# Content Moderation Justice and Fairness on Social Media: Comparisons Across
Different Contexts and Platforms

Jie Cai  Pennsylvania State UniversityUniversity ParkUSA<EMAIL_ADDRESS>0000-0002-0582-555X ,  Aashka Patel  New Jersey Institute of
TechnologyNewarkUSA<EMAIL_ADDRESS>0000-0003-4566-2537 ,  Azadeh Naderi  New
Jersey Institute of TechnologyNewarkUSA<EMAIL_ADDRESS>0000-0002-0732-9912 and
Donghee Yvette Wohn  New Jersey Institute of TechnologyNewarkUSA
<EMAIL_ADDRESS>0000-0001-5583-4430

(2024)

###### Abstract.

Social media users may perceive moderation decisions by the platform
differently, which can lead to frustration and dropout. This study
investigates users’ perceived justice and fairness of online moderation
decisions when they are exposed to various illegal versus legal scenarios,
retributive versus restorative moderation strategies, and user-moderated
versus commercially moderated platforms. We conduct an online experiment on
200 American social media users of Reddit and Twitter. Results show that
retributive moderation delivers higher justice and fairness for commercially
moderated than for user-moderated platforms in illegal violations; restorative
moderation delivers higher fairness for legal violations than illegal ones. We
discuss the opportunities for platform policymaking to improve moderation
system design.

Social Media, Content Moderation, Justice and Fairness, Platform Governance,
Policymaking

††journalyear: 2024††copyright: rightsretained††conference: Extended Abstracts
of the CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Extended Abstracts of the CHI Conference on
Human Factors in Computing Systems (CHI EA ’24), May 11–16, 2024, Honolulu,
HI, USA††doi: 10.1145/3613905.3650882††isbn: 979-8-4007-0331-7/24/05††ccs:
Human-centered computing Empirical studies in collaborative and social
computing††ccs: Human-centered computing Empirical studies in HCI

##  1\. Introduction

To fight harmful content and maintain a safe online space, platforms use
algorithms to automatically or human labor to remove harmful content and
sanction offenders manually (Gillespie, 2018), which is termed “content
moderation.” Content moderation is defined as “the practice of screening of
user-generated content (UGC) posted to Internet sites, social media platforms
and other online outlets, to determine the appropriateness of the content for
a given site, locality, or jurisdiction” (Roberts, 2017). Social media
platforms have developed complex and platform-specific content moderation
policies to regulate harmful content (Pater et al., 2016). This paper uses the
term “policies” to indicate formalized statements such as rules, standards,
terms of services, and community guidelines. These content moderation policies
guide human moderators and algorithms to remove offensive content and sanction
offenders (Cai et al., 2021). Many users complain their content has been
removed by the platform without a clear explanation and frequently express
confusion about what action has triggered a moderation action or account
suspension (Lyu et al., 2024; Ma and Kou, 2022; Elmimouni et al., 2024),
leading users to feel frustrated and leave (Myers West, 2018).

As moderation strategies vary across different contexts and platforms, users
may perceive different levels of justice and fairness in the moderation
decision. Many HCI scholars have explored the platform’s moderation mechanisms
from various perspectives, such as human labor (Steiger et al., 2021) and
transparency (Ma et al., 2023b; Ma and Kou, 2023). From the end-user
perspective, some work explores the bystander effect (Blackwell et al., 2018),
and others explore victims of harmful content (Schoenebeck et al., 2021b; Xiao
et al., 2022). For example, recent work shows that explanation of content
removal increases a user’s perceived fairness and engagement in the community
again, and a user’s perceived education and interaction are more effective
than blocking (Cai and Wohn, 2019), suggesting that removal explanation is
positively associated with users’ perceived justice and fairness.

In line with working to understand the user’s perception of moderation
decisions, we ran an online experiment to understand how the explanation in
different contexts with different harmful content can influence users’
perception of fairness and justice. We extend the line of research about
justice and fairness in content moderation. The findings can potentially
benefit social media platforms by furthering their understanding of how their
users perceive moderation decisions and enabling them to better current
moderation practices with policymaking.

##  2\. Related Work

###  2.1. Dependent Variables: Justice and Fairness

Computing systems are embedded with different biases regarding context, user
populations, and technical constraints, further leading to unfairness and
injustice in use (Friedman and Nissenbaum, 1996; Smith et al., 2023). Content
moderation as a sociotechnical process can privilege normalized groups while
pushing other user groups with different races, genders, and religions, to the
margins (Das et al., 2021).

