content Creation and Sharing Among Older Adult Bloggers. Proceedings
of the 2016 CHI Conference on Human Factors in Computing Systems (2016),
5529–5542. DOI:https://doi.org/10.1145/2858036
[19]
Susan L. Bryant, Andrea Forte, and Amy Bruckman. 2005. Becoming Wikipedian:
Transformation of participation in a collaborative online encyclopedia. In Pro-
ceedings of the International ACM SIGGROUP Conference on Supporting Group
Work, 1–10. DOI:https://doi.org/10.1145/1099203.1099205
[20]
Brian Butler, Elisabeth Joyce, and Jacqueline Pike. 2008. Don’t look now, but
we’ve created a bureaucracy: The nature and roles of policies and rules in
Wikipedia. Conference on Human Factors in Computing Systems - Proceedings
(2008), 1101–1110. DOI:https://doi.org/10.1145/1357054.1357227
[21]
Robyn Caplan and Tarleton Gillespie. 2020. Tiered Governance and Demonetiza-
tion: The Shifting Terms of Labor and Compensation in the Platform Economy.
Soc Media Soc 6, 2 (2020). DOI:https://doi.org/10.1177/2056305120936636
[22]
Kyle Chayka. 2021. What the “Creator Economy” Promises—and
What It Actually Does. The New Yorker. Retrieved from https:
//www.newyorker.com/culture/innite-scroll/what-the-creator-economy-
promises-and- what-it-actually-does
[23]
Chih Ping Chen. 2013. Exploring Personal Branding on YouTube. Journal of
Internet Commerce 12, 4 (2013), 332–347. DOI:https://doi.org/10.1080/15332861.
2013.859041
[24]
Dasom Choi, Uichin Lee, and Hwajung Hong. 2022. “It’s not wrong, but I’m
quite disappointed”: Toward an Inclusive Algorithmic Experience for Content
Creators with Disabilities; “It’s not wrong, but I’m quite disappointed”: Toward
an Inclusive Algorithmic Experience for Content Creators with Disabilities.
CHI Conference on Human Factors in Computing Systems (2022). DOI:https:
//doi.org/10.1145/3491102
[25]
Nicole S. Cohen. 2015. From Pink Slips to Pink Slime: Transforming Media
Labor in a Digital Age. The Communication Review 18, 2 (April 2015), 98–122.
DOI:https://doi.org/10.1080/10714421.2015.1031996
[26]
Juliet M. Corbin and Anselm Strauss. 1990. Grounded theory research: Pro-
cedures, canons, and evaluative criteria. Qualitative Sociology 1990 13:1 13, 1
(March 1990), 3–21. DOI:https://doi.org/10.1007/BF00988593
[27]
David Craig and Stuart Cunningham. 2019. Social media entertainment: The new
intersection of Hollywood and Silicon Valley. NYU Press.
[28]
Stuart Cunningham and David Craig. 2019. Creator Governance in Social Media
Entertainment. Soc Media Soc 5, 4 (October 2019), 205630511988342. DOI:https:
//doi.org/10.1177/2056305119883428
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
[29]
Brittany I. Davidson and Adam N. Joinson. 2021. Shape Shifting Across So-
cial Media: Soc Media Soc 7, 1 (February 2021). DOI:https://doi.org/10.1177/
2056305121990632
[30]
Nicholas Davis, Holger Winnemöller, Mira Dontcheva, and Ellen Yi-Luen Do.
