Free Speech and Safe Spaces: How Moderation Policies Shape Online Discussion Spaces


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Article

Introduction

Habermas (1991) articulated the concept of the public 
sphere as a space for private citizens to come together as a 
public to deliberate. In the age of digital connections, the 
Internet has been theorized as a new space for these kinds of 
discussions of civic importance (Papacharissi, 2004; Price, 
Nir, & Cappella, 2006). The accessibility and visibility of 
networked digital media have been considered democratiz-
ing forces that can allow even more citizens to participate in 
the kind of public discussion needed for a democracy (Dylko 
et al., 2012; Papacharissi, 2002). Online discourse has had 
some striking positive effects on politics across the world 
through the last decade, from the Arab Spring uprisings of 
2011 (Lotan, Graeff, Ananny, Gaffney, & Pearce, 2011; 
Wilson & Dunn, 2011) to #MeToo (Hawbaker, 2018; 
Pazzanese & Walsh, 2017). In a comparison of online com-
ments to letters to the editor published in newspapers, 
McCluskey and Hmielowski (2012) found that online com-
ments on a news story were more diverse in viewpoints and 
more likely to challenge institutions than conventional let-
ters to the editor.

However, Internet discussion spaces still face the same 
issue that all other spaces for public discussion in the past 
have faced: how can—and should—these spaces be designed 
to meet the needs of a public sphere? Ever since the creation 
of online spaces for discussion, there has been controversy 
about the extent to which speech should be regulated and 
controlled (Pfaffenberger, 1996). According to a 2018 Pew 
poll, most US teens have experienced cyberbullying, includ-
ing ridicule, threats, or false rumors (Anderson, 2018). 
Recent years have also seen horrific mass violence, such as 
the killing of Muslim minorities in Sri Lanka, as a result of 
misinformation and hate speech spread on online discussion 
spaces (Taub & Fisher, 2018).

In the face of all the potentials and dangers, what kind of 
speech should be allowed in online spaces, and to what end? 

832588 SMSXXX10.1177/2056305119832588Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietyGibson
research-article20192019

Stanford University, USA

Corresponding Author:
Anna Gibson, Department of Communication, Stanford University, 
Building 120, Room 110, 450 Serra Mall, Stanford, CA 94305-2050, USA. 
Email: agibson2@stanford.edu

Free Speech and Safe Spaces: How 
Moderation Policies Shape Online 
Discussion Spaces

Anna Gibson

Abstract
How do moderation policies affect online discussion? This article analyzes nearly a quarter of a million anonymous comments 
over a 14-month period from two online Reddit forums matched in topic and size, but with differing moderation policies 
of “safe space” and “free speech.” I found that in the safe space, moderators removed significantly more comments, and 
authors deleted their own comments significantly more often as well, suggesting higher rates of self-censorship. Looking only 
at relatively low frequency posters, I found that language in the safe space is more positive and discussions are more about 
leisure activities, whereas language in the free speech space is relatively negative and angry, and material personal concerns 
of work, money, and death are more frequently discussed. Importantly, I found that many of these linguistic differences 
persisted even in comments by users who were concurrently posting in both subreddits. Altogether, these results suggest 
that differences in moderation policies may affect self-censorship and language use in online space, implicating moderation 
policies as key sites of inquiry for scholars of democratic discussion.

Keywords
reddit, moderation, online moderation, communication accommodation theory, linguistic style matching, civil discussion, 
self-censorship

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2 Social Media + Society

On its face, this is a normative question for political philoso-
phers. However, it can also be approached empirically. This 
article will investigate one specific comparative case of how 
the values-driven moderation strategies of “safe spaces” and 
“free speech” shape discussion in two Reddit discussion 
forums.

Free Speech and Safe Space

A policy of freedom of speech has been justified through its 
potential to unearth truth and create the social conditions 
necessary for democracy to flourish, as people can make 
informed decisions and check those in power (Ananny, 2018; 
Ash, 2016; Habermas, 1991). Speech is also a defining char-
acteristic of humans, and it has been argued that only through 
total freedom of speech can a person fully explore and under-
stand their own humanity (Ash, 2016, p. 73).

Culturally, freedom of speech from government censor-
ship is dearly held American value; according to a 2015 Pew 
poll, Americans have the strongest support for free expres-
sion in the world (Wike, 2016). The same poll also reports 
that Americans are more tolerant of offensive speech than 
any other country.