The justice lens from criminal justice systems has been applied to the content
moderation domain; if users perceive moderation decisions to lack justice and
fairness, they are likely to stop abiding by the rules of the platforms or
even seek their own ways to punish the offenders, resulting in punishment that
may be indeterminate, uncalibrated, or inaccurate (Blackwell et al., 2018).
Moderation decisions considering different contexts and offender’s
characteristics can potentially increase the perceived fairness and justice
(Cai and Wohn, 2021; Schoenebeck et al., 2021a). As such, in order to keep
online communities safe and civil, we aim to assess users’ perceptions of
justice and fairness on social media platforms.

Justice is the perceived adherence to rules that reflect appropriateness in
decision contexts (Colquitt and Zipay, 2015), including consistency, accuracy,
bias suppression, and correctability (Thibaut and Walker, 1976). Justice has
been discussed broadly in online harassment and platform governance (Cai and
Wohn, 2021; Kou, 2021; Schoenebeck and Blackwell, 2021). In our study, we
center the general users’/bystanders’ perspective of justice in content
moderation. We view it as adherence to rules of conduct regarding online
content violation and moderation strategies applied. Fairness is an
individual’s moral evaluation of the rules of conduct (Colquitt and Rodell,
2015). Fairness is an important factor for online content moderation. For
instance, providing explanations for content removal can increase an
offender’s perceived fairness and desire to keep participating in the
community (Jhaver et al., 2019). In this study, users’ perceived fairness of
moderation decisions is the users’ moral evaluations of rule enforcement
regarding online content violations and moderation strategies.

###  2.2. Independent Variables: Platform Types, Violation Types, Moderation
Types

####  2.2.1. Commercially moderated Versus User-moderated Platforms

While specific platforms practice a bottom-up governance model that relies on
community members to enforce policies, others practice top-down governance in
which “officials implement a relatively detailed set of rules over a given
community” (Bradford et al., 2019). Examples of such platforms include
Facebook and Twitter, where online harassment is dealt with centrally rather
than relying on volunteer moderators (Cai et al., 2021). Commercial content
moderators for these platforms are paid and contingent (Roberts, 2017). Users
of such platforms believe that the company should bear more responsibility for
content moderation rather than having the responsibility fall on the users
themselves (Patel et al., 2021). On the other hand, with user-moderated
platforms such as Twitch, creators of the user-generated content have
moderating privileges and can appoint their followers to be additional
moderators (Wohn, 2019). Often, volunteer moderators find personal meaning in
their roles (Seering and Kairam, 2022) and want to strengthen their online
communities by guiding and developing offenders rather than simply “cleaning
up” misbehavior (Seering et al., 2019; Wohn, 2019). Overall, there are
apparent differences between how commercial and user moderators treat
offenders (Cook et al., 2021); consequently, the different treatment may
affect users’ perceived justice and fairness of the punishment. As such, we
ask our research question:

  * •

1) Will users’ perceived justice and fairness of online moderation decisions
be higher or lower for commercially moderated versus user-moderated social
media platforms?

####  2.2.2. Illegal versus Legal Content Scenarios

Social media platforms face increasing pressure from users and lawmakers to
“clean up their platforms” (Leetaru, 2018). The legality of content posted
online matters for some users, and these users generally believe that
platforms should not have the power to remove this content “as long as their
posts are not illegal and do not incite illegal assembly, destruction of
property or violence” (Morici, 2021). This study considers illegal violations
as content-inducing crimes or public safety concerns. Users believe social
media platforms should have the ability and fairness to remove and moderate
illegal material, such as child pornography, from their platforms (Langvardt,
2018; Jiang et al., 2021).

Online abuse such as racial slurs, bullying, sexual harassment, spam, trolls,
and hate speech toward a specific group or individuals are also considered
violations (Blackwell et al., 2017; Jiang et al., 2021). This abuse is mainly
handled by the platform or even the specific entities like end-users and human
moderators (Cai et al., 2021). In this study, by community guidelines of
various social media platforms, we consider this online abuse targeting a
specific group or individual without severe public impact as a legal
violation. These violations are broadly defined and contingent on different
platforms and communities. Because the belief that illegal content should be
moderated on social media platforms echoed in existing literature and media
articles, we developed the following hypotheses:

  * •

H2a. Perceived justice is higher in illegal compared to legal content
scenarios.