2013. Toward a cognitive theory of creativity support. In Proceedings of the 9th
ACM Conference on Creativity & Cognition - C&C ’13, ACM Press, New York,
New York, USA, 13–22. DOI:https://doi.org/10.1145/2466627.2466655
[31]
Michael Ann Devito, Ashley Marie Walker, and Jeremy Birnholtz. 2018. “Too
gay for Facebook”: Presenting LGBTQ+ identity throughout the personal social
media ecosystem. Proc ACM Hum Comput Interact 2, CSCW (November 2018),
44. DOI:https://doi.org/10.1145/3274313
[32]
Treyton Devore. 2021. How to Build a Sustainable Creator Business. Cre-
atorbread. Retrieved from https://www.creatorbread.com/blog/how-to-build- a-
sustainable-creator- business
[33] DistroKid. 2023. DistroKid. Retrieved from https://distrokid.com/
[34]
Niels van Doorn. 2017. Platform labor: on the gendered and racialized ex-
ploitation of low-income service work in the ‘on-demand’ economy. https:
//doi.org/10.1080/1369118X.2017.1294194 20, 6 (June 2017), 898–914. DOI:https:
//doi.org/10.1080/1369118X.2017.1294194
[35]
Steven Dow, Anand Kulkarni, Scott Klemmer, and Björn Hartmann. 2012. Shep-
herding the Crowd Yields Better Work. In Proceedings of the ACM 2012 Conference
on Computer Supported Cooperative Work (CSCW ’12), ACM, New York, NY,
USA, 1013–1022. DOI:https://doi.org/10.1145/2145204.2145355
[36]
Gary Drenik. 2022. The Creator Economy Is Booming. Here’s How Businesses
Can Tap Into Its Potential. Forbes. Retrieved from https://www.forbes.com/
sites/garydrenik/2022/08/23/the-creator- economy-is-booming-heres-how-
businesses-can- tap-into-its-potential/?sh=305e02713d27
[37]
Brooke Erin Duy. 2017. (Not) getting paid to do what you love: Gender, social
media, and aspirational work.
[38]
Brooke Erin Duy. 2020. Algorithmic precarity in cultural work: Communication
and the Public 5, 3–4 (September 2020), 103–107. DOI:https://doi.org/10.1177/
2057047320959855
[39]
Brooke Erin Duy, Annika Pinch, Shruti Sannon, and Megan Sawey. 2021. The
Nested Precarities of Creative Labor on Social Media: Soc Media Soc 7, 2 ( June
2021). DOI:https://doi.org/10.1177/20563051211021368
[40]
Brooke Erin Duy, Thomas Poell, and David B. Nieborg. 2019. Platform Practices
in the Cultural Industries: Creativity, Labor, and Citizenship: https://doi.org/
10.1177/2056305119879672 5, 4 (November 2019). DOI:https://doi.org/10.1177/
2056305119879672
[41]
Brooke Erin Duy, Urszula Pruchniewska, and Leah Scolere. 2017. Platform-
specic self-branding: Imagined aordances of the social media ecology. ACM
International Conference Proceeding Series Part F1296, (July 2017). DOI:https:
//doi.org/10.1145/3097286.3097291
[42]
Brooke Erin Duy and Megan Sawey. 2022. In/Visibility in Social Media Work:
The Hidden Labor Behind the Brands. Media Commun 10, 1 (January 2022),
77–87. DOI:https://doi.org/10.17645/MAC.V10I1.4460
[43]
Jared Duval, Ferran Altarriba Bertran, Siying Chen, Melissa Chu, Divya Subra-
monian, Austin Wang, Geofrey Xiang, Sri Kurniawan, and Katherine Isbister.
2021. Chasing play on tiktok from populations with disabilities to inspire playful
and inclusive technology design. Conference on Human Factors in Computing
Systems - Proceedings (May 2021). DOI:https://doi.org/10.1145/3411764.3445303
[44]
Nancy Ettlinger. 2018. Algorithmic aordances for productive resistance. Big
Data Soc 5, 1 (April 2018). DOI:https://doi.org/10.1177/2053951718771399
[45]
Ryan Faulkner, Steven Walling, and Maryana Pinchuk. 2012. Etiquette in
Wikipedia: weening new editors into productive ones. In WikiSym 2012 Con-
ference Proceedings - 8th Annual International Symposium on Wikis and Open
Collaboration (WikiSym ’12), ACM, New York, NY, USA, 5:1–5:4. DOI:https:
//doi.org/10.1145/2462932.2462939
[46]
Christian Fieseler, Eliane Bucher, and Christian Pieter Homann. 2019. Unfair-
ness by Design? The Perceived Fairness of Digital Labor on Crowdworking
Platforms. Journal of Business Ethics 156, 4 (June 2019), 987–1005. DOI:https:
//doi.org/10.1007/S10551-017- 3607-2/TABLES/3
[47]
Casey Fiesler, Jessica L. Feuston, and Amy S. Bruckman. 2015. Understanding
Copyright Law in Online Creative Communities. In Proceedings of the 18th ACM
Conference on Computer Supported Cooperative Work & Social Computing - CSCW
’15, ACM Press, New York, New York, USA, 116–129. DOI:https://doi.org/10.