A libertarian commitment to total freedom, including 
freedom of speech, has been a central part of Internet culture 
since at least the establishment of Usenet as an alternative to 
ARPAnet in 1979 (Pfaffenberger, 1996; Reagle, 2013). Many 
of the early prominent figures of the Internet advocated a 
“hacker” ethos, which celebrated the liberation of systems 
and information from any form of centralized control 
(Reagle, 2013; Turner, 2006). Early online spaces, such as 
Usenet, reflected that demand for freedom through cultural 
norms of users, as well as the technical affordances of the 
systems themselves (Pfaffenberger, 1996). Users abhorred 
the threat of censorship (Pfaffenberger, 1996) and uplifted 
the idea of the “rational, autonomous individual” (Turner, 
2006). Pfaffenberger (1996) summarizes this attitude as 
“[it’s] up to the individual user, not some committee or 
administrator, to decide what’s worth reading” (p. 369). This 
libertarian view advocates equal access to discursive spaces 
for all without threat of intervention (Fiss, 2009). As the 
president of the American Civil Liberties Union has sug-
gested, in a land of free speech, the solution to hate speech is 
more speech (as paraphrased in Marwick, 2017).

Fiss (2009) argues, however, that there is an inherent 
irony to free speech; even in the most open space, some 
voices will tend to have more access to this “free” discur-
sive space than others because of social and economic hier-
archies. Similarly, Fraser (1990), in her critique of the 
Habermasian public sphere, asserts that the bracketing pro-
cess that Habermas proposes—to leave behind all individ-
ual markers such as class to engage in rational political 
deliberation—is simply impossible and minority groups 
must form their own subaltern counterpublics.

Indeed, there is substantial evidence that many “free” 
online spaces exhibit the sexist and racist tendencies of 
broader culture. Herring (1996) found that even in theoreti-
cally free and neutral spaces, women were discouraged from 
participating in favor of the domination of a small and 
vocal sect of men. Attempts by women to assert their pres-
ence were met by silence or attempts to delegitimize their 
statements (Herring, 1996, p. 486). Gray (2012) documented 
the persistent racist and sexist abuse experienced by 
black female gamers in Xbox Live gaming spaces. Reagle 
(2013) examined why women are disproportionately under-
represented on Wikipedia as compared with other online 
spaces, despite the site’s explicit embrace of a free culture. 
He suggests that free culture in online environments is laden 
with historical and cultural connotations. Thus, online spaces 
labeled as “free” may in fact signpost a space for argumenta-
tive, male-dominated discussion. In a more extreme exam-
ple, Marwick and Lewis (2017) note that “commitments to 
‘free speech’ in certain communities can serve as an on-ramp 
for far-right radicalization” (p. 46).

As Freeman examined in her 1972 paper “The Tyranny of 
Structurelessness,” even when formal structures of power are 
eliminated, informal structures persist and these structures 
tend to benefit some members and punish others. “Contrary 
to what we would like to believe, there is no such thing as a 
‘structureless’ group” (Freeman, 1972, p. 152).

Safe Spaces

The meaning of safe space has been widely contested in aca-
demic literature as well as popular culture (Barrett, 2010; 
Harris, 2015; Stengel & Weems, 2010). Several scholars 
have noted that the metaphor of a safe space is both overused 
and undertheorized (Boostrom, 1998). For the purposes of 
this empirical study, however, I will follow the Roestone 
Collective (2014) in their calls to understand safe spaces 
through their relational work, as sites for “negotiating differ-
ence and challenging oppression.”

In her book “Mapping Gay L.A.,” Kenney (2001) traces 
the development of the safe space discourse around the cre-
ation of radical feminist spaces in the 1970s: “the notion of 
safe space implies a certain license to speak and act freely, 
form collective strength, and generate strategies for resis-
tance” (p. 24). She notes that in this context safe spaces acted 
as “a means rather than an end” for women to find strength 
and community.

The Roestone Collective writes that “the categories of 
safe and unsafe are socially produced and context depen-
dent” (p. 1350) Thus, safe spaces arise in social contexts of 
specific threats to specific groups. In her 2018 ethnographic 
case study of one online safe space, the Facebook group Girl 
Army, Clark-Parsons identifies the need for such spaces in 
the context of rhetoric that seeks to silence women’s partici-
pation. Within these spaces, “marginalized users [can] speak 



Gibson 3

freely, seek support, and organize action against injustices 
faced outside the group’s boundaries” (p. 2127).