  * •

H2b. Perceived fairness is higher in illegal compared to legal content
scenarios.

####  2.2.3. Restorative Versus Retributive Moderation Strategies

Retributive justice is “a theory of punishment in which individuals who
knowingly commit an act deemed morally wrong receive a proportional punishment
for their misdeeds” (Blackwell et al., 2018). Restorative justice is “a
process whereby all the parties with a stake in a particular offense come
together to collectively resolve how to deal with the aftermath of the offense
and its implications for their future” (Messmer and Otto, 1992). Generally,
platforms remove offensive content and the offender from their communities.
This content moderation practice is believed to echo the American criminal
justice system and retributive justice (Hasinoff et al., 2020). Through
restorative justice, the offenders are meant to acknowledge their wrongdoings,
accept responsibility for their transgressions, and demonstrate remorse
(Schoenebeck et al., 2021a). As such, through restorative content moderation,
social media platforms can build healthier, resilient, and long-term online
communities (Hasinoff et al., 2020).

We adapt the retributive and restorative perspectives and divide the
moderation strategies into retributive and restorative moderation, similar to
the moderation styles punishing and nurturing (Jiang et al., 2023).
Retributive moderation strategies in this study refer to banning offenders for
rule-breaking and incapacitating offenders’ community participation.
Restorative moderation strategies in this study refer to light punishment
compared with the same violation (e.g., warning offenders with rule
explanation and potential consequences if offenders keep behaving similarly)
to maintain the community. Users of social media platforms may prefer one form
of moderation strategy over the other. As such, we developed the following
hypotheses:

  * •

H3a. Perceived justice is higher for retributive compared to restorative
moderation strategies.

  * •

H3b. Perceived fairness is higher for retributive compared to restorative
moderation strategies.

Table 1. Scenario Descriptions in the Experiment

Scenario 1a |  Scenario 1b |  Scenario 1c |  Scenario 1d
---|---|---|---
a restorative moderation strategy for illegal content on a user-moderated platform |  a restorative moderation strategy for illegal content on a commercially moderated platform |  a retributive moderation strategy for illegal content on a user-moderated platform |  a retributive moderation strategy for illegal content on a commercially moderated platform
Scenario 2a |  Scenario 2b |  Scenario 2c |  Scenario 2d
a restorative moderation strategy for legal content on a user-moderated platform |  a restorative moderation strategy for legal content on a commercially moderated platform |  a retributive moderation strategy for legal content on a user-moderated platform |  a retributive moderation strategy for legal content on a commercially moderated platform

##  3\. Methods and Materials

We choose Twitter (now “X”) and Reddit as our research context. Twitter is a
typical platform applying commercial moderation, while Reddit is known for
user moderation. Both platforms provide thread-style communication with
sharing and commenting features. The similar affordances and the different
moderation policies indicate they fit well for design experiments with various
scenarios.

###  3.1. Online Experiment Deployment

We conducted an online experiment with participants recruited from Qualtrics.
We limited the recruitment of participants to the United States to ensure that
all participants had a similar understanding of the topic. We also set the
gender as 50% male and 50% female to control the gender bias. Our sample size
was 200 (100 with either legal or illegal scenarios). To ensure our sample was
as representative as possible, we asked Qualtrics to set these quota
guidelines. If potential participants were under 18 and did not utilize
Twitter and/or Reddit, they were terminated from the online experiment. If
online experiment takers answered either “I will not provide my best answers”
or “I can’t promise either way” to our question “Do you commit to providing
your thoughtful and honest answers to the questions in this online
experiment?”, they were also terminated. In the consent form at the beginning
of the survey, we stated that participants would be exposed to violent and
controversial content with relevant mental health resources to mitigate the
negative impact of this study. The median time for participants to complete
the online experiment was 7.4 minutes. If participants completed the online
experiment, they were compensated per their agreement with Qualtrics, the
panel provider. We paid Qualtrics $5.00 (USD) for each participant. Our
participants were 18 to 65 or older, mostly between 30-49 (50.5%) and White
(69%).