1145/2675133.2675234
[48]
Adam Fish and Ramesh Srinivasan. 2011. Digital labor is the new killer app:
New Media Soc 14, 1 (October 2011), 137–152. DOI:https://doi.org/10.1177/
1461444811412159
[49]
Andrea Forte and Amy Bruckman. 2008. Scaling Consensus: Increasing Decen-
tralization in Wikipedia Governance. In (HICSS ’08), IEEE Computer Society,
Washington, DC, USA, 157–166. DOI:https://doi.org/10.1109/HICSS.2008.383
[50]
Andrea Forte, Vanesa Larco, and Amy Bruckman. 2009. Decentralization in
Wikipedia Governance. J. Manage. Inf. Syst. 26, 1 (July 2009), 49–72. DOI:https:
//doi.org/10.2753/MIS0742-1222260103
[51]
Guo Freeman and Donghee Yvette Wohn. 2020. Streaming your Identity:
Navigating the Presentation of Gender and Sexuality through Live Stream-
ing. Comput Support Coop Work 29, 6 (December 2020), 795–825. DOI:https:
//doi.org/10.1007/S10606-020- 09386-W/TABLES/1
[52]
Jonas Frich, Lindsay Macdonald Vermeulen, Christian Remy, Michael Mose
Biskjaer, and Peter Dalsgaard. 2019. Mapping the Landscape of Creativity Sup-
port Tools in HCI. Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems 18, (2019). DOI:https://doi.org/10.1145/3290605
[53]
Alessandro Gandini. 2016. Digital work: Self-branding and social capital in the
freelance knowledge economy. Marketing Theory 16, 1 (March 2016), 123–141.
DOI:https://doi.org/10.1177/1470593115607942
[54]
R. Stuart Geiger and Heather Ford. 2011. Participation in Wikipedia’s arti-
cle deletion processes. In (WikiSym ’11), ACM, New York, NY, USA, 201–202.
DOI:https://doi.org/10.1145/2038558.2038593
[55]
Werner Geyser. 2022. The State of the Creator Economy | Denition, Growth &
Market Size. Inuencer Marketing Hub.
[56]
Zoe Glatt. 2022. We’re all told not to put our eggs in one basket: uncertainty,
precarity and cross-platform labor in the online video inuencer industry. Int J
Commun (January 2022).
[57]
Erving Goman. 1959. Impression management: The presentation of self in
everyday life. Teacher 21, 5 (1959), 259. DOI:https://doi.org/10.2307/2089106
[58]
Auguest E. Grant, K. KENDALL GUTHRIE, and SANDRA J. BALL-ROKEACH.
1991. Television Shopping. Communic Res 18, 6 (December 1991), 773–798.
DOI:https://doi.org/10.1177/009365091018006004
[59]
Lee Hair. 2021. Friends, not ATMs: parasocial relational work and the construc-
tion of intimacy by artists on Patreon. Sociological Spectrum 41, 2 (2021), 196–212.
DOI:https://doi.org/10.1080/02732173.2021.1875090
[60]
Lee Hair, Ross Bonifacio, and Donghee Yvette Wohn. 2022. Multi-platform
practices among digital patronage creators. Convergence: The International Jour-
nal of Research into New Media Technologies 0, 0 (May 2022), 1–19. DOI:https:
//doi.org/10.1177/13548565221104990
[61]
Aaron Halfaker, Aniket Kittur, and John Riedl. 2011. Don’T Bite the Newbies:
How Reverts Aect the Quantity and Quality of Wikipedia Work. In (WikiSym
’11), ACM, New York, NY, USA, 163–172. DOI:https://doi.org/10.1145/2038558.