Such spaces are never understood as perfectly safe, but 
perpetually doing the work of “negotiating and foreground-
ing difference” (Roestone Collective, 2014). This differs 
from the caricature of such spaces as infantile playgrounds 
(McKee, 2015; @RichardDawkins, 2015). Following the 
work of Foucault (1978), speakers are understood not just as 
neutral voices, but emanating from bodies situated within 
interconnected webs of power: “[power] is not something 
that is acquired, seized, or shared . . . power is exercised from 
innumerable points, in the interplay of nonegalitarian and 
mobile relations.” Thus, the way in which power structures 
discourse and the relative positionality of speakers is salient 
in deciding what kind of language to allow in these spaces.

Policies of safe spaces are thus concerned with preventing 
the marginalization of voices already hurt by dominant 
power relations. This may be implemented through strict no-
tolerance policies of “hate speech” or other discussion that 
would undermine the political project assumed in the space 
of the community. In practice, this often means that people 
can be censored or ejected from a space for not properly 
observing the standards of speech, tone, or style (Clark-
Parsons, 2018). This includes not only hateful statements but 
also ignorantly prejudiced or unintentionally traumatizing 
topics without giving notice to readers in the form of trigger 
or content warnings (Manne, 2015).

Spiral of Silence

In a highly influential 1974 paper, Noelle-Neumann coined 
the term “spiral of silence” to describe why members of the 
public with minority perspectives on value-laden topics 
stayed silent. According to her theory, if individuals perceive 
their opinions to be in the minority, they will refrain from 
expression out of fear of social isolation. This tendency is 
self-reinforcing, as a lack of public support for a minority 
position will discourage others with that opinion from 
expression. Thus, public expression is understood not 
through a Habermasian lens of rational debate, but performa-
tive social interaction (Scheufle & Moy, 2000). The theory 
has been met with limited, but positive, empirical support in 
the pre-digital age (see Scheufle & Moy, 2000, and Glynn, 
Hayes, & Shanahan, 1997 for reviews). Online support for 
the spiral of silence has been found across a variety of digital 
spaces (De Koster & Houtman, 2008; Gearhart & Zhang, 
2018; Hampton et al., 2014; Liu, Rui, & Cui, 2017; Meyer & 
Speakman, 2016; Schulz & Roessler, 2012; Stoycheff, 2016). 
McDevitt, Kiousis, and Wahl-Jorgensen (2003) compared 
discussions about the topic of abortions between individuals 
who were face to face or connected through computer mes-
saging and found that individuals in the computer condition 
were perceived as more moderate.

However, several recent studies suggest that the effects of 
a theorized spiral of silence are only exhibited by specific 

people with regard to specific topics. Hayes, Glynn, and 
Shanahan (2005) developed a scale to measure a construct at 
the individual level they called the “willingness to self-
censor.” In an experiment designed to put participants in a 
spiral-of-silence situation, Hayes, Uldall, and Glynn (2010) 
found that the willingness to self-censor was driven almost 
entirely by those who had scored high on the scale; for indi-
viduals who were not dispositional self-censors, opinion cli-
mate made no difference in opinion expression. A study by 
Matthes, Morrison, and Schemer (2010) examined the role 
of “attitude certainty” with regard to the spiral of silence and 
concluded that individual differences in the strength of atti-
tude affected susceptibility to a spiral of silence.

Topic, too, appears to make a difference. Gearhart and 
Zhang (2018) found that the degree to which a topic has been 
an enduring, emerging, or transitory subject of public debate 
affected the willingness of individuals to voice minority 
opinions. Porten-Cheé and Eilders (2015) failed to find sup-
port for the spiral of silence, but conceded that their chosen 
topic of public discussion, climate change, actually had little 
to no moral conflict in its German context.

Neubaum and Krämer (2018) propose that the social envi-
ronment in which the opinion is expressed may also affect 
the tendency of individuals to be susceptible to the spiral of 
silence. Different social environments may yield different 
expected sanctions for transgressions, changing the situa-
tional fear of isolation and correspondingly changing the 
stakes in deciding to share an opinion perceived to be in the 
minority.