Table 2. Descriptive Statistics for Each Group and Scenario in Terms of Perceived Justice | Justice-a | Justice-b | Justice-c | Justice-d | Justice-overall
---|---|---|---|---|---
| Scenario_1a | Scenario_1b | Scenario_1c | Scenario_1d |
Group1: Illegal |  M = 3.32, SD = 1.30 |  M = 3.19, SD = 1.29 |  M = 3.93, SD = 1.35 |  M = 4.48, SD = 1.10 |  M = 3.73, SE = 0.11
| Scenario_2a | Scenario_2b | Scenario_2c | Scenario_2d |
Group2: Legal |  M = 3.60, SD = 1.33 |  M = 3.77, SD = 1.34 |  M = 3.45, SD = 1.49 |  M = 3.66, SD = 1.44 |  M = 3.62, SE = 0.11
|  p = .136 |  p = .002 |  p = .019 |  p ¡ .001 |  p = .487

(a) Justice

Interaction effect between scenarios and groups regarding perceived justice.
The starting point on the top is the legal violation group.

(b) Fairness

Interaction effect between scenarios and groups regarding perceived fairness.
the starting point on the top is the legal violation group.

Figure 1. Interaction between scenarios and groups regarding perceived justice
and fairness.

###  3.2. Experimental Design

Our online experiment used a between-subjects design by legality (Group 1
illegal, Group 2 legal). Then, we used a within-subjects design for each group
with four scenarios (1a, 1b, 1c, 1d, 2a, 2b, 2c, 2d). We showed each group a
total of 4 mock scenarios designed by the team, as shown in Appendix A. The
first group (Group 1) of participants viewed four scenarios in which illegal
content was posted to Reddit and Twitter. The second group viewed four
scenarios where legal content was posted to Reddit and Twitter, as shown in
Table 1. We ran a series of mixed ANOVA with Bonferroni post-hoc tests to
answer RQ1 and test our main hypotheses, H2s and H3s.

Before creating the scenarios, the team read Twitter’s Community Guidelines
and Reddit’s Content Policy. We did this to create illegal and legal content
from scratch that would violate the two platforms’ policies and would likely
be removed from the platform. Team members wrote the illegal and legal content
of all eight scenarios for the online experiment design. We presented the
scenarios in the lab meeting to collect feedback and modify them. The illegal
scenarios are consistently about terrorism inducing public safety concerns,
and legal scenarios are consistently about sexism and racism targeting a
specific group or individual. Each scenario states either a Reddit or Twitter
user posted the message or tweet to either Reddit or Twitter. The scenario
then states the message or tweet the fake Reddit or Twitter user posted.
Following this, the scenario states the removal from the platform by the
moderator on the respective platforms for violating the platform’s policy. The
scenario states the policy that the message or tweet violated. Additionally,
the scenario states the moderator either warned the user or permanently banned
the user. After each scenario, participants answered questions about perceived
justice and fairness. Scenarios and survey questions are in Appendix B.

###  3.3. Measurements

The participants’ perception of justice for each scenario was measured with a
single item: “Is it necessary to punish the (Reddit or Twitter) user for their
post to deliver justice?”. Participants answered on a 5-point Likert scale
from 1 (Absolutely Not Necessary) to 5 (Absolutely Necessary). This item was
extracted and adapted from the Punishment Orientation Questionnaire (Yamamoto
and Maeder, 2019). The participants’ perception of fairness for each scenario
was measured with a single item: “How fair do you perceive the (Reddit or
Twitter) moderator’s decision to be?”. Participants answered on a 5-point
Likert scale from 1 (very unfair) to 5 (very fair).

##  4\. Results

###  4.1. Perceived Justice

Mauchly’s Test of Sphericity indicated that sphericity was met (W = .98,
$\chi^{2}(5)$ = 4.02, p = .547). The tests of within-subjects effects show a
significant main effect among the scenarios (F(3,594) = 21.45, p ¡ .001,
$\eta^{2}$ = .10). There was also a significant interaction between scenarios
and groups (F(3,594) = 21.05, p ¡ .001, $\eta^{2}$ = .13). The partial eta
squared indicated that interaction had a stronger impact than the main effect
on justice. All the descriptive statistics for each scenario and the p values
between groups are shown in Table 2. The interaction effect is shown in 1(a).

