"I'm not sure what difference is between their content and mine, other than the person itself": A Study of Fairness Perception of Content Moderation on YouTube

Abstract

How social media platforms could fairly conduct content moderation is gaining attention from society at large. Researchers from HCI and CSCW have investigated whether certain factors could affect how users perceive moderation decisions as fair or unfair. However, little attention has been paid to unpacking or elaborating on the formation processes of users’ perceived (un)fairness from their moderation experiences, especially users who monetize their content. By interviewing 21 for-profit YouTubers (i.e., video content creators), we found three primary ways through which participants assess moderation fairness, including equality across their peers, consistency across moderation decisions and policies, and their voice in algorithmic visibility decision-making processes. Building upon the findings, we discuss how our participants’ fairness perceptions demonstrate a multi-dimensional notion of moderation fairness and how YouTube implements an algorithmic assemblage to moderate YouTubers. We derive translatable design considerations for a fairer moderation system on platforms affording creator monetization.

Full text

PACM on Human-Computer Interaction, Vol. 6, No. CSCW2, Article 425, Publication date: November 2022.
“I’m not sure what difference is between their content
and mine, other than the person itself”: A Study of Fairness
Perception of Content Moderation on YouTube
RENKAI MA, Pennsylvania State University, USA
YUBO KOU, Pennsylvania State University, USA
1
How social media platforms could fairly conduct content moderation is gaining attention from society at
large. Researchers from HCI and CSCW have investigated whether certain factors could affect how users
perceive moderation decisions as fair or unfair. However, little attention has been paid to unpacking or
elaborating on the formation processes of users’ perceived (un)fairness from their moderation experiences,
especially users who monetize their content. By interviewing 21 for-profit YouTubers (i.e., video content
creators), we found three primary ways through which participants assess moderation fairness, including
equality across their peers, consistency across moderation decisions and policies, and their voice in
algorithmic visibility decision-making processes. Building upon the findings, we discuss how our participants’
fairness perceptions demonstrate a multi-dimensional notion of moderation fairness and how YouTube
implements an algorithmic assemblage to moderate YouTubers. We derive translatable design considerations
for a fairer moderation system on platforms affording creator monetization.
CCS Concepts: • Human-centered computing → Collaborative and social computing → Empirical
studies in collaborative and social computing
KEYWORDS: content moderation; creator moderation; fairness perception; algorithmic moderation;
moderation experience; moderation fairness; YouTuber
ACM Reference format:
Renkai Ma and Yubo Kou. 2022. “I’m not sure what difference is between their content and mine, other than
the person itself”: A Study of Fairness Perception of Content Moderation on YouTube. In PACM on Human
Computer Interaction, Vol. 6, CSCW, Article 425, November 2022. ACM, New York, NY, USA. 28 pages.
https://doi.org/10.1145/3555150
1 INTRODUCTION
In a creator economy where numerous content creators make a living on social media platforms
such as YouTube and Twitch [13,47,80], the fairness of content moderation decisions has
significant and growing impacts on their livelihoods. Video content creators claimed that
Facebook cut their thousands of dollars compared with the typical advertising income they earned
This work is partially supported by the National Science Foundation, under grant no. 2006854.
Author’s addresses: Renkai Ma (renkai@psu.edu) and Yubo Kou (yubokou@psu.edu), College of Information Sciences
and Technology, The Pennsylvania State University, University Park, PA, 16802, USA
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https://doi.org/10.1145/3555150
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from videos [22]; creators from marginalized groups complained their ad income was unfairly
reduced on YouTube compared with others [3,5,70], and YouTube overturned over 2.2 million
erroneous copyright claims that had already shared creators’ ad income with copyright owners
[77]. This thread of news also shows how content moderation could be fairly conducted on social
media platforms is gaining attention from society at large.
Recent research from HCI and CSCW has investigated end-users’ fairness perceptions after
they experience content moderation, especially on whether and how certain factors could affect
such perceptions. For example, Jhaver et al. discovered that users who received explanations of
content removal tended to perceive moderation as fair on Reddit [43]. Vaccaro et al. tested how
users experienced different appeal explanations written by either algorithms or humans on
Facebook and found that their perceived fairness increased after receiving all types of explanations
[92]. However, what is relatively less discussed in the literature is the formation processes of
fairness perception: how users develop their perceived (un)fairness from experiences with content
moderation. In this study, we argue fairness perception means that users invoke the notion of
fairness to describe their experiences with not only the decisions they receive but also decision-
making processes they experience [29,58,91].
Thus, we draw from the interpretive lens of procedural justice/fairness to investigate how users
develop their fairness perceptions. Procedural justice refers to a decision-making process
considering voice comprehensively and allocating resources [37]. Individuals’ fairness perceptions
are impacted by the quality of their experience with decision-making processes and not only with
outcomes from such processes [91,103]. Specifically, individuals’ social contexts (e.g., time, people)
[60] help them compare the consistency of decision-making processes. And the extent of their
representation (i.e., voice) in decision-making [61] could affect their fairness perceptions. This
paper aims to situate this interpretive lens in content moderation on YouTube to analyze and
unpack YouTubers’ fairness perceptions on moderation. We ask:
How do YouTubers’ fairness perceptions generate from moderation experiences?
To answer this question, we interviewed 21 YouTubers who monetized their video content
2
and
had experiences with YouTube moderation. Through an inductive qualitative analysis [55,90], we
found three primary ways through which participants assessed moderation fairness, including
their voice in algorithmic visibility decision-making processes, equality across their peers, and
consistency across moderation decisions and policies. Thus, moderation fairness is best viewed as
a multi-dimensional notion. That is, our participants developed fairness perceptions from
temporal, social, and technical dimensions of moderation.
The focus on fairness perception and the findings about how participants experienced the
complex, bureaucratic procedures behind an unequivocal moderation decision allow us to observe
how content moderation on YouTube manifests not as a single class of algorithms, but an
algorithmic assemblage. Algorithmic assemblage refers to a mixed “infrastructure that supports
implementation, maintenance, use, and evolution of algorithms, data, and platforms” [74]. On
YouTube, algorithms of different purposes such as copyright, visibility, and monetization work
together to moderate our YouTuber participants. Thus, participants who experienced moderation
could feel impacts from multiple sources, which tend to have ripple effects that are multiple and
2
Those YouTubers signed YouTube Partner program (YPP, https://support.google.com/youtube/answer/72851?hl=en) to
be eligible to acquire ad income. So, they accordingly might encounter a series of content moderation that amateur
YouTubers or viewers would not meet, such as demonetization [3,6]. In this paper, we refer to YouTubers as those
creators who joined the YPP.