Moderation Policies

The history of online discussion is replete with stories of 
users deliberately deceiving (Van Gelder, 1985) and hurting 
(Dibbell, 1993) fellow forum users. Many online discussion 
spaces have some sort of moderation policies and/or struc-
ture in place to address the needs of users (Gillespie, 2018; 
Grimmelmann, 2015).

Like other spaces for debate, online forums also use mod-
erators—usually a computer program and/or a designated 
person—to determine and enforce the baseline rules of dis-
cussion. Moderators play an important role in preventing dis-
ruptive users like trolls or spam from taking over forums 
(Brunton, 2013). Moderator powers often include the ability 
to screen, modify, and delete comments, or ban users (Matias, 
2016). Consequently, moderators have more power to affect 
the discussion in online forums than other forum partici-
pants. Therefore, the forum policies established by modera-
tors, and the effects they have on discussion or spirals of 
silence, are important to investigate and understand.

There have been many studies of what kinds of online 
community structures facilitate community growth and lon-
gevity (see, for example, Aumayr & Hayes, 2017; Hinds & 
Lee, 2008; Lin et al., 2007; Wagner, Liu, Schneider, 
Prasarnphanich, & Chen, 2009), but this is the first study to 



4 Social Media + Society

use a matching methodology to compare effects of differ-
ences in moderation policy.

Method

Language Accommodation Theories

Speakers adjust their communication within different groups 
and contexts with respect to self and group identity 
(Dragojevic, Gasiorek, & Giles, 2015). Such accommoda-
tion, often understood through the framework of communi-
cation accommodation theory (CAT), happens both at a 
conscious level and at an unconscious level. Language is 
often used to negotiate in-group social identity and is per-
ceived in accordance with local sociocultural norms around 
language. A person who wants to be seen as a member of the 
“in-group,” for example, may consciously or unconsciously 
adopt the group’s linguistic features, such as intonation, 
accent, or word usage. Such changes are examples of accom-
modation. On the other hand, a person who is trying to dis-
tance themselves from the group may emphasize linguistic 
features that differ from the norms of the group; this is lin-
guistic divergence. In addition, linguistic accommodation 
may serve an instrumental end, such as increasing communi-
cative efficiency (Dragojevic et al., 2015).

Linguistic style matching (LSM), as defined by 
Niederhoffer and Pennebaker (2002), builds on CAT. One 
prominent part of LSM is that within groups, word use cova-
ries to reflect how “in-sync” speakers are. The mechanism in 
this situation is priming: the words one speaker uses prime 
the other’s response.

LIWC, or Linguistic Inquiry and Word Count, sorts and 
displays the words present in text into a variety of categories 
by frequency (Tausczik & Pennebaker, 2010). The word 
types included linguistic categories (e.g. word count, articles, 
and prepositions), social/affect categories (e.g. first-person 
singular pronoun usage, positive, and negative emotion), and 
cognitive categories (e.g. tentative, certainty). This tool has 
been shown as an effective way to study how groups con-
verge under LSM using transcripts of conversations (Ireland 
et al., 2011; Niederhoffer & Pennebaker, 2002; Scissors, 
Gill, Geraghty, & Gergle, 2009; Scissors, Gill, & Gergle, 
2008) as well as text from computer mediated writing and 
discussion (Gonzales & Hancock, 2008; Gonzales, Hancock, 
& Pennebaker, 2010; Ireland & Pennebaker, 2010).

Because LIWC analyzes categories of invisible structural 
“style” words, such as pronouns, that are theoretically rich and 
clinically tested but may otherwise go unnoticed, LIWC pro-
vides a method to study group language convergence at mass 
scale in an unbiased and meaningful way (Pennebaker, 2011).

Object of Study

According to Alexa.com, as of 18 October 2017, Reddit is 
the eighth most popular website globally, ranking above 

Amazon and Twitter. In the United States, it is the fourth 
most popular, below only Google, YouTube, and Facebook.

The site is organized into communities, called subreddits, 
which users can subscribe to or visit (“About,” n.d.). Users 
with accounts can post text, links, or images into these sub-
reddits and also comment in response to the posts. Anyone 
can create an account without an email address and begin 
posting and commenting immediately. Users tend to remain 
anonymous or use pseudonyms and connect with strangers 
rather than friends or family (Bergstrom, 2011; Lamont, 
2014; Shelton, Lo, & Nardi, 2015). Redditors can vote posts 
and comments “up” or “down” which will affect the post or 
comment’s public score and subsequently how easily other 
redditors and the general public will see that post or com-
ment (Grimmelmann, 2015).