2038585
[62]
Chang Dae Ham, Joonghwa Lee, Jameson L. Hayes, and Young Han Bae. 2018.
Exploring sharing behaviors across social media platforms: International Jour-
nal of Market Research 61, 2 (June 2018), 157–177. DOI:https://doi.org/10.1177/
1470785318782790
[63]
Alison Hearn. 2008. ‘Meat, Mask, Burden‘: Probing the contours of the branded
‘self‘. Journal of Consumer Culture 8, 2 (July 2008), 197–217. DOI:https://doi.org/
10.1177/1469540508090086
[64]
Alison Hearn. 2011. Confessions of a Radical Eclectic: Reality Television,
Self-Branding, Social Media, and Autonomist Marxism. Journal of Commu-
nication Inquiry 35, 4 (August 2011), 313–321. DOI:https://doi.org/10.1177/
0196859911417438
[65]
Oliver Ibert, Anna Oechslen, Alica Repenning, and Suntje Schmidt. 2022. Plat-
form ecology: A user-centric and relational conceptualization of online plat-
forms. Global Networks 22, 3 (July 2022), 564–579. DOI:https://doi.org/10.1111/
GLOB.12355
[66]
Indeed Editorial Team. 2022. 15 Ways To Make Money as a Content Creator.
Indeed. Retrieved from https://www.indeed.com/career-advice/pay-salary/how-
to-make- money-as-content-creator
[67]
Henry Jenkins. 1992. Textual Poachers: Television Fans and Participatory Culture.
Routledge.
[68]
Shagun Jhaver, Darren Scott Appling, Eric Gilbert, and Amy Bruckman. 2019.
“Did you suspect the post would be removed?”: Understanding user reactions to
content removals on reddit. Proc ACM Hum Comput Interact 3, CSCW (November
2019), 1–33. DOI:https://doi.org/10.1145/3359294
[69]
Naveena Karusala, Apoorva Bhalla, and Neha Kumar. Privacy, Patriarchy, and
Participation on Social Media. Proceedings of the 2019 on Designing Interactive
Systems Conference. DOI:https://doi.org/10.1145/3322276
[70]
Brian Keegan and Darren Gergle. 2010. Egalitarians at the gate: one-sided gate-
keeping practices in social media. In Proceedings of the 2010 ACM conference on
Computer supported cooperative work - CSCW ’10 (CSCW ’10), ACM Press, New
York, New York, USA, 131–134. DOI:https://doi.org/10.1145/1718918.1718943
[71]
Erin Klawitter and Eszter Hargittai. 2018. “It’s like learning a whole other
language”: The role of algorithmic skills in the curation of creative goods. Int J
Commun 12, (2018), 3490–3510. DOI:https://doi.org/10.5167/UZH-168021
[72]
Christian Koch, Moritz Lode, Denny Stohr, Amr Rizk, and Ralf Steinmetz. 2018.
Collaborations on YouTube: From Unsupervised Detection to the Impact on
Video and Channel Popularity. ACM Transactions on Multimedia Computing,
Communications, and Applications (TOMM) 14, 4 (October 2018). DOI:https:
//doi.org/10.1145/3241054
[73]
Susanne Kopf. 2020. “Rewarding Good Creators”: Corporate Social Media Dis-
course on Monetization Schemes for Content Creators. Soc Media Soc 6, 4 (Oc-
tober 2020), 205630512096987. DOI:https://doi.org/10.1177/2056305120969877
[74]
Susanne Kopf. 2022. Corporate censorship online: Vagueness and discursive
imprecision in YouTube’s advertiser-friendly content guidelines. New Media
Soc (February 2022). DOI:https://doi.org/10.1177/14614448221077354/ASSET/
CHI ’23, April 23–28, 2023, Hamburg, Germany Renkai Ma et al.