RQ1 tries to understand if users’ perceived justice was higher or lower for
user-moderated platforms (i.e., Reddit) versus commercially moderated
platforms (i.e., Twitter); we need to specifically compare scenario_1a and 1b,
scenario_1c and 1d, scenario_2a and 2b, and scenario_2c and 2d. The Bonferroni
posthoc tests with simple effects showed a perceived justice difference in
scenario_1c and 1d ( p ¡ .001). Overall, perceived justice is higher for
commercially moderated than for user-moderated platforms in illegal scenarios
with retributive moderation strategies.

H2a stated that perceived justice is higher in illegal than legal scenarios.
The tests of between-subjects effects showed that, in general, there is no
significant difference in perceived justice between illegal and legal
scenarios (p = .487). Bonferroni posthoc tests comparing justice in scenarios
revealed a significant difference in scenarios_b, c, and d; specifically, in
scenario_b, perceived justice was lower in the illegal group compared to the
legal group (p = .002); in scenario_c, perceived justice is higher in the
illegal group compared to the legal group (p = .019); and in scenario_d,
perceived justice is higher in the illegal group compared to the legal group
(p ¡ .001). Overall, the perceived justice was higher in illegal scenarios
compared to legal scenarios with retributive moderation strategies and had
either no difference or was lower in illegal scenarios compared to legal
scenarios with restorative moderation strategies. Thus, H2a was partially
supported.

H3a stated that perceived justice was higher for retributive moderation
strategies than restorative moderation strategies. Specifically, we compare
scenario_a and c to see the different moderation strategies on commercially
moderated platforms and compare scenario_b and d to see the different
moderation strategies on user-moderated platforms. The simple effects for the
interaction revealed that the perceived justice increased significantly from
scenario_a to scenario_c in the illegal group (p ¡ .001) but showed no
significant difference in the legal group (p = 1.00). The simple effects for
the interaction revealed that the perceived justice increased significantly
from scenario_b to scenario_d in the illegal group (p ¡ .001) but showed no
significant difference in the legal group (p = 1.00). Overall, the perceived
justice was higher for retributive compared to restorative moderation
strategies in the illegal group but had no difference in the legal group. H3a
was partially supported. In other words, in illegal scenarios, retributive
moderation is more just; in legal scenarios, there is no justice difference
regarding retributive/restorative moderation.

###  4.2. Perceived Fairness

Mauchly’s Test of Sphericity indicated that sphericity was not met (W = .79,
$\chi^{2}(5)$ = 46.50, p ¡ .001). The tests of within-subjects effects show a
significant main effect among the scenarios (F(2.59, 512.25) = 4.16, p = .009,
$\eta^{2}$ = .02). There was also a significant interaction between scenarios
and groups (F(2.59, 512.25) = 17.38, p ¡ .001, $\eta^{2}$ = .08). The partial
eta squared indicated that interaction had a stronger impact than scenarios on
fairness. All the descriptive statistics for each scenario and the p values
between groups are shown in Table 3. The interaction effect is shown in 1(b).

Table 3. Descriptive Statistics for Each Group and Scenario in Terms of Perceived Fairness | Fairness-a | Fairness-b | Fairness-c | Fairness-d | Fairness-overall
---|---|---|---|---|---
| Scenario_1a | Scenario_1b | Scenario_1c | Scenario_1d |
Group1: Illegal |  M = 4.20, SD = 1.00 |  M = 4.27, SD = 0.96 |  M = 4.39, SD = 0.99 |  M = 4.72, SD = 0.74 |  M = 4.39, SE = 0.08
| Scenario_2a | Scenario_2b | Scenario_2c | Scenario_2d |
Group2: Legal |  M = 4.26, SD = 0.89 |  M = 4.21, SD = 1.01 |  M = 3.65, SD = 1.39 |  M = 3.83, SD = 1.33 |  M = 3.99, SE = 0.08
|  p = .630 |  p = .692 |  p ¡ .001 |  p ¡ .001 |  p ¡ .001

RQ1 tries to understand if users’ perceived fairness was higher or lower for
user-moderated platforms versus commercially moderated platforms. We
specifically compare scenario_1a and 1b, scenario_1c and 1d, scenario_2a and
2b, and scenario_2c and 2d. The Bonferroni posthoc tests with simple effects
showed only a perceived fairness difference in scenario_1c and 1d (p ¡ .001).
Overall, perceived fairness is higher for commercially moderated platforms
than for user-moderated platforms in illegal scenarios with retributive
moderation strategies.