The structure of Reddit into subreddits makes it an inter-
esting site for study because there is not a uniform policy of 
moderation across the site. The company takes a hands-off 
stance with regard to content in favor of letting individual 
subreddits make and enforce their own moderation policies, 
as detailed in a blog post from 2014 titled “Every Man Is 
Responsible for His Own Soul”:

We uphold the ideal of free speech on reddit as much as possible 
not because we are legally bound to, but because we believe that 
you—the user—has the right to choose between right and 
wrong, good and evil, and that it is your responsibility to do so. 
When you know something is right, you should choose to do it. 
But as much as possible, we will not force you to do it.

You choose what to post. You choose what to read. You choose 
what kind of subreddit to create and what kind of rules you will 
enforce. We will try not to interfere—not because we don’t care, 
but because we care that you make your choices between right 
and wrong.

The established norms of acceptable discourse in any 
given subreddit can vary wildly; moderators create and post 
rules and then are expected to enforce those policies accord-
ingly. Reddit users can join or leave communities in reaction 
to those policies. Therefore, it can be posited that moderation 
policies act as an independent factor in the study of subreddit 
discourse. Studying the differences in discourse between 
subreddits with differing moderation policies may therefore 
provide evidence of the effects of those policies on com-
menter discourse.

Of course, there are many variables that may affect dis-
cussion within a subreddit: size, topic, and relative visibility 
are some of the most prominent factors. All of these may 
have much more profound effects on the observed measures 
of discourse than moderation style. Therefore, any investiga-
tion of subreddit discourse will need to account for these 
confounding variables. To compare the effects of moderation 
policy, I will use a matching strategy and analyze two sub-
reddits that are alike in almost every way except for modera-
tion policy.



Gibson 5

Subreddits in this Study

The two subreddits chosen for study in this article are r/lgbt 
(www.reddit.com/r/lgbt) and r/ainbow (www.reddit.com/r 
/ainbow).

According to RedditList.com (n.d.), as accessed 18 
October 2017, r/lgbt has approximately 170,000 subscribers, 
and r/ainbow has approximately 49,000 subscribers. For 
context, of the over 4,000 subreddits tracked by RedditList.
com, the top 100 subreddits have over 650,000 subscribers 
each. The median subreddit (r/grilling, ranked 2, 135th) only 
has approximately 24,000 subscribers. Even though these 
sizes are not a perfect match, they are acceptable relative to 
the spread of community sizes on Reddit.

Both subreddits have similar self-descriptions, identify-
ing themselves as spaces for lesbian, gay, bisexual, and trans-
gender (LGBT) and other to have discussions. The r/lgbt 
subreddit defines itself in the following way:

This subreddit is by and for people who are Gender and Sexual 
Minorities (GSM), including but by no means limited to LGBT 
(Lesbian, Gay, Bisexual and Transgender) people, and respect 
for our diversity and experiences is paramount. All are welcome 
to participate who agree to follow the rules outlined below and 
in: The r/lgbt FAQ

It also boasts a “Safe Space” badge icon on its sidebar.
The r/ainbow subreddit posts this self-description:

A free area for the discussion of issues facing those who identify 
as gay, lesbian, bisexual, transgender, and all other sexual or 
nonsexual orientations and/or gender identities. Post links to 
articles, self-posts, photographs, experiences and whatever else 
is important to your experience of queer life. We encourage you 
to treat others with respect, start and/or engage in robust 
discussion and interact with the community. The more we know 
each other, the better we’ll get along.

These the two subreddits explicitly define the ideological 
bases for their policies and then link to each other as alterna-
tive, making them excellent sites for studying the effects of 
moderation policy. For example, r/lgbt explicitly bills itself 
as a safe space and links to r/ainbow as an alternative, less 
moderated space:

This is a safe space. Anyone can make a mistake and accidentally 
say something hurtful or triggering. If you find yourself 
corrected for making this error, please try to learn from it. This 
is not a place to tell people that they need to reclaim a pejorative 
so you can use it, that they should laugh at jokes about them, or 
that they otherwise just ‘shouldn’t be so sensitive.’ For lightly 
moderated LGBT-related discussion, I recommend /r/ainbow. /r/
ainbow does not moderate discussion, but the community will 
expect that you treat them with respect. For more information, 
see /r/ainbow’s FAQ.