IMAGES/LARGE/10.1177_14614448221077354-FIG1.JPEG
[75]
Harper Lambert. 2022. 5 Ways TikTok Stars Are Reshaping the Creator Econ-
omy. Yahoo! Retrieved from https://www.yahoo.com/video/5-ways- tiktok-stars-
reshaping-130000261.html
[76]
Andrew M. Ledbetter and Colten Meisner. 2021. Extending the personal branding
aordances typology to parasocial interaction with public gures on social
media: Social presence and media multiplexity as mediators. Comput Human
Behav 115, (February 2021), 106610. DOI:https://doi.org/10.1016/J.CHB.2020.
106610
[77]
Caitlin Lustig, Sean Rintel, Liane Scult, and Siddharth Suri. 2020. Stuck in
the middle with you: The Transaction Costs of Corporate Employees Hiring
Freelancers. Proc ACM Hum Comput Interact 4, CSCW1 (May 2020). DOI:https:
//doi.org/10.1145/3392842
[78]
Renkai Ma and Yubo Kou. 2021. “How advertiser-friendly is my video?”:
YouTuber’s Socioeconomic Interactions with Algorithmic Content Modera-
tion. PACM on Human Computer Interaction 5, CSCW2 (2021), 1–26. DOI:https:
//doi.org/10.1145/3479573
[79]
Renkai Ma and Yubo Kou. 2022. “I’m not sure what dierence is between their
content and mine, other than the person itself”: A Study of Fairness Perception
of Content Moderation on YouTube. Proc ACM Hum Comput Interact 6, CSCW2
(2022), 28. DOI:https://doi.org/10.1145/3555150
[80]
Keri Mallari, Spencer Williams, and Gary Hsieh. 2021. Understanding analytics
needs of video game streamers. Conference on Human Factors in Computing Sys-
tems - Proceedings (May 2021), 12. DOI:https://doi.org/10.1145/3411764.3445320
[81]
Alice E. Marwick. 2013. Status Update: Celebrity, Publicity, and Branding in the
Social Media Age.
[82]
Sarah Mcroberts, Ye Yuan, Kathleen Watson, and Svetlana Yarosh. Behind the
Scenes: Design, Collaboration, and Video Creation with Youth. Proc 18th ACM
Int Conf Interact Des Child (2019) 19,. DOI:https://doi.org/10.1145/3311927
[83]
Colten Meisner and Andrew M. Ledbetter. 2020. Participatory branding on social
media: The aordances of live streaming for creative labor: New Media Soc 24, 5
(November 2020), 1179–1195. DOI:https://doi.org/10.1177/1461444820972392
[84]
Meta. 2021. Launching Reels on Facebook in the US. Meta. Retrieved from
https://about.fb.com/news/2021/09/launching-reels- on-facebook- us/
[85]
Meta. 2023. Partner Monetization Policies. Meta Business Help Center. Re-
trieved from https://www.facebook.com/business/help/169845596919485?id=
2520940424820218
[86]
Meta. 2023. Instagram Partner Monetization Policies. Instagram Help Center.
Retrieved from https://www.facebook.com/help/instagram/512371932629820?
helpref=faq_content
[87]
Teresa K. Naab and Annika Sehl. 2016. Studies of user-generated content: A
systematic review: https://doi.org/10.1177/1464884916673557 18, 10 (October
2016), 1256–1273. DOI:https://doi.org/10.1177/1464884916673557
[88]
Peter Nagy and Gina Ne. 2015. Imagined Aordance: Reconstructing a Keyword
for Communication Theory: Soc Media Soc 1, 2 (September 2015). DOI:https:
//doi.org/10.1177/2056305115603385
[89]
Donald A. Norman. 1999. Aordance, conventions, and design. Interactions (May
1999), 469–473. DOI:https://doi.org/10.1145/301153.301168
[90]
Fayika Farhat Nova, Michael Ann Devito, Pratyasha Saha, Kazi Shohanur Rashid,
Shashwata Roy Turzo, Sadia Afrin, and Shion Guha. 2021. “Facebook Promotes
More Harassment”: Social Media Ecosystem, Skill and Marginalized Hijra Iden-
tity in Bangladesh. Proc ACM Hum Comput Interact 5, CSCW1 (April 2021), 157.