H2b stated that perceived fairness was higher in illegal than legal scenarios.
The tests of between-subjects effects showed that, in general, there was a
significant difference in perceived fairness between illegal and legal
scenarios (p ¡ .001). Bonferroni posthoc tests comparing fairness in scenarios
revealed a significant difference in scenarios_c and d; specifically, in
scenario_c, perceived fairness is higher in the illegal group compared to the
legal group (p ¡ .001), and in scenario_d, perceived fairness is higher in the
illegal group compared to the legal group (p ¡ .001). Overall, the perceived
fairness was higher in illegal scenarios compared to legal scenarios with
retributive moderation strategies, and there was no difference in illegal
scenarios compared to legal scenarios with restorative moderation strategies.
Thus, H2b was partially supported. In other words, moderating illegal
scenarios was fairer than legal scenarios in scenarios with retributive
moderation, but there was no fairness difference regarding legality in
scenarios with restorative moderation.

H3b stated that perceived fairness is higher for retributive than restorative
moderation strategies. We compared scenario_a and c to see the different
moderation strategies on commercially moderated platforms and compared
scenario_b and d to see the different moderation strategies on user-moderated
platforms. The simple effects revealed that perceived fairness showed no
significant difference in the illegal group (p = .760) but decreased
significantly in the legal group (p ¡ .001) from scenario_a to c. The simple
effects revealed that the perceived fairness increased significantly from
scenario_b to d in the illegal group (p ¡ .001) but decreased significantly in
the legal group (p = .012). Overall, the perceived fairness was higher or no
difference for retributive compared to restorative moderation strategies in
the illegal group but lower in the legal group. H3b was partially supported.
In other words, retributive moderation is fairer in illegal scenarios, but in
legal scenarios, restorative moderation is fairer.

##  5\. Discussion

###  5.1. Not Only Restorative But Retributive Moderation Can also Improve
Justice and Fairness

Recent research proposes to move from retributive justice to restorative
justice to mediate the harm and resolve the conflict between violators and
victims (Schoenebeck and Blackwell, 2021). This line of research criticizes
that retributive justice can even cause severe sequential harm to the
stakeholders (Schoenebeck et al., 2021a, b). However, they often consider
online toxicity as a whole and do not consider the nuanced difference of
violation types. In this study, we separate violation from the legality
perspective. Our results consistently show that users consider retributive
moderation strategies to deliver higher justice and fairness for illegal
violations than legal ones, and restorative strategies deliver higher justice
for legal violations than illegal ones. In this sense, we contribute to a
nuanced understanding of violation and perceived justice differences. First,
we supplement the line of restorative justice research (Schoenebeck et al.,
2021a) and point out that it is more about the legal violation related to
online toxicity and harassment.

Second, retributive moderation is still preferred to deal with illegal
violations. Users still think it is fair for illegal violations to receive
severe punishment, such as terrorism and public violence. Speculatively, an
illegal violation is more likely to cause severe consequences for public
safety or society, like content inflaming civil unrest or terrorism. These
types of violations should be punished severely online or even have the police
involved offline. Legal violation is more likely to cause psychological harm
for individual entities, like harassment towards a specific group related to
gender, race, identity, or disability (Lyu et al., 2024; Uttarapong et al.,
2021). Consequently, we suggest that both types of moderation strategies
should be incorporated into the platform’s moderation policymaking, and it is
difficult to weigh which one is better and should be emphasized. Policymakers
should try to differentiate the types of violations. Though the potential
legal violation can sometimes lead to illegal violations at scale, it is
essential to explore the transition between these two types of violations
further and identify the appropriate boundary to intervene with relevant
agencies.

We can also get some clues from the tech giants’ moderation policies about
their different attitudes toward legal or illegal violations. For example,
Facebook, Microsoft, Twitter, and YouTube established the “Global Internet
Forum to Counter Terrorism” in 2017 to coordinate content removal about
“violent terrorist imagery and propaganda”. However, there is little or no
collaboration about daily online harassment. Different platforms have
different policies regarding online harassment, such as community guidelines
and codes of conduct. Such situations also cause challenges from the
policymaking at the platform level and hinder platform collaboration. It seems
there are no clear collaborative solutions for the legal violations since
different platforms hold different values, such as the Stormfront white
nationalist website (Hemphill, 2022). This study also sheds light on
collaborative policymaking to deal with legal violations from some commonly
shared values for most platforms, such as racism and sexism, with zero
tolerance.