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overlapping on their future content creation and monetization endeavors. The algorithmic
assemblage, in turn, renders source of accountability less traceable should an unfairness issue
arise, as well as the issue of moderation fairness a multi-dimensional notion.
This study contributes to the HCI and CSCW literature with multi-fold insights: (1) to our
knowledge, this study is among the first to contribute a nuanced account of for-profit content
creators’ interactions with content moderation; (2) we reveal how the assemblage of various
classes of algorithms serve platform governance purposes; (3) we offer a conceptual understanding
of how a notion of multi-dimensional moderation fairness develops from moderation experiences;
(4) we articulate transferable design considerations for fair moderation systems on platforms
affording creator monetization; (5) we highlight for future study: beyond understanding user
perspectives on moderation outcomes, we should not ignore whether and how users have
different qualities of experiences in moderation processes.
2 BACKGROUND: Monetization and Algorithmic Content Moderation on YouTube
Our study site is YouTube, the largest video sharing platform and a place for video content
creators to perform profitable content creation. This process of converting video creation to profits
refers to “monetization.” YouTube offers YouTube Studio dashboard [104], as shown in Figure 1
(left), for YouTubers to understand their channel/content analytics (e.g., monetization statues,
viewer engagement rates). As a primary source for monetization, ad income is calculated from cost
per 1,000 impressions (CPM) (i.e., the unit income per 1000 views), multiplying by the proportion
of a video’s total views to 1,000 [105]. So, viewership matters to creators’ ad income. Along with
viewership, according to YouTube’s policies [106], viewers’ engagement is also critical to
YouTubers’ ad income. That is because more viewer engagement might allow a video to become
“viral” through YouTube’s recommendation algorithms [107]. Thus, more viewer engagement
could generate more profits.
YouTube Studio dashboard presents whether certain moderation happens. For example, when a
YouTuber clicks the Content tab in Figure 1 (left), they can observe all the videos’ analytics in a
catalog style. Then, YouTubers might further find a specific video is imposed with ‘limited ads’
(i.e., yellow dollar symbol), as shown in Figure 1 (right). Thus, their future ad income of such
videos will decrease, namely rendering demonetization punishments [2,21,62].
Fig. 1. YouTube Studio dashboard (left) and notification of ‘limited ads’ moderation decision (right).
YouTube largely utilizes algorithms to conduct video removal or account suspension [35] and
moderation decisions that are less severe than these are also majorly adjudicated by moderation
algorithms. For example, journalists and researchers reported that YouTube applies machine

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learning (ML) algorithms to detect copyright infringement and issue ad suitability (e.g., ‘limited
ads’) decisions [108] based on the metadata of videos (e.g., titles, thumbnails, descriptions,
captions, etc.) [3,36,73]. YouTube also uses automatic tools to hide videos that it deems as
potentially mature under ‘restricted mode’ [109]. However, only when YouTube provides
opportunities for “request review,” as shown in Figure 1 (right), or appeal [110] can YouTubers
initiate appeals to reverse potential false-positive moderation decisions. Hence, human reviewers
on YouTube take important roles in auditing and reconciling platform-wide decisions made by
moderation algorithms. YouTube’s algorithmic moderation here provides a nuanced scenario for
understanding how users interact with an algorithmic moderation system.
3 RELATED WORK
We introduce notions of justice/fairness and how researchers contextualize the notions in
different settings. As fairness perception plays a part in social media users’ interactions with
moderation systems, we situate our work in past literature discussing algorithmic content
moderation and fairness in moderation.
3.1 Procedural Justice and Fairness Perception
Given algorithms’ growing role in decision-making processes in many aspects of our society, such
as hiring and insurance, researchers have expressed their reflections on algorithmic fairness
[15,49,56,96]. The key value underlying this line of work aligns with the notion of fairness.
Fairness is defined on different ontological bases. Some define algorithmic fairness as biases
that coexist within the mathematical models [23,32]. To narrow the gap between true values and
algorithms’ expected values [65], researchers expand the model’s training process to involve as
many outliers as possible. Researchers have strived to reach proportionate classification
performance by involving various groups’ factors such as genders, race, education level, or
computational literacy [15,38,96,98] in facial recognition algorithms [1,67], job hiring procedures
[25,63], and criminal justice systems (e.g., risk score [14,51]). Growing efforts have also been made
to improve algorithms’ mathematic models by involving the notion of fairness into classifiers’
formulation [27,101]. Outside of academia, ML practitioners further focus on collecting
comprehensive datasets for model training and avoiding biased manual labeling
3
to ensure
algorithmic fairness [41]. However, focusing on purely mathematical formulation is not the sole
way to define algorithmic fairness [95]. A “fair” algorithm developed in a fair mathematical and
economic setting could be perceived as unfair by end-users from different social contexts [57].
Furthermore, people’s perceptions of fairness and trustworthiness on algorithms could be affected
by algorithmic accuracy [14], and people might judge human decision-making as fairer than
algorithmic one [56]. Thus, there is no absolute definition of algorithmic fairness to align with
existing mathematical definitions [15,94].
Moving beyond the technical and mathematical definitions of fairness, HCI and CSCW
researchers’ investigations of fairness have engaged with concepts and theories from philosophy
and social sciences. One of the most famous notions is John Rawls’ Justice as Fairness, where he
maintains that all individuals ought to be guaranteed equal rights of liberty and thus achieve
3
In the algorithmic model’s training process, there is no ground truth to identify if specific historical decisions or
manual labels offered by crowdsourcing is right and unbiased.

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different positions [31,71]. At the same time, social organizations need to contextualize individual
differences to allocate resources to secure the value of equity [64].
This justice notion stresses outcome equality and resonates well with procedural justice.
Procedural justice focuses on a fair decision-making process considering voice comprehensively
and allocating resources [37]. People’s fairness perception is impacted by the quality of their
experience in decision-making processes and not only the results of the processes [91,103]. In
detail, when people commit wrong acts by violating policies or laws, the authority in the juridical
system should allow these violators to voice their side of stories and then makes a neutral
judgment [60]. If people could express their voice before decisions are made, it could enhance the
possibility of reaching the deemed equitable decisions [89]. Also, when people believe that their
voice is included in decision-making processes, they tend to believe that they have a say in the
outcomes they would experience later, thus increasing perceived fairness [61]. Without voice or
control in decision-making processes, people may develop different fairness perceptions when
comparing their expectations of outcomes, others’ experiences, and what they think others
experience with their own [60]. This reflects a key criterion in procedural justice: consistency,
meaning “similarity of treatment and outcomes across people or time or both” [91]. Thus, voice
and consistency constitute the major procedural justice notions.
Fairness perception is conditioned upon various social contexts [84]. Education, for example,
could be a significant predictor to understand fairness perceptions of algorithms [76]. Wang et al.
[96] found that users with lower education levels tended to perceive favored outcomes as fairer
than users with higher education levels. Van Berkel et al. [15] added that users having higher
algorithmic literacy, especially the females with higher education levels, tended to perceive
algorithms as less fair. Beyond education, Lee et al. [59] suggested attention to resource allocation
because when people receive more efficient resource distribution, they might perceive algorithms
as fairer. Especially when factors such as race and gender [49] or loan repayment rates in a loan
approval system, have been involved in algorithms [78], users might perceive acquiring resources
proportionally as fairer. Besides, researchers have also pointed out that algorithms’ clarity or
transparency (e.g., outcome explanations) is a crucial condition in helping users recognize the
fairness of decision-making processes [58]. This line of diverse work presents the importance of
contextualizing fairness in specific social contexts and recognizing its plurality [57].
3.2 Algorithmic Moderation and Fairness in Content Moderation
Early day content moderation relied upon manual intervention to deal with identified deviant
behaviors [19,52,53]. Manual or human moderation plays a role in “structuring participation in a
community to facilitate cooperation and prevent abuse” [39]. However, manual moderation is
time-consuming and costly [39,82]. Reviewing a sheering volume of potentially harmful content
would also bring psychological harm to human workers [72,85], engender emotional labor [26,97],
and cause epistemic ramifications [66]. Thus, social media platforms have increasingly
implemented algorithms in their moderation systems [7,24,44].
However, moderation algorithms have intrigued various concerns from the public. For
example, the international society is concerned that automatic tools need to be designed as
accountable to protect human rights (e.g., free speech) [83]. Journalists stressed that algorithms
could not perfectly enforce content policies given users’ complex language use, cultural
background, and intentions of generating content [82]. Even social media platforms admitted that
over-reliance on algorithms hurts more innocent users, and they thus reverted to deploy
additional human moderators [12].