DOI:https://doi.org/10.1145/3449231
[91]
Caitlin Petre, Brooke Erin Duy, and Emily Hund. 2019. “Gaming the Sys-
tem”: Platform Paternalism and the Politics of Algorithmic Visibility. Soc Me-
dia Soc 5, 4 (October 2019), 205630511987999. DOI:https://doi.org/10.1177/
2056305119879995
[92]
Thomas Poell, David B. Nieborg, and Brooke Erin Duy. 2021. Platforms and
Cultural Production. John Wiley & Sons.
[93]
Hector Postigo. 2016. The socio-technical architecture of digital labor: Convert-
ing play into YouTube money. New Media Soc 18, 2 (2016), 332–349. DOI:https:
//doi.org/10.1177/1461444814541527
[94]
Tobias Raun. 2018. Capitalizing intimacy: New subcultural forms of micro-
celebrity strategies and aective labour on YouTube. Convergence 24, 1 (January
2018), 99–113. DOI:https://doi.org/10.1177/1354856517736983
[95]
Alexander Rihl and Claudia Wegener. 2017. YouTube celebrities and parasocial
interaction: Using feedback channels in mediatized relationships. Convergence:
The International Journal of Research into New Media Technologies 25, 3 (October
2017), 554–566. DOI:https://doi.org/10.1177/1354856517736976
[96]
Jane Ritchie, Jane Lewis, Carol McNaughton Nicholls, and Rachel Ormston. 2013.
Qualitative Research Practice. A Guide for Social Science Students and Researchers.
SAGE Publishing.
[97]
Carla Rover.2022. Mapping The Trends: From Inuencers to Inuence Marketing.
a.list.
[98]
Shruti Sannon, Elizabeth L. Murnane, Natalya N. Bazarova, and Geri Gay. 2019.
“I was really, really nervous posting it”: Communicating about invisible chronic
illnesses across social media platforms. Conference on Human Factors in Comput-
ing Systems - Proceedings (May 2019), 13. DOI:https://doi.org/10.1145/3290605.
3300583
[99]
Lana el Sanyoura and Ashton Anderson. 2022. Quantifying the Creator Econ-
omy: A Large-Scale Analysis of Patreon. Proceedings of the International AAAI
Conference on Web and Social Media 16, (May 2022), 829–840. Retrieved from
https://ojs.aaai.org/index.php/ICWSM/article/view/19338
[100]
Ryan Schram. 2020. The state of the creator economy: Ingenta Connect. Journal
of Brand Strategy 9, 2 (2020), 152–162.
[101]
Leah Scolere, Urszula Pruchniewska, and Brooke Erin Duy. 2018. Constructing
the Platform-Specic Self-Brand: The Labor of Social Media Promotion. Soc
Media Soc 4, 3 (July 2018). DOI:https://doi.org/10.1177/2056305118784768
[102]
Burr Settles and Steven Dow. 2013. Let’s get together: the formation and success
of online creative collaborations. In Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems - CHI ’13, ACM Press, New York, New
York, USA, 2009–2018. DOI:https://doi.org/10.1145/2470654.2466266
[103]
Manya Sleeper, William Melicher, Hana Habib, Lujo Bauer, Lorrie Faith Cranor,
and Michelle L. Mazurek. 2016. Sharing personal content online: Exploring
channel choice and multi-channel behaviors. Conference on Human Factors in
Computing Systems - Proceedings (May 2016), 101–112. DOI:https://doi.org/10.