###  5.2. Explanation Can Improve Justice and Fairness in Certain Scenarios
But Not All

In line with work about moderation explanation increasing perceived justice
and fairness (Jhaver et al., 2019), we extend and show how the explanation of
moderation decisions across different platforms affects users’ perceived
justice and fairness. Regarding illegal violations, retributive strategies on
commercially moderated platforms deliver higher justice and fairness than
user-moderated platforms. Many scholarships have highlighted the importance of
moderation transparency to improve justice and fairness (Felzmann et al.,
2020; Suzor et al., 2019). Most of them focus on one platform’s moderation
policies. For example, many criticize that commercial moderation lacks
transparency and that community moderation should keep increasing transparency
with clear rules and norms with active engagement (Ma et al., 2023a; Ma and
Kou, 2022). We contribute to the comparison of these types of moderation with
an explanation. Users’ perception of the platform difference after viewing
explanations also plays a significant role in perceived justice and fairness
only in illegal violations with retributive moderation. Offering explanations
with restorative moderation strategies seems to have no platform difference,
even retributive moderation under legal scenarios. In this sense, we provide a
nuanced understanding of the moderation strategies in different scenarios with
platform differences and show that transparency is essential to justice and
fairness in a specific situation, but not all. Such findings supplement prior
work regarding the tension of punishing and nurturing (Jiang et al., 2023) and
offer naunaced differences to consider moderation resource allocation. For
example, with scenarios where the explanation makes no or little difference,
the platform should invest less human labor and resources.

##  6\. Limitation and Future Work

Although this study has important implications for content moderation
policies, it is subject to a few limitations. Firstly, since many people in
the US come from diverse cultures, their cultural biases may be influenced by
living or growing up in the US. Thus, people from the same culture in the US
may hold different cultural views compared to those in their country of
origin. Therefore, future studies should take into account the cultural
differences in users of different countries (Jiang et al., 2021) and
investigate how different perceptions of social media use in other regions
affect content moderation. Secondly, we used a single-item measurement for
fairness and justice, whereas most other fairness and justice research employs
qualitative methods. It is crucial for HCI scholars to develop a scale for
future content moderation studies. Additionally, the preliminary study did not
explore action variables, so it would be beneficial to investigate how users
would like platforms to take action regarding content moderation to make their
decisions more just and fair. Last, there are also potential opportunities to
explore how to incorporate the policy into moderation system design (Yang et
al., 2023) and to reshape the governance and platform identity to make the
platform inclusive and diverse (Das et al., 2021).

###### Acknowledgements.

This research was funded by the National Science Foundation (Award No.
1928627).

## References

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##  Appendix A Experimental Design Process

Figure 2. Experimental design process.

This is a figure to show the experimental design process with two groups of
between-subjects design first, and then within each group, there is a repeated
measure with four different scenarios.

##  Appendix B Scenarios and Main Questions

Warning: The following content might be disturbing.
---

###  B.1. Illegal Scenarios

####  B.1.1. Scenario 1a – Restorative, User, Illegal

A Reddit user posted this message in a subreddit.

> Dear god these fucking snowflakes protest everything. They’re such idiots.
> About to start pulling up to their marches and blow them up to teach them a
> lesson. Who’s coming with?

This user’s message was removed by the moderators of the subreddit. This post
was removed for violating Reddit’s Content Policy Rule 7: “Keep it legal, and
avoid posting illegal content…”. Instigating terrorism is illegal. In
addition, the moderators gave the user a warning saying if they post any more
illegal content, they will be banned from the subreddit.

How fair do you perceive the Reddit moderator’s decision to be? (The question
will repeat after each scenario)

  * •

Very Unfair

  * •

Unfair

  * •

Neutral

  * •

Fair

  * •

Very Fair

Is it necessary to punish the Reddit user for their post to deliver justice?
(The question will repeat after each scenario)

  * •

Absolutely Not Necessary

  * •

Slightly Necessary

  * •

Moderately Necessary

  * •

Very Necessary

  * •

Absolutely Necessary

####  B.1.2. Scenario 1b – Restorative, Commercial, Illegal

A Twitter user posted this tweet to their account.

> Got a new supply of Havendust. Shoot me a DM if you wanna try some of this
> new icing. Local pickup only.