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In this background of concerning about algorithmic moderation’s issues, prior researchers have
started to investigate users’ perspectives on moderation after they experience it. One of academic
attention is focused on the fairness aspect of moderation. Prior researchers have uncovered the
unfairness of moderation appeared in users’ marginalization. For instance, Haimson et al.
uncovered that sexual and racial minority groups who expressed their personal identities tended
to experience content removal higher than others [40]. Sybert found that sexual minority groups
contested NSFW (not safe for work) content ban by posting content that condemned Tumblr’s
new platform policies and legitimacy of moderation [88]. Furthermore, Feuston et al. found pro-
eating (Pro-ED) order users considered account suspension as unfair because they lost
opportunities of reflecting on their personal content and receiving community support [30]. Even
though Pro-ED users were found to circumvent moderation, Gerrard has stressed that moderation
should impartially protect such an already marginalized group [33].
The lack of transparency in moderation decision-making might incur perceived unfairness.
Suzor et al. [86] uncovered users felt largely unfair about their content removal or account
suspension because they did not receive detailed explanations on what rules they violated.
Researchers also found that YouTubers with small fanbase considered moderation punishments
were unfairly imposed on them compared with large channels [21], and they requested
explanations for moderation decision-making processes [62].
Research from HCI and CSCW has also investigated users’ fairness perceptions on moderation
cases. Lampe et al. [53] have found that when working as voluntary meta-moderators, users can
rate moderation decisions as either “fair” or “unfair” on Slashdot to reverse unfair moderation
decisions or remove low-quality moderators. More recent research has tended to focus on how
certain factors could influence users’ fairness perception given their moderation experiences. For
instance, Jhaver et al. [43] discovered that users on Reddit who experienced content removal with
receiving explanations perceived the moderation decision as fair. In the phase of contesting
moderation, Vaccaro et al. [92] simulated Facebook’s appeal process and uncovered that after
receiving explanations of appeals, users’ fairness perceptions improved.
The variety of work above shows that (1) prior researchers have considered unfairness of
moderation as biased decisions for marginalized people and (2) for users in general, prior work has
been focused on whether and how users generate a binary conclusion between fairness or
unfairness. However, relatively little attention has been paid to unpacking or elaborating on the
formation processes of fairness perception: how users develop their perceived (un)fairness after
experiencing content moderation. Thus, we aim to fill this research gap.
4 METHODS
We conduct a qualitative study by interviewing 21 YouTubers with a semi-structured interview
protocol and use inductive qualitative analysis to analyze the whole interview dataset.
4.1 Data Collection
We conducted 21 interviews with YouTubers who had experienced YouTube moderation, as
shown in Table 1. After obtaining the Institutional Review Board (IRB) board’s approval in our
institution, we used both purposeful sampling and snowball sampling [90] to recruit participants
to join this study. For the purposeful sampling, we created a recruitment website describing the
criteria of this study: recruiting a YouTuber who is over 18 years old and has experienced
YouTube moderation (e.g., ‘limited ads,’ ‘age restriction,’ etc.). We disseminated this website on

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social media platforms such as Reddit (e.g., r/youtube, r/PartneredYoutube) and Twitter. We
searched keywords related to YouTube moderation, such as ‘demonetization’ on Twitter, to
directly message YouTubers who have shared their moderation experiences in their tweets. For
the snowball sampling, we let interviewed YouTubers introduce YouTubers in their community
who also experienced moderation to join our study. As shown in Table 1, we recruited YouTubers
from various content categories (e.g., games, technology, entertainment, education) with a wide
scope of subscription numbers (i.e., fanbase ranging from ~2k to ~497k on interview dates). In this
study, nearly every participant was compensated with a 20 dollars gift card, except three
proactively and firmly expressed their will not to be recompensated.
Table 1. YouTubers’ demographic information. Subscription number (fanbase) was accordingly collected on
the date of the interviews. Work status is self-identified by YouTubers depending on their time spent on
video creation. Career refers to the consistent period of video creation for their primary channel on YouTube
by the date of interviews. Category refers to the channel’s content category, which is defined by YouTube
(note: animation is under the entertainment category). Recruit indicates a participant is either recruited by
purposeful sampling (coded as 0) or snowball sampling (1). “N/A” means that our participants did not
disclose the information.
#
Subscription #
Age
Work
Status
Nationality
Race
Gender
Career
(yrs.)
Category
Recruit
P1
~ 25.8k
18
part-time
US
White
Male
0.5
Games
0
P2
~ 21.3k
23
full-time
US
White
Male
5
Games
0
P3
~ 6.6k
40
part-time
England
White
Male
3
Travel
0
P4
~ 52k
28
part-time
US
Black
Female
6
People
0
P5
~ 4.33k
19
part-time
England
White
Male
5
Technology
0
P6
~ 268k
29
full-time
US
White
Male
9
Animation
0
P7
~ 84.7K
29
full-time
US
White
Male
3
Games
0
P8
~ 177k
32
part-time
US
White
Male
4
History
0
P9
~ 365k
28
full-time
Germany
White
Male
2
Entertainment
0
P10
~ 23.1k
38
part-time
Mexico
Hispanic
Female
3
Education
0
P11
~ 292k
29
part-time
Brazil
White
Female
12
Entertainment
0
P12
~ 2.02k
21
part-time
England
White
Male
3
Education
0
P13
~ 124k
19
full-time
US
Hispanic
Male
4
Entertainment
0
P14
~ 88.6k
28
part-time
Colombia
Hispanic
Male
2
Education
1
P15
~ 12.6k
29
part-time
Mexico
Hispanic
Male
6
Education
1
P16
~ 35.5k
29
part-time
Mexico
Hispanic
Female
4
Technology
1
P17
~ 5.7k
21
part-time
US
N/A
Male
8
Entertainment
0
P18
~ 26.8k
29
part-time
Mexico
Hispanic
Female
3
Education
1
P19
~ 53.9k
32
part-time
Mexico
Hispanic
Female
3
Technology
1
P20
~ 497k
25
full-time
US
N/A
Male
2
Entertainment
0
P21
~ 230k
22
part-time
US
White
Male
7
Animation
0
We held interviews as well as recorded and transcribed them through Zoom. The duration of
each interview ranged from 28 minutes to 94 minutes (Average = 66.5), with the median equaling
63.5 minutes. The duration of conducting interview procedures, i.e., data collection, was from Jan
to Mar 2021. Before starting every interview, we requested and acquired verbal consent from
participants, confirming their willingness to join this study. Also, we informed them that their
personal information would be anonymous and protected, and they reserved the right to withdraw
from the interviews whenever they wanted. We followed a semi-structured interview protocol
(see Appendix A) to conduct each interview with YouTubers. Based on prior work (e.g., [43,62,92])
that discussed users’ moderation experiences, we designed a part of questions to understand how
YouTubers experience and handle moderation. We further designed another part given the
functional dimensions on YouTube [105,111] to investigate YouTubers’ fairness perceptions. In the