Similarly, r/ainbow describes itself a free speech area and 
links to r/lgbt as an alternative, more moderated space:

This subreddit is lightly moderated. The community actively 
self-moderates offensive comments with downvotes, but 
comments are generally not removed except for violations of 
site-wide guidelines and as outlined below. If you prefer a more 
hands-on approach, try /r/lgbt. /r/lgbt requires trigger warnings, 
and removes comments and users for violations of their rules, 
which are detailed in their FAQ.

This subreddit is a free speech zone . . . It is also lightly 
moderated, which means that it’s up to you the community to 
downvote offensive posts and comments, and upvote 
constructive content. Please use your voting and posting powers 
to create the community you want to see.

In an exploratory study, there were very few other simi-
larly paired subreddits. Often, it appeared that users who felt 
they were being censored started free speech subreddits in 
reaction to new policies in larger subreddits. Such new free 
speech subreddits, in addition to being smaller than the origi-
nal subreddit by several orders of magnitude, generally cen-
tered reactionary politics and rejected the safe-space 
subreddits as being compromised by “SJWs” (frankenmine, 
2015) or Social Justice Warriors, a pejorative term ascribed 
to individuals concerned with identity politics or feminism 
(Massanari & Chess, 2018). In contrast, the subreddits stud-
ied here, r/lgbt and r/ainbow, have relatively more similar 
political standpoints, activity, and subscribers.

Research Questions

Comment Deletion

Heavily moderated spaces entail more moderator interven-
tion, including deleting offending comments. Along these 
lines, I wanted to explore whether moderators will delete 
more comments in a safe space than the free speech subred-
dit. This might also manifest as more pressure for users to 
delete their own comments, either due to intrinsic motiva-
tions like personal shame, or extrinsic factors, like other 
users downvoting that comment overwhelmingly. That is, I 
suspect more moderation to lead to not only greater amounts 
of moderator intervention, specifically to remove comments, 
but also to have that reflected in users’ own self-monitoring 
and self-censorship of their own comments.

Research Question 1a. Do moderators delete more com-
ments in r/lgbt, the safe space, than r/ainbow, the free 
speech space?
Research Question 1b. Do more users delete their own 
comments in r/lgbt, the safe space, than r/ainbow, the free 
speech space?

Participation

When a user creates an account Reddit, he or she is automati-
cally “subscribed” to a number of popular subreddits, meaning 

www.reddit.com/r/lgbt
www.reddit.com/r/ainbow
www.reddit.com/r/ainbow


6 Social Media + Society

posts from those subreddits will comprise that user’s Reddit 
homepage. A user can then change what subreddits’ posts 
they want to see by either subscribing to or unsubscribing 
from as many subreddits as they want. Not all users who sub-
scribe to a subreddit will post or ever even comment in that 
subreddit, remaining “lurkers” who watch without interact-
ing through text. I was curious to see if there was a difference 
between the two subreddits in the proportion of users who 
commented in that subreddit.

As discussed in the literature review, safe spaces are con-
cerned with minimizing the structural barriers that prevent 
individuals in different positions of power from participating 
in discussions. Thus, if safe space moderation policies are 
successful, a greater proportion of the subscribers for the 
safe space subreddit will actually participate in the subreddit 
by writing a comment. Conversely, it could be argued that 
the heightened standards for entry in a safe space dissuade 
individuals who are already hesitant about participation.

Research Question 2. How do the safe space, r/lgbt, and 
the free speech space, r/ainbow, differ in overall participa-
tion rates by subscribers?

Language Use

Free speech and safe spaces have been characterized by the 
ways in which participants interact; discussion in free speech 
spaces has been described as overly rational and argumenta-
tive (Reagle, 2013), and discussion in safe spaces as overly 
emotional, deferent, and meek (Lukianoff & Haidt, 2015; 
Massanari & Chess, 2018). To explore whether these differ-
ences were real, I decided to analyze language use using 
LIWC2015 (Pennebaker, Boyd, Jordan, & Blackburn, 2015).