1145/2858036.2858170
[104]
İrem Şot. 2022. Fostering intimacy on TikTok: a platform that ‘listens’ and
‘creates a safe space’: Media Cult Soc (June 2022). DOI:https://doi.org/10.1177/
01634437221104709
[105]
Matt G. Southern. 2021. YouTube Introduces Clips For Live Streams. Search
Engine Journal. Retrieved from https://www.searchenginejournal.com/youtube-
introduces-clips- for-live-streams/394053/#close
[106]
Steve. 2021. Twitch Aliate vs Partner: Benets & Earnings Compared. Launch
Kit. Retrieved from https://launchkit.io/twitch-aliate-vs-partner/
[107]
Edson C. Tandoc, Chen Lou, and Velyn Lee Hui Min. 2019. Platform-swinging
in a poly-social-media context: How and why users navigate multiple social
media platforms. Journal of Computer-Mediated Communication 24, 1 (January
2019), 21–35. DOI:https://doi.org/10.1093/JCMC/ZMY022
[108]
Tom Taulli. 2020. TikTok: Why The Enormous Success? Forbes. Re-
trieved from https://www.forbes.com/sites/tomtaulli/2020/01/31/tiktok-why-
the-enormous- success/?sh=1acde42565d1
[109]
Nick Taylor, Ursula Hurley, and Philip Connolly. 2016. Making Community:
The Wider Role of Makerspaces in Public Life. In Proceedings of the 2016 CHI
Conference on Human Factors in Computing Systems - CHI ’16, ACM Press, New
York, New York, USA, 1415–1425. DOI:https://doi.org/10.1145/2858036.2858073
[110]
Kurt Thomas, Patrick Gage, Kelley Sunny Consolvo, Patrawat Samermit, and
Elie Bursztein. 2022. “It’s common and a part of being a content creator”’:
Understanding How Creators Experience and Cope with Hate and Harass-
ment Online.” CHI Conference on Human Factors in Computing Systems (2022).
DOI:https://doi.org/10.1145/3491102
[111]
TikTok. 2021. TikTok Creator Fund: Your questions answered. TikTok. Retrieved
from https://newsroom.tiktok.com/en-gb/tiktok-creator-fund-your- questions-
answered
[112]
TikTok. 2023. Understanding your analytics. Retrieved from
https://www.tiktok.com/creators/creator-portal/en-us/tiktok- content-
strategy/understanding-your- analytics/
[113]
Sarah J. Tracy. 2013. Qualitative Research Methods: Collecting Evidence, Crafting
Analysis.
[114]
Emiliano Treré and Alice Mattoni. 2015. Media ecologies and protest movements:
main perspectives and key lessons. Inf Commun Soc 19, 3 (March 2015), 290–306.
DOI:https://doi.org/10.1080/1369118X.2015.1109699
[115]
Twitch.tv. 2023. Creator Dashboard. Retrieved from https://help.twitch.tv/s/
article/creator-dashboard?language=en_US
[116]
Twtich.tv. 2023. How to Use Clips. Retrieved January 24, 2023 from https://help.
twitch.tv/s/article/how-to- use-clips?language=en_US
[117]
Jirassaya Uttarapong and Rae Jereza. 2022. Social Support in Digital Patronage:
OnlyFans Adult Content Creators as an Online Community; Social Support in
Digital Patronage: OnlyFans Adult Content Creators as an Online Community.
CHI Conference on Human Factors in Computing Systems Extended Abstracts
(2022). DOI:https://doi.org/10.1145/3491101
[118]
Jessica Vitak and Jinyoung Kim. 2014. “Youcan’t block p eople oine”:examining
how facebook’s aordances shape the disclosure process. In Proceedings of the
17th ACM conference on Computer supported cooperative work & social computing
- CSCW ’14, ACM Press, New York, New York, USA, 461–474. DOI:https://doi.
org/10.1145/2531602.2531672
[119]
Dennis Wang, Yi Chieh Lee, and Wai Tat Fu. 2019. “I Love the Feeling of Being
on Stage, but I Become Greedy.” Proc ACM Hum Comput Interact 3, CSCW
(November 2019), 92. DOI:https://doi.org/10.1145/3359194
[120]
Philip Weber, Sabrina Brodesser, Thomas Ludwig, and Laura Grönewald. 2021.