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process of conducting interviews, once we found intriguing points related to our research
questions or unique experiences that need to be elaborate, we put forward probes, i.e., asking
follow-up questions. Additionally, many of our participants shared their screens during Zoom
interviews or sent screenshots through emails to permit us to use them. This significantly
supplemented our data collection.
4.2 Data Analysis
We conducted an inductive qualitative analysis to analyze all interview data [55] by NVivo 12.
NVivo 12 stored all interview transcript data, and two researchers read through it all. They first
obtained an initial impression of the size of the dataset and YouTubers’ moderation experiences. In
a weekly meeting, they discussed the initial understanding and agreed to start the ‘open coding’
process, given the richness of the dataset. Then, two researchers separately returned to the dataset
and assigned discrete codes, either to sentences or paragraphs. The purpose of this step is to
convert textual data into condensed codes. During three weeks of open coding and weekly
meetings, two researchers discussed and resolved their disagreements on their assigned codes,
which then were altered to describe the data appropriately. After completing opening coding, two
researchers started the iterative ‘axial coding’ process. They re-read the quotes and associated
open coding codes and classified these codes together into higher-level concepts (i.e., axial coding
codes).
After two researchers moved back and forth between codes and associated data, they identified
the connections between axial coding codes (i.e., categories), combined them into themes, and
dissolved disagreements weekly. Concepts and ideas from the procedural justice scholarship
informed the axial coding process. We looked at whether some axial coding codes could align with
notions in procedural justice (e.g., voice, consistency). If so, we paid attention to how they could
be gathered into higher-level themes to end the selective coding process. In the final preparation
of presenting findings, the researchers removed themes that did not have enough data to support
them. This data analysis process ended by generating overarching themes from axial coding codes
to answer the research question.
5 FINDINGS
We found how our participants developed fairness perceptions from their moderation experiences:
(1) they encountered unequal moderation treatments through cross-comparisons; (2) they
observed that the moderation system made inconsistent decisions, processes, system actions or
those inconsistent with content policies; (3) they did not have voice or control in multiple
algorithmic visibility decision-making processes. Both inconsistency and the lack of voice violate
the core principles in procedural justice.
5.1 Equality in Comparing Moderation Treatment
Equal treatment matters to fairness perception [102]. Equality perception refers to how YouTubers
actively compared their moderation treatments with others to assess moderation fairness.
YouTube’s public statement [112] endorses the value of equality by claiming that nearly all
YouTube’s policies are equally applied to YouTubers. However, our participants thought
differently and claimed that they could observe unequal moderation actions through cross-

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comparison. For example, several small YouTubers
4
stated that large YouTubers received
preferential treatments. P4, a small YouTuber creating fitness videos, described her experience and
reasoning to us:
There are female YouTubers creating fitness videos with million-plus subscribers, so I’m
not sure what difference is between their content and mine, other than the person itself.
(…) I’m a black woman, so I don’t know if race has a part to play in it, but I just want to
point it out that if YouTube is flagging their content, at least, the women that are in
control of those channels are gonna speak on it, the way that many YouTubers do. So, it
leads me to believe that they are not experiencing the demonetization issues, but I am
[experiencing] in such a large volume and a high frequency. [P4]
P4 performed observations and cross-comparisons on large YouTubers’ experiences in the same
content category with her. She assumed that those large YouTubers would complain in their
videos and let viewers know if they experienced YouTube moderation. Since those she observed
did not complain, she inferred that they did not experience the highly frequent moderation as her.
Thus, she felt it unfair that she received disproportional moderation, different from large
YouTubers.
Prior work has uncovered that sexual minority and small YouTubers felt demonetization
punishment was unfair compared with large YouTubers [21,62]. Beyond that, our participants
further reported fairness issues related to support resources they had access to upon receiving
penalties. Small YouTubers felt unfair when receiving less support to remedy moderated content.
For example, P17 described:
If you contact a YouTuber with 100K or more, they are usually assigned a partner
manager. With that power, they can communicate with someone who works at YouTube
directly and get better specifications to fix their videos. With someone like myself who’s
under that threshold, usually I might have to talk to a content creator [large YouTuber],
whom I know and say: “Hey, can you ask your partner manager or whatever?” and
sometimes they say yes and sometimes they say no, [they are not] willing to help me.
[P17]
Partner managers refer to YouTube-hired experts helping YouTubers grow their channels. As
claimed by YouTube, “we determine partner eligibility according to channel size, channel activity,
and adherence to YouTube” for inclusion [113]. P17 observed that this service also helped large
YouTubers repair moderation issues. So, he sensed YouTube moderation’s unfairness from
acquiring unequal distribution of support resources (e.g., communication, human reviewers’
support) to solve moderation issues given different channel sizes.
Echoing the sentiment, some participants with a large fanbase also acknowledged that
YouTube might issue unfair moderation on others, especially the small ones. P6, a large YouTuber
creating anime videos, told us:
One thing that’s helped a bit is the form that I can fill out. That gives me a breakdown of
the guidelines, and I’m able to figure out what is overall acceptable on YouTube. And
then I apply that to other videos that I see and be like, hey, this is being unfair to this, this
should have been fine like what the example of [YouTube A (a small anime YouTuber)],
4
In this paper, we define ‘small YouTuber’ as those in the YouTube Partner program having more than 1,000 but less
than 100K subscribers. Large YouTuber correspondingly refers to those with more than 100K subscribers.

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his video obviously should not have been marked as ‘made for kids’ because there’s
literally some blood. There’s some humor that kids would be completely confused. It was
very obviously targeted toward young adults. [P6]
In this example, the form that P6 mentioned refers to the self-certification function for
YouTubers to self-report whether their new content complies with content policies before
uploading it [111]. ‘Made for kids’ is a moderation tag (in audience settings) directing specific
videos primarily for viewers under 13 years old and disabling a series of features at both video and
channel levels [114] (e.g., comment close and channel membership close [4]). P6, a relatively large
anime YouTuber, observed uneven moderation treatments when applying the knowledge that he
acquired from using self-certification to other small YouTubers who created similar videos. He
thus considered moderation as unfair for small YouTubers because he thought they should not
have been tagged ‘made for kids.’
In sum, our participants attributed unequal moderation treatments to their different fanbases or
identities. This perceived inequality existed in moderation decisions, the frequency or severity of
moderation practices they encountered, and disproportionate resources acquired to repair
moderation issues. Thus, our participants felt their moderation experiences as unfair after their
cross-comparisons.
5.2 Consistency within Algorithmic Moderation Decisions
YouTubers’ fairness perception hinges on consistency across moderation decisions and policies.
However, our participants perceived the unfairness when they observed that the moderation
system made inconsistent decisions or ones inconsistent with content policies.
5.2.1 Consistency between Moderation Algorithms and Content Policy
YouTube relies on machine learning algorithms to enforce content policies to moderate videos
through metadata (e.g., titles, thumbnails, descriptions, captions, etc.) [3,36,73]. And YouTubers
have developed folk theories regarding what texts would cause moderation to happen [3,68]. Our
participants described how such knowledge informed them of misalignment between algorithmic
decisions and content policies, resulting in the feeling of unfairness. P7, a YouTuber creating
games-related videos, said:
The demonetized one [video] was a video game related to World War Two, and it
included Hitler [in the captions]. I was talking about World War Two or Germany in this
context. YouTube is very sensitive [to violent content], even though it is a video game. Of
course, they don’t really care. (…) Basically, they didn’t explain anything. It was very
opaque. [P7]
In the above example, P7 referred to the ‘demonetized one’ as a video with ‘limited ads’ [108],
denoting that few or no advertisers would like to place ads on the video. YouTube’s advertiser-
friendly content guidelines [115] state that “violence in the normal course of video gameplay is
generally acceptable for advertising, but montages, where gratuitous violence is the focal point, is
not.” P7 deduced the keyword “Hitler” in his video might be associated with violence, but he
considered his video unrelated to violence at all. Thus, he felt it unfair that YouTube’s moderation
failed to be in line with content policies.
Prior work uncovered that Facebook inconsistently applied content rules among users [54,92].
In a similar vein, our YouTubers participants were particularly concerned about the consistency
between the actions of moderation algorithms and content policies. They considered YouTube