The LIWC2015 program can analyze 90 different vari-
ables for each text, and thus I had many categories through 
which I could compare the two subreddits. Rather than sim-
ply going through all 90 variables and looking for differ-
ences, I chose to focus in on a few categories that pertain 
specifically to interpersonal attitude and boundary negotia-
tion, as I believed these would most starkly draw out linguis-
tic differences between the communities. I chose to examine 
five categories of language from LIWC: summary variables, 
linguistic dimensions, affective processes, personal con-
cerns, and informal language.

Word choice can be used to indicate the attentional focus, 
or the “gaze” of a speaker, and therefore identifying common 
themes in words can serve to reflect the speaker’s mental 
state (Tausczik & Pennebaker, 2010). For example, as indi-
viduals identify more and more with a group, they will shift 
from using first-person singular pronouns (“I” and “me”) to 
first-person plural pronouns (“we” and “us”). First-person 
plural pronouns, however, can also be used to insinuate dis-
tance, as in an exclusive we-and-not-you, Royal-we, or we-
as-those-who-agree-with-me (“We need to make America 
great again”). Thus, the usage frequency of first-person 

pronouns is not only a reflection of group identity but also 
status and arrogance, among other traits (Pennebaker, 2011; 
Pennebaker & Lay, 2002).

Research Question 3. How does language use differ 
between r/lgbt, the safe space subreddit, and r/ainbow, the 
free speech subreddit?

Crossposters

To test whether these differences arise because of author 
traits or contextual cues in the individual subreddits, I 
decided to isolate a subset of comments created by authors 
who had concurrently posted in both subreddits. I can then 
examine whether these comments by “recent crossposters” 
exhibited the same kinds of differences in linguistic markers. 
If these differences persist in this subset of comments, it 
would provide evidence that language differences are influ-
enced by contextual language usage rather than innate author 
characteristics.

Research Question 4. Do users who post in both the free 
speech and safe space subreddits show differences in the 
language they use in each subreddit?

Data Analysis

A total of 2,76,574 comments from r/lgbt and r/ainbow made 
between 1 June 2016 and 31 July 2017 (inclusive) were 
downloaded from the BigQuery Reddit repository. These 
dates were chosen to include a number of recent American 
political events relevant to the LGBT community which 
might spark civic discussion, including the Orlando Pulse 
massacre (12 June 2016), the American presidential election 
(4 November 2016), and the presidential announcement 
through a tweet to exclude transgender individuals from the 
military (@realDonaldTrump, 2017). For each comment, the 
initial data set contained the comment author, subreddit, 
month and year posted, and full body text.

Data Cleaning

A “bot” is a pre-programmed reddit user that posts a com-
ment when triggered by a certain type of comment made by 
another user. Various bots do such diverse things as tran-
scribe comic strips (u/ImageTranscribingBot), fix common 
spelling errors (u/CommonMisspellingBot), or post pictures 
of cats for sad people (u/ThisCatMightCheerYou). Because 
bots post indiscriminately, they were excluded from the data 
set for comment analysis.

There is no fixed list of bots, so I created my own list to 
exclude comments from those authors. First, all usernames 
and comments from the sample containing the word “bot” 
were examined to find posting patterns that corresponded to 
bot behavior, including multiple postings in a row of the 



Gibson 7

same or similarly formatted post. Suspected bots were fur-
ther researched by looking up their username on Reddit and/
or archived Reddit pages. This yielded a list of 62 bots and 
covered 1,170 comments in my sample.

Next, I compiled a list of alleged bots from Goodbot-
Badbot’s voting site (Good Bot, Bad Bot). This list is automati-
cally created through user votes and potentially contained false 
positive bot identifications. The list contained 1,116 bot names 
and covered 1,196 comments in my sample. Only 33 usernames 
appearing in the Goodbot-Badbot list were both in my data set 
and not on my previous list of bot names. These suspected bots 
were further investigated and yielded an additional 24 bots.

In total, my list of bots from my sample contained 86 
usernames, and these bots created 1,168 comments. A total 
of 652 were in r/lgbt and 516 were in r/ainbow. After 
removing these comments, my data set contained 2,75,406 
comments.

In my remaining data set of comments, many comment 
entries were incomplete. If a user deletes their comment or if 
that comment is removed by a moderator, Reddit indicates 
that this action has taken place by replacing the body of the 
comment as either “[deleted]” or “[removed],” respectively. 
If that action took place between the creation of the comment 
and the archiving of the comment into the BigQuery Reddit 
repository, the body of the comment is preserved as only 
“[deleted]” or “[removed].”