This user’s tweet was removed from the platform by Twitter’s moderators. This
tweet was removed for violating Twitter’s Community Guidelines that state “You
may not use our service for any unlawful purpose or in furtherance of illegal
activities. This includes selling, buying, or facilitating transactions in
illegal goods or services, as well as certain types of regulated goods or
services.” Selling controlled substances is illegal. In addition, the user was
also given a warning  saying if they post any more illegal content, their
account will be suspended from posting on Twitter.

####  B.1.3. Scenario 1c – Retributive, User, Illegal

A Reddit user posted this message in a subreddit.

> If one more fucking leftie posts a damn petition about some stupid ass
> leftie problem, my gun is coming outta the safe and a bullet is going
> through their head. Gonna call my hunting buddies to come with me too. Come
> join us in front of City Hall at 1pm.

This user’s message was removed by the moderators of the subreddit. This post
was removed for violating Reddit’s Content Policy Rule 7: “Keep it legal, and
avoid posting illegal content…”. Instigating terrorism is illegal. In
addition, the moderators permanently banned the user from posting in the
subreddit.

####  B.1.4. Scenario 1d – Retributive, Commercial, Illegal

A Twitter user posted this tweet to their account.

> Took sweet ass bath pictures of a 2-year-old when I was babysitting. DM me
> if you want to see them. Will sell to the highest bidder.

This user’s tweet was removed from the platform by Twitter’s moderators. This
tweet was removed for violating Twitter’s Community Guidelines that state “We
have zero tolerance for child sexual exploitation on Twitter.” Child sexual
exploitation is illegal. In addition, the moderator permanently banned the
user’s account from posting on Twitter.

###  B.2. Legal Scenarios

####  B.2.1. Scenario 2a – Restorative, User, Legal

A Reddit user posted this message in a subreddit.

> Gay people are heathens. They don’t deserve to live and spread their
> disgustingness. Proud of the guy who shot up that gay nightclub. They
> deserved to die.

This user’s message was removed by the moderators of the subreddit. This post
was removed for violating Reddit’s Content Policy Rule 1: “Remember the human.
Reddit is a place for creating community and belonging, not for attacking
marginalized or vulnerable groups of people. Everyone has a right to use
Reddit free of harassment, bullying, and threats of violence. Communities and
users that incite violence or that promote hate based on identity or
vulnerability will be banned.” In addition, the moderators gave the user a
warning saying if they post any more hateful content, they will be banned from
the subreddit.

####  B.2.2. Scenario 2b – Restorative, Commercial, Legal

A Twitter user posted this tweet to their account.

> Feminists are disgusting. They are spreading their filthy ideas that women
> are just as good as men and must be stopped. I wouldn’t mind if they were
> all raped.

This user’s tweet was removed from the platform by Twitter’s moderators. This
tweet was removed for violating Twitter’s Community Guidelines that state “You
may not promote violence against, threaten, or harass other people on the
basis of race, ethnicity, national origin, caste, sexual orientation, gender,
gender identity, religious affiliation, age, disability, or serious disease.”
In addition, the user was also given a warning saying if they post any more
hateful content, their account will be suspended from posting on Twitter.

####  B.2.3. Scenario 2c – Retributive, User, Legal

A Reddit user posted this message in a subreddit.

> Damn leftie sheep believing in fake science and going around wearing masks.
> COVID isn’t real. Take off your masks or I’m gonna cough on you.

This user’s message was removed by the moderators of the subreddit. This post
was removed for violating Reddit’s Content Policy Rule 1: “Remember the human.
Reddit is a place for creating community and belonging, not for attacking
marginalized or vulnerable groups of people. Everyone has a right to use
Reddit free of harassment, bullying, and threats of violence. Communities and
users that incite violence or that promote hate based on identity or
vulnerability will be banned.” In addition, the moderators permanently banned
the user from posting in the subreddit.

####  B.2.4. Scenario 2d – Retributive, Commercial, Legal

A Twitter user posted this tweet to their account.

> Mexican kids at the border should be tear-gassed. They’re coming into our
> country to take our jobs.

This user’s tweet was removed from the platform by Twitter’s moderators. This
tweet was removed for violating Twitter’s Community Guidelines that state “You
may not promote violence against, threaten, or harass other people on the
basis of race, ethnicity, national origin, caste, sexual orientation, gender,
gender identity, religious affiliation, age, disability, or serious disease.”
In addition, the moderator permanently banned the user’s account from posting
on Twitter.