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moderation as unfair when moderation algorithms seemingly took more time to implement
updates in content policy. For instance, P10, a YouTuber creating science and education videos,
described:
I felt unfair because it (YouTube) only stated that they didn’t find it suitable for their
advertisers. (…) During the same week or two, all of our channels were demonetized on
COVID-19 videos, so I was constantly in touch with other YouTubers, and we concluded
that the problem was the COVID-19 situation (…). I think it may have been from May to
July [, 2020]. We would all still make videos telling people to take care, but we would not
say ‘COVID-19’; (…) our community just used euphemisms that kept us on the safe side.
[P10]
Limited explanations of ‘limited ads’ motivated P10 to collaborate with her community to
observe, exchange information, and discuss their past experiences. P10 and her community
showed their benevolent intentions of complying with advertiser-friendly guidelines, and the
content policy explicitly claimed support for science and education videos. YouTube updated its
policy, allowing YouTubers to discuss COVID-19 without ‘limited ads’ [116]. However, before that
time, the community collaboratively questioned moderation’s fairness due to conflicts between the
intentions they assumed their videos had, namely disseminating scientific knowledge of COVID-
19 and algorithm’s classification based on the keywords of their videos.
Prior work suggested that inconsistent enforcement of content policies might lead to chaotic
communities [79]. However, our participants such as P10 showed they did not insist on the
negative unfairness perception; rather, they proactively managed to solve their moderation issues
through community-wide support.
Moreover, participants experienced unfairness when anticipated moderation actions did not
take place. For example, P7 and P11 shared similar experiences:
There’s nudity in the game [video] I forgot to censor, several parts of nudity, and it’s
completely monetized. It has almost one million views, and there’s literally nudity. It’s
not accurate at all. The [moderation] algorithm is absolutely terrible on YouTube. [P7]
Recently I did a video where I cursed (…). The whole video was me cursing at them. And
it didn’t get demonetized. I was a bit shocked how this didn’t get demonetized. (…)
[YouTube is in the] really bad accuracy. When I do high-value projects, I get
demonetization. When I do this trolling, I don’t get demonetized. [P11]
In the above two examples, while creating different categories of content, P7 and P11 both felt
it unfair that YouTube did not issue ‘limited ads’ to videos that they perceived as not advertiser-
friendly. Also, YouTube demonetized the videos which P11 presumed as advertiser-friendly. Both
participants showed they understood what content would violate advertiser-friendly guidelines
[115]. They then attributed the moderation conduct that was not in line with content policies to
the issues of algorithmic accuracy. The above cases presented that the perceived fairness emerged
when moderation systems failed to consistently implement content policies.
Lastly, our participants noticed that YouTube might run different moderation algorithms on
several services. For instance, P9, a YouTuber who also did live-streaming, said to us:
I had the live stream [on YouTube Live] where I took videos that were already uploaded
on YouTube; (…) they were monetized, and I played them in a live stream 24/7 (…). It did
so for two months, but then suddenly, I received a warning from YouTube saying that my

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live stream violated the community guidelines, which doesn’t make any sense to me even
though I can appeal. [P9]
YouTube Live is a live streaming service provided by YouTube; P9 displayed the already
published videos on the YouTube live. Those videos were in all normal statuses on YouTube,
which thus gave him a policy-level signal that his videos did not violate any rules. However, he
found that YouTube moderated his live streaming after two months of displaying the already
published pre-recorded videos. He felt it unfair why YouTube ran an inconsistent level of content
moderation between YouTube and YouTube Live while both should moderate content equally
given policies [117].
5.2.2 Consistency in Moderation Explanations
Participants’ feelings of unfairness arose when they received inconsistent moderation
explanations from different entities in the moderation system. For example, P2, a YouTuber
making reaction videos, said to us:
My channel was deleted for spam scams or commercially deceptive content, which is
incredibly unfair because there’s nothing in that category that I did. It happened literally
after a third copyright strike (warning). (…) “If you think this was done incorrectly, email
this back, and we’ll review it.” [P2]
Reaction video is a type of video content where a YouTuber video record their real-time
reactions (facial expressions, physical postures, language, etc.) when watching other people’s
video. YouTube’s content policy [118] claims that once YouTubers receive a third copyright strike
(i.e., warning), their channels will be suspended. P2 experienced inconsistency that he expected a
copyright strike, but YouTube’s explanation cited the reason for generating scams to suspend his
channel. Hence, he felt the reason for suspending his channel was unreasonable and unfair.
YouTube is clearly aware of the potential limits in their algorithms and thus suggests that
YouTubers like P2 could contact human moderators to correct potentially disagreed moderation
decisions.
Participants also received explanations that were inconsistent after they contacted human
reviewers (e.g., creator support [119]) behind the moderation system. For example, P13, a
YouTuber making comedy videos, said to us:
My bigger video got taken down by scams, that was ridiculous, so I kept @ing them on
Twitter. Four days later, I received an email from creator support. (…) “from our internal
team, your video was intended to incite violence or dangerous illegal activities.” So that’s
completely different from ‘scams or deceptive practices.’ I was like, please explain to me
how my video was intended to incite illegal activities that have an inherent risk of
serious physical harm or death now. [P13]
The example above showed the inconsistency between the algorithmic explanation and the
human explanation that P13 received. The first explanation already invoked P13’s sense of
unfairness; the inconsistency between the first and second exacerbated this feeling. P13 claimed
that his video did not intend to promote violence and thus urged a convincing and detailed
moderation explanation to alleviate his sense of unfairness.

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5.2.3 Consistency between Different Algorithms
YouTubers noticed an inconsistency between different algorithms in YouTube moderation. Some
participants reported how YouTube’s moderation algorithm and monetization algorithm were
inconsistent. For instance, P3 described:
I know that (YouTube) they’re playing advertisements because people will leave a
comment saying there are three ads in this video. (…) This is a video that had the yellow
dollar sign. So, it’s amazing that they claimed it’d been known to [be] demonetized, yet
they’re still putting ads on these videos. [P3]
The yellow dollar symbol refers to ‘limited ads.’ A video with ‘limited ads’ should have few or
no ads. However, P3 found that a high quantity of ads was placed on his video with ‘limited ads,’
but P3 could not earn much from it. P3 thus developed perceptions of unfairness.
Similarly, YouTube’s moderation algorithms and visibility/recommendation algorithms were
not always consistent. Without any notifications or explanations, YouTube could incoherently
limit the direct visibility of videos, leaving YouTubers to test whether they experience content
moderation on their own. For example, P21 said to us by showing one piece of evidence from
Figure 2.1 to 2.3:
[When I] turned on the restricted mode filter, half of my videos just disappeared. (…)
There’s no label from the creator (Studio) dashboard. You have to @ YouTube on Twitter
to access the appeal [form] because some YouTubers don’t even know it exists. That’s
how I got access to this form where I can send them the video links to appeal to this
restricted mode. Still, again, since they (YouTube) don’t tell you that your videos [are]
unrestricted to begin with, they will not tell you if your video is restricted correctly. [P21]
Fig. 2.1. Two videos are in all normal statuses on the YouTube Studio dashboard.
Fig. 2.2. When the restricted mode is off. Fig. 2.3. When the restricted mode is on.
In the above example, ‘restricted mode’ is a content filter that prevents viewers from videos
containing potentially mature content. At a policy level, P21’s video had all normal statuses (i.e.,
visibility, monetization, and restriction), indicating his video was neither ‘age-restricted’ nor
‘made for kids’ nor unfriendly for advertisers. In other words, he deemed that his video did not
violate any content policies. However, YouTube blocked the direct visibility of videos for
audiences who opened ‘restricted mode’ or without accounts signed in. P21 was only able to find
this inconsistent moderation by switching to another YouTube account and testing with the
Video 1
Video 2
Video 1
Video 1
Video 2