Many users go farther, by not just deleting their com-
ments, but by deleting their user accounts as well. In this 
case, username attached to the comment will be replaced by 
“[deleted].” It is not uncommon to find vestigial comments 
in comment threads with a comment that says “[deleted]” 
posted by a “[deleted]” author.

My data set thus also contained many of these “null” com-
ments. Because they are not useful for a linguistic analysis, I 
separated these comments into a separate data set.

Creating the Data Set of Crossposters

In the remaining set of 2,45,146 comments, 3,339 had an 
author who deleted his or her account. Therefore, I cannot 
know whether or not the author of the comment was a cross-
poster. I therefore excluded those 3,339 authorless comments 
for the following analysis, leaving a total of 241,807 com-
ments in the corpus.

To identify comments as being made by a crossposter, I 
filtered the corpus at both the author and comment level. I 
identified three levels at which a comment could be identi-
fied as being authored by a crossposter, here numbered from 
least strict to strictest:

Level 1. Filtered at the author level. The comment’s 
author posted in both r/ainbow and r/lgbt at some point 
within the 14-month sample window.
Level 2. Filtered at the author level. The comment’s 
author posted in both r/ainbow and r/lgbt in the same cal-
endar month at some point within the 14-month sample 
window.
Level 3. Filtered at the comment level. The comment was 
written in the same calendar month as another comment 
by the same author in the other subreddit.

At level 1, 43.4% of comments qualified as being written 
by a crossposter, at level 2, 39.6%, and at level 3, my strictest 
level, only 26.5% of comments qualified. To maximize con-
textual posting validity, I chose to classify comments as hav-
ing been authored by a crossposter if and only if they 
qualified under level 3. This subset thus consists of 22.1% of 
comments in r/lgbt and 34.6% of comments in r/ainbow. 
Please refer to Tables 1 and 2 for a detailed breakdown of the 
number of authors and comments in each subreddit and level.

Results

Research Questions 1a and 1b: Comment 
Removal and Deletion

There were a total of 13,680 comments with the body 
“[deleted].” Of those, only three comments had a username 
attached to them, and the rest had username “[deleted].” Of 
the 13,680 comments deleted by the author, 9,228 were 
posted in r/lgbt, the safe space, and 4,452 were posted in r/
ainbow, the free speech space (see Table 3). A chi-square test 

Table 1. Authorship in Subreddits.

r/lgbt  
(safe space)

r/ainbow  
(free speech)

Total authors 31,209 9,215
Authors who posted
 Only in one subreddit 27,787 (89.0%) 5,793 (62.9%)
 Some point in both 3,422 (11.0%) 3,422 (37.1%)
 Both in same month 2,154 (6.90%) 2,154 (23.4%)

Table 2. Relative Size of Comment Groups.

Subreddit Total 
comments

Level 1 Level 2 Level 3

No. of comments Total (%) No. of comments Total (%) No. of comments Total (%)

r/lgbt (safe space) 156,686 57,288 (36.6) 52,419 (33.5) 34,689 (22.1)
r/ainbow (free speech) 85,121 47,595 (55.9) 43,268 (50.8) 29,449 (34.6)
Total 241,807 104,983 (43.4) 95,687 (39.6) 64,138 (26.5)



8 Social Media + Society

indicated that the difference in proportion of deleted com-
ments is significant, χ2 (1) = 5.52 and p = .019.

A total of 16,580 comments had the body text body 
“[removed].” Of those, only three comments had a user-
name attached to them, the rest had username “[deleted],” 
indicating a ban by moderators. Of the 16,580 comments 
removed by moderators, 14,910 were posted in r/lgbt, the 
safe space, and 1,670 were posted in r/ainbow, the free 
speech space (see Table 4). A chi-square test indicated that 

the difference in proportion of removed comments is signifi-
cant, χ2 (1) = 3,937.6 and p < .001.

In summary, I found significant differences in self-dele-
tion and moderator removal of comments between the two 
subreddits, as summarized below in Table 5.

Research Question 2: Participation

To examine participation, I first counted the number of 
unique authors per month in each subreddit from my com-
ment corpus.

As illustrated in Figure