“It’s a Kind of Art!”’: Understanding Food Inuencers as Inuential Content
Creators.” Proceedings of the 2021 CHI Conference on Human Factors in Computing
Systems (2021). DOI:https://doi.org/10.1145/3411764
[121]
Howard T. Welser, Dan Cosley, Gueorgi Kossinets, Austin Lin, Fedor Dokshin,
Geri Gay, and Marc Smith. 2011. Finding social roles in Wikipedia. In Proceedings
of the 2011 iConference on - iConference ’11, ACM Press, New York, New York,
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content
Synchronization, and Audience Management CHI ’23, April 23–28, 2023, Hamburg, Germany
USA, 122–129. DOI:https://doi.org/10.1145/1940761.1940778
[122]
Donghee Yvette Wohn and Guo Freeman. 2020. Audience Management Practices
of Live Streamers on Twitch. IMX 2020 - Proceedings of the 2020 ACMInternational
Conference on Interactive Media Experiences (June 2020), 106–116. DOI:https:
//doi.org/10.1145/3391614.3393653
[123]
Donghee Yvette Wohn, Guo Freeman, and Caitlin McLaughlin. 2018. Explain-
ing viewers’ emotional, instrumental, and nancial support provision for live
streamers. Proceedings of the 2018 CHI Conference on Human Factors in Computing
Systems 2018-April, (2018), 1–13. DOI:https://doi.org/10.1145/3173574.3174048
[124]
Donghee Yvette Wohn, Peter Jough, Peter Eskander, John Scott Siri, Masaho
Shimobayashi, and Pradnya Desai. 2019. Understanding digital patronage: Why
do people subscribe to streamers on twitch? CHI PLAY 2019 - Proceedings of
the Annual Symposium on Computer-Human Interaction in Play (October 2019),
99–110. DOI:https://doi.org/10.1145/3311350.3347160
[125]
Jamie Woodcock and Mark R Johnson. 2019. The Aective Labor and Perfor-
mance of Live Streaming on Twitch.tv. Television & New Media 20, 8 (2019),
813–823. DOI:https://doi.org/10.1177/1527476419851077
[126]
Eva Yiwei Wu, Emily Pedersen, and Niloufar Salehi. 2019. Agent, gatekeeper,
drug dealer: How content creators craft algorithmic personas. Proc ACM Hum
Comput Interact 3, CSCW (2019), 1–27. DOI:https://doi.org/10.1145/3359321
[127]
Anbang Xu and Brian Bailey. 2012. What do you think?: a case study of benet,
expectation, and interaction in a large online critique community. In Proceedings
of the ACM 2012 conference on Computer Supported Cooperative Work - CSCW
’12, ACM Press, New York, New York, USA, 295–304. DOI:https://doi.org/10.
1145/2145204.2145252
[128]
YouTube. 2023. How YouTube Makes Money. Retrieved January 24, 2023
from https://www.youtube.com/howyoutubeworks/our-commitments/sharing-
revenue/
[129]
YouTube. 2023. YouTube Partner Program overview & eligibility. YouTube Help.
Retrieved from https://support.google.com/youtube/answer/72851?hl=en
[130]
YouTube. 2023. Manage mid-roll ad breaks in long videos. YouTube Help.
Retrieved from https://support.google.com/youtube/answer/6175006?hl=en#
zippy=%2Cfrequently-asked- questions
[131]
YouTube. 2023. Navigate YouTube Studio. YouTube Help. Retrieved from https:
//support.google.com/youtube/answer/7548152?hl=en
[132]
Jing Zeng and D. Bondy Valdovinos Kaye. 2022. From content moderation to
visibility moderation: A case study of platform governance on TikTok. Policy
Internet 14, 1 (March 2022), 79–95. DOI:https://doi.org/10.1002/POI3.287
[133]
Xuan Zhao, Cli Lampe, and Nicole B. Ellison. 2016. The social media ecology:
User perceptions, strategies and challenges. Conference on Human Factors in
Computing Systems - Proceedings (May 2016), 89–100. DOI:https://doi.org/10.
1145/2858036.2858333
[134]
Changtao Zhong, Hau-wen Chang, Dmytro Karamshuk, Dongwon Lee, and
Nishanth Sastry. 2017. Wearing Many (Social) Hats: How Dierent Are Your
Dierent Social Network Personae? Eleventh International AAAI Conference on
Web and Social Media (May 2017).