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‘restricted mode,’ as shown from Figure 2.1 to 2.3. Thus, he perceived it unfair that YouTube
conducted inconsistent moderation on his videos.
5.3 Voice in Algorithmic Visibility Decisions
Procedural justice assumes that voice in decision-making processes enhances people’s perceived
fairness and is more likely to produce an equitable outcome [89]. Voice means allowing “people an
opportunity to provide inputs to decision maker” [16]. Prior work has stressed that platforms need
to have users’ voice/representation in moderation decision-making for designing contestability for
moderation [93] or embedding procedural justice in it [29]. Users’ feelings of fairness could
improve if users feel their voice has been heard [92]. Resonating with this line of work, we found
our participants wanted their voice to be heard either in moderation-decision making or after the
issuance of moderation decisions. But beyond that, we uncovered that when YouTubers realized
their voice or input was not heard, the effects of moderation-related algorithms had been already
taking actions, triggering ripple negative effects on their YouTube channels. So, their perceived
unfairness arose. For instance, P21 shared with us his comparative evidence (see Figure 3) between
a properly monetized video and two videos with different types of moderation decisions:
When [my] videos are in the limited ads, they just take your video completely out of the
algorithm. (…) they will not push it in browse features; they will not push it in your
suggested videos; they will not push it through notifications. People can still search it up.
They can find it on your channel, but it’s incredibly unlikely to find it that way, which is
terribly unfair. [P21]
Fig. 3. Different traffic sources of videos with full monetization (left), ‘limited ads’ (middle), and tags of ‘made
for kids’ (right).
In this example, the moderation tag, ‘made for kids,’ directs videos’ visibility to audiences
under 13 years old and disables specific videos features (e.g., commenting). Such moderation tag,
according to YouTube, should not have affected his visibility in search and recommendation
algorithms [120]. However, by observing and analyzing the videos’ different online traffic sources,
P21 found that once he received either ‘limited ads’ or ‘made for kids,’ YouTube algorithmically
constrained the visibility of his videos (i.e., diminished ‘suggested video’ rate and ‘browse
feature’[42] in Figure 3). This ultimately led to less watch retention time and subscription
increment. P21 showed us that he could tell and describe moderation’s various negative impacts
on him. However, none of the algorithmic visibility decisions had considered his voice, so his
perceived unfairness arose.
Besides the algorithmic impacts caused by one moderation decision each time (e.g., either
‘made for kids’ or ‘limited ads’), YouTube moderation overlapped between different types of it to
harm participants’ performance metrics. For example, P8 said:
This was a (…) limited ads video got age-restriction it but beyond that. They also delisted
it from search, so the best way to tell that is to type in my channel’s name and the name

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of the video. And if it doesn’t show up in that search thing, there’s something wrong
here, especially when it’s a video that got about 20,000 views in the first day. It’s obvious
shadowban because you cannot find it. [P8]
In this example, ‘age-restriction’ refers to the moderation decision where YouTube sets videos
unviewable to “users under 18 years of age or signed out;” at a policy level, an age-restricted video
might also be considered not advertiser-friendly (i.e., ‘limited ads’), which is what P8 experienced.
However, YouTube further disabled the public searchability of P8’s video, which exceeded his
common understanding. P8 used ‘shadowban’ to describe the phenomenon that YouTube seemed
to block the direct visibility of his video from non-subscribers. This case presented that P8 was
unable to express his voice before the multiple algorithms made decisions and took effect, arising
his perceived unfairness.
Furthermore, once experiencing moderation, participants found that moderation algorithms
even affected their future channel performance, so they doubted YouTube moderation’s fairness.
For example, P13 shared his observation with us from Figure 4.1 to 4.2:
I gotta get off this call (moderation), and now I’m working on my next video, which I’m
trying to make extra great. Because I want to hopefully get back in the [recommendation]
algorithm again, but I can see this video that’s like eight minutes that I put several hours
of editing into, as I spent almost three days straight editing it, literally not being pushed
out. It’s unfair because I can compare it to my last omega (popular) video’s views, and I
can look at an omega video from a few months ago. [P13]
Fig. 4.1. The underperformance of future videos.
Fig. 4.2. Channel’s longitudinal changes of subscription before (left) and after (right) moderation.
In the above example, once receiving a combined moderation decision of ‘limited ads’ and ‘age-
restriction,’ P13 found that all his new videos, even without these penalties, underperformed in

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viewership and viewer engagement metrics, as shown in Figure 4.1. Thus, by comparing with his
past similar videos, he felt it unfair that YouTube did not promote his videos even though they had
all normal statuses to monetize. P13 further observed that his subscription quantity increments
largely dwindled at a channel level once experiencing moderation, as shown in Figure 4.2.
However, YouTube neither showed how the moderation system made decisions to render P13’s
channel underperform in different visibility metrics nor had his voice in making these algorithmic
decisions, implying his perceived unfairness.
P20’s thought after he experienced moderation summarized the perceived unfairness where our
participants felt their voice was not heard by moderation algorithms:
“When your videos get limited ads, your other videos may underperform [in metrics].
There’s so much that can be hard to wrap your head around because you have to [assure]
at least two algorithms work correctly, the monetization and the recommendation
algorithm.” [P20]
As P20 noted, YouTubers needed to navigate between different ‘black-box’ algorithms. From a
procedural justice perspective, they were not given the power to express their voice before
algorithms made moderation decisions, and thus, they failed to comprehend why the ripple effects
happened.
So, in sum, our participants encountered a complex set of moderation decisions resulting from
multiple classes of algorithms, as well as a series of algorithmic impacts beyond what they
understood as fair. The lack of voice in these decision-making processes aroused their feeling of
unfairness.
6 DISCUSSION
Through an interview study with 21 YouTubers, we unpack and elaborate on the formation
processes of how our participants develop their fairness perceptions from experiences with
content moderation on YouTube: they reported (1) unequal moderation treatments, (2)
inconsistent moderation procedures and outcomes or inconsistency between these and content
policies; (3) their voice was scarcely involved before or after algorithmic visibility decision-making
processes.
Building upon these findings, we will discuss how a multi-dimensional notion of moderation
fairness generates from our participants’ fairness perceptions of moderation processes and
outcomes. We will further discuss how YouTube moderation demonstrates an algorithmic
assemblage: not a single algorithm but different classes of algorithms moderate content on
YouTube. Ultimately, we put forward design considerations for the moderation systems on a
platform like YouTube that affords monetization for content creators.
6.1 Moderation Fairness: A Multi-Dimensional Notion
Many prior studies have explored the result of perceived fairness in moderation with a binary
question, whether users felt moderation as fair or unfair upon receiving moderation explanations
[43,92] or upon having their participation in the adjudication of moderation cases [29]. Probing
deeper into YouTuber’s moderation experience, we unpacked and elaborated on the processes of
how YouTubers developed such fairness perceptions from the consistency, equality of, and their
voice in moderation procedures and outcomes.

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Fairness perception is not an isolated experience but involves comparison. We discovered that
our participants compared their moderation outcomes and the associated time of happening,
severity, resource allocations for repairing content, and subject characteristics (e.g., fanbase) with
those of other YouTubers. In the view of procedural justice, people perceive fairness from
consistency based on comparisons across time, people, or both [91]. Our participants individually
and collectively compared moderation actions, outcomes, and impacts with the claims of content
policies. This thus extended a binary question of whether users feel moderation as fair or unfair to
how their perceived (un)fairness was generated from moderation experiences. In other words,
fairness perception concerned not only a conclusion about whether the system was fair or not but
also the cognitive activities such as comparison and evaluation through which they reached the
conclusion.
Voice inclusion, both prior to and after authorities make decisions, matters to fairness
perception [60,91]. Our empirical findings pointed to participants’ representation, or lack thereof,
in moderation decision-making processes. They could observe how various moderation-related
algorithms made decisions and affected their metrics beyond the initial scope. However, they were
unable to have any voice in decision-making processes to obtain a sense of control over
moderation decisions. So, their perceived unfairness arose. This lack of understanding,
communication, and control for moderation outcomes indicated the importance of recognizing
YouTubers’ voice in moderation decision-making processes.
Moderation fairness on YouTube thus presents as a multi-dimensional notion as summarized in
Figure 5:
Fig. 5: Multi-dimensional notion of fairness in YouTube moderation.
• First, it has a temporal dimension in terms of at what stage YouTubers invoked the fairness
of moderation. Participants could invoke the concept of fairness when they just received
moderation decisions or penalties, when they sought resources to repair them, when they
tracked their channels’ later performance, or when they observed how future new videos
with all normal statuses underperformed in metrics.
• Second, it concerns the social context where YouTubers situate their fairness perception. A
social context here denotes a group of people who shares similar traits and thus allow cross-
comparisons. Our participants shared several distinct social contexts, including groups of

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participants who they were familiar with, who created content in the same category (e.g.,
fitness, education), and a network of YouTubers with various fanbases who experienced
punishments. Different social contexts allowed them to assess the fairness of interactions
they had with the moderation system on YouTube.
• Third, a technical dimension of moderation fairness surfaced. Our participants experienced
(1) inconsistency either in moderation explanations, decisions, system-actions, or those
inconsistent with content policies, (2) unequal moderation treatments with others because
they attributed inconsistent technical actions to their subjective characteristics, and (3)
unbalanced support from different algorithms for unmoderated videos and moderated ones
as well as between themselves and others.
• Collectively, when experiencing moderation processes over time, our participants had
different qualities of moderation experiences. They developed various fairness perceptions
because they experienced different impacts of moderation and made sense of it differently;
such cognitive activities from different participants showed social, temporal, and technical
dimensions of moderation.
YouTubers’ moderation experiences contour the multi-dimensions of moderation fairness,
complicating the understanding of moderation. Prior studies found that YouTubers felt it unfair
that they experienced disproportionate demonetization penalties [21,62] or recommendation rates
compared with large YouTubers [100]. Beyond YouTubers’ comparisons on outcomes or decisions
they encounter, this study further painted a more comprehensive picture of how their fairness
perceptions were generated from technical, social, and economic dimensions of moderation. When
examining the consistency of moderation, prior research has discussed inconsistent moderation
decisions from human moderators [11,86], and journalists have uncovered the unfair moderation
on users’ visibility [5]. Beyond inconsistent moderation decisions, we further highlighted how
moderation fairness on YouTube demonstrated temporal dimensions of moderation, arousing our
participants’ perceived unfairness. Such perceptions were generated when participants had
different qualities of moderation experiences in processes (e.g., observing ripple impacts, handling
moderation), as shown in Figure 5.
For future work in HCI and CSCW, this multi-dimensional moderation fairness indicates the
importance of studying moderation experiences: beyond understanding moderation outcomes,
researchers should not ignore how end-users have different qualities of experiences in social,
temporal, and technical dimensions of content moderation. For users who experienced moderation
outcomes (e.g., content removal, demonetization), as our participants’ moderation experiences
shown, their fairness perceptions originated not only from the punishments/decisions but also
from measuring the equality, consistency of, and their representation in moderation processes.
6.2 Content Moderation as an Algorithmic Assemblage
Commonly discussed moderation algorithms are those that directly impact content, such as
removing a video that contains hateful speech. However, moderators might use a bricolage of
tools to do the moderation work in online communities like subreddits [26]. Meanwhile, platform
users could also experience multiple moderation mechanisms at work. Our findings showed that
what moderates our participants on YouTube is not just a sole moderation algorithm. For instance,
when the ad or monetization algorithms issued a ‘limited ads’ (i.e., demonetization) decision, the
video would remain intact but would no longer be featured in search or recommendation. The
visibility algorithm could further reduce the visibility of a whole channel for the YouTuber in
question. On YouTube, it is thus an algorithmic assemblage composed of different classes of

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algorithms that work together to moderate content creation. When YouTubers interact with the
algorithmic assemblage, they essentially interact with the organization that implements it
[8,74,75]. So, when interacting with the algorithmic assemblage of YouTube moderation,
YouTubers participants generated their fairness perceptions in negotiations for content creation
favors from YouTube. That was because they cared about the consequences of not involving their
voice in moderation decision-making, which harm their content visibility and monetization.
Prior moderation studies have explored modes and algorithms built for the purpose of
moderation [34,36]. Moderation algorithms issue punishments that could be directly felt, such as
content removal or account termination. Visibility algorithms do not issue direct punishments.
Instead, they govern YouTubers through multi-layer visibility limits ranging from content-hiding
for certain groups of viewers (e.g., ‘restricted mode’) to public searchability ban to overall
recommendation rate deduction. When these outcomes appeared inconsistently, our participants
realized that their voice was barely included in the visibility algorithm’s decision-making
processes. YouTubers cannot observe the impacts on their performance of channels and videos
until their reduced visibility has taken effect for a while.
Amateur or small YouTubers were found to perceive recommendation algorithms as unfair
[100], and large YouTubers who obtained more knowledge had greater visibility than YouTubers
who did not [17,18]. Viewing from the angle of content moderation, our study distinctively
showed how one or more moderation decisions intertwined with algorithmic visibility. That is,
underneath moderation decisions, our participants experienced unfair visibility deduction at
transverse (i.e., videos, channels) and longitudinal levels, both and respectively. Meanwhile,
human moderators and moderation algorithms released inconsistent moderation decisions, adding
instability to their future content visibility and the labor of solving moderation issues.
Users wished to obtain control strategies to make their presence more visible by algorithms
(e.g., Twitter [20]). However, growing work has pointed out that users disproportionately lack
control of the harm caused by visibility algorithms (e.g., filtering, searching, ranking) [9,28,46],
which might occasionally hamper marginalized people’s recovery online [30]. Similarly, as our
participants’ experiences showed, observing and analyzing algorithmic visibility outcomes did not
indicate that YouTubers have their voice heard by YouTube’s human moderators or moderation
algorithms either before or after receiving moderation decisions. Valuing individual voice in
content moderation procedures, at the same time, has been a rising consideration for researchers,
especially in designing contestability for moderation with various values such as fairness and
transparency [93]. To design such contestability on YouTube, moderated YouTubers should be
considered as relevant stakeholders in moderation decision-making. That is because YouTubers
joining the YouTube Partner program essentially have a contract relationship with YouTube. In
this contact relationship, their activities of creating and monetizing video content become
YouTube’s commodity to earn ad income from advertisers. So, valuing YouTubers’ voice could
manifests the value of interactional justice [50], where the social media company,
Google/YouTube, values their digital or creative labor, YouTubers [69].
A lack of voice in algorithmic decision-making might further deepen the existing inequality
between users. Prior studies or news reports have initially discovered how YouTubers perceived
demonetization penalties as unfairly imposed on them [3,21,62]. We, through this study, moved a
step forward to show YouTubers’ lack of their voice in multiple algorithmic decisions, especially
those moderation penalties they discovered on their own. Various visibility deductions and
inconsistent moderation decisions further constituted an unequal ad income distribution.

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6.3 Design Considerations
While YouTube is an authority to implement platform governance practices, it should embrace
diverse voices in decision-making processes to ensure procedural justice. YouTubers’ content
creation and livelihoods rely on the platform; at the same time, their value and voice for the
platform should be fairly recognized by the decision-maker, YouTube (e.g., the moderation
system). Otherwise, they might develop different perceptions of fairness from their moderation
experience, as our participants did.
Offering moderation explanations could sometimes relieve users’ perceived unfairness [43], but
it is conditioned by the platform’s will to first disclose whether and how moderation happens.
Prior studies have uncovered that letting users know the benevolent intentions behind algorithmic
decision-making could increase their perceived fairness [58]. However, we discovered that certain
YouTube’s moderation practices were presented inapparent where YouTubers can only know they
experienced such moderation through their own tests. As P21 and other minority people
experienced [81], the silent moderation happened on videos with all normal statuses, which
further showed inconsistency with content policies. To resolve perceived unfairness, we thus
suggest that YouTube should disclose whether YouTuber’s specific videos are invisible under
‘restricted mode,’ which ought to be listed in the Studio dashboard. Also, if invisible, YouTube
should clarify the reasons and how to repair such moderation issues in steps instead of publicly
claiming “will not respond” to YouTubers even though they appeal [109]. This suggestion
resonated with many researchers’ call on making moderation more transparent [45,48].
For specific functionality designs, end-users’ voice and input should be valued in an algorithmic
assemblage of moderation decision-making processes. For example, as shown in our findings,
YouTube created a ‘self-certification’ (i.e., ad suitability) function in 2019. It aimed to improve the
accuracy of ML algorithms by acquiring YouTubers’ input to issue fairer ad-suitability decisions in
line with advertiser-friendly content guidelines [115]. However, our findings showed that
YouTube moderation was inconsistent in issuing and enforcing ad-suitability decisions. Also,
YouTubers further felt unfair about inconsistent decisions such as ‘made for kids’ and being
invisible under ‘restricted mode.’ Thus, we suggest self-certification functionality should further
consider more classes of user input for moderation decision-making processes because YouTube
moderation cannot be simply referred to as ‘limited ads’ moderation. Hence, asking YouTubers
whether and why their video is made for kids before listing it, instead of choosing options in
audience settings, can give YouTubers chances to predict and acquire a sense of control over how
acceptable their video is. This change could inform platform developers or engineers about the
way how users want their voice involved in moderation decision-making. That is, resonating with
the research trend of embedding procedural justice in moderation [29,92], to allow moderated
users’ inputs to impact moderation decision-making (e.g., having their inputs as training data for
moderation algorithms before decisions are really made).
Also, this study provides transferable design considerations for content moderation on other
platforms. Many platforms nowadays afford monetization for content creators to benefit from
creating content on platforms such as Facebook, Twitter, TikTok, Instagram, and YouTube. Based
on our findings, we argued that creators are entitled to know whenever moderation decisions
affect their visibility and monetization because moderation is conducted through algorithmic
assemblages. For example, YouTube educates YouTubers on how to improve their visibility
through recommendation algorithms, giving them knowledge and control of delivering content to
audiences [87]. However, YouTube did not teach them how to repair moderation issues brought
by visibility algorithms. When moderation decisions were made, we found that both visibility of

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the moderated video and future new videos with all normal statuses became less favored by
recommendation algorithms, which presented a conflict with content policies. So, YouTubers
might feel it unfair that they could not have their voice in visibility algorithmic decisions.
These findings resonated with “shadowban,” the phenomenon that researchers have largely
discussed. It was reported that minority people (e.g., woman, sexual minority)’s visibility was
largely decreased by moderation algorithms on Reddit [99] and Instagram [10]. However, our
findings (e.g., Section 5.3) have been extending this line of work regarding what impacts of
visibility decrease (or shadowban) meant to YouTubers. That is, like users on Instagram or Reddit,
YouTubers might lose chances of free expression or self-disclosure. But moreover, they needed to
undergo potential loss in monetization due to visibility decrease. So, not only moderation
algorithms were found to be an assemblage structure but also creators’ negative experiences
where the impacts generated from this structure were also correlated.
Thus, we suggest specific procedures that platforms affording monetization for creators could
apply to disclose how moderation decisions would affect creators. First, using YouTube as an
example, when a YouTuber receives a ‘limited ads’ decision, YouTube should inform them of how
such a decision affects their Cost per 1,000 impressions (CPM) [105] instead of only claiming a
statement that most advertisers would not place ads. Second, YouTube should let creators know
whether such a decision affects their visibility (e.g., reach to audiences, audience engagement).
Last, YouTube should inform creators of how this impact will be calculated to the monetization
mechanism, especially for the YouTubers who have revered false-positive moderation decisions.
These strategies could be transferred to other platforms affording monetization for content
creators.
7 LIMITATION AND FUTURE WORK
The 21 YouTube partners we interviewed might not represent the experience of all YouTubers
who perform either profitable or amateur video creation. Algorithmic moderation on YouTube is a
complex process involving a massive-scale user base, so our findings cannot represent the
moderation operations or fairness perceptions of all. Especially those YouTubers or a team
working for one YouTube channel who have a much larger fanbase (e.g., more than 1 or 10 million
subscribers) might have different experiences and perceptions of YouTube moderation. However,
with the nature of interview studies for qualitative research, we do not intend to produce
generalizability but yield a novel and insightful understanding of how YouTubers perceive the
fairness of YouTube moderation. So, future studies could investigate how larger or amateur
creators (i.e., ones who cannot monetize) experience moderation from severer punishments such
as account suspension or content removal.
Although our participants are from relatively different backgrounds (e.g., countries, ages
groups), they did not express much about how their cultural background might affect their
moderation experiences. They tended to describe personal interactions with YouTube’s
moderation systems. This was because, we assumed, the technologies on YouTube are relatively
universal. While the cultural difference in interactions with moderation was not our focus in this
study, we did recognize this could be a future study.
Also, in our study, we found several YouTubers experienced moderation majorly due to
copyright infringements. Copyright cases involve copyright laws regarding ‘fair use’ and one
more stakeholder, copyright owners, in the moderation procedures. The owners can take part or
all YouTubers’ ad income earned from a video that violates copyright [36]. Thus, it would be more
complex than other types of moderation. Future research could explore the relationship between

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YouTuber’s monetization and YouTube copyright moderation involving the platform, YouTubers,
and copyright owners.
8 CONCLUSION
Research in HCI and CSCW has started to investigate users’ content moderation experiences.
Beyond focusing on specific moderation decisions (e.g., content removal), we argue that we should
not ignore how users experience moderation processes and how they generate fairness
perceptions from different dimensions of moderation, as the multi-dimensions of moderation
fairness showed on YouTube. This study with 21 YouTubers uncovered that participants’
perceived fairness arose when they measured equality, consistency, and their representation in
moderation decision-making processes and outcomes. Their experiences show that on YouTube,
not a sole class of moderation algorithm but an algorithmic assemblage moderates YouTubers’
profitable content creation, implementing platform governance. To design a fairer moderation
system, users’ voice needs to be involved in moderation decision-making process and allowed to
impact this process. That is because, though moderation systems are relatively universal, users’
moderation experiences could be different due to their temporal, social, or technical contexts. We
thus call for action: future research should not ignore whether and how moderated users have
different qualities of experiences in moderation processes.
ACKNOWLEDGMENTS
We thank the associate chairs and anonymous reviewers for their constructive feedback and
suggestions. We thank Xinning Gui for her feedback to the iterations of this paper. We also
appreciate 21 YouTubers’ participation and support for this study. Lastly, this work is partially
supported by the NSF, under grant no. 2006854.
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A INTERVIEW PROTOCOL
Interview Protocol
Guiding Research Question: How do YouTubers’ fairness perceptions generate from moderation experiences?
1. Warm-up questions
How old are you?
What gender do you identify with?
What ethnicity do you identify with?
Which country do you locate in?
How many years do you consistently create videos on YouTube?
What content category/community of your channel is?
Do you consider yourself as a part-time or full-time YouTuber, on your time spent on creating
videos?
2. Investigating
moderation experience on
YouTube
What moderation or punishment did you experience?
Can you explain how did that happen?
What explanations did YouTube provide for it?
How do you think of these explanations?
Did this moderation affect your video’s performance? If so,
What metrics are affected? And how do you know that?
How did this affect your channel’s metrics?
Have you noticed any impression rate difference after experiencing it? If so, could you elaborate
the situation?
How did this affect your community who create the same content with you?
How do you handle moderation punishment?
How effective is your coping behavior for it?
3. Understanding
perceived fairness on
YouTube moderation
Do you feel if it’s fair for the moderation you experienced, and why?
If exist, how do you feel about the impacts that your channel experience?
Do you feel if it’s fair for the procedure of fixing the moderation, and why?
Do you feel if it’s fair for the appeal process, and why?
How do you consider YouTube Team’s role in this process, and why?
What did you learn from the moderation?
Received: January 2022, Revised: April 2022, Accepted: May 2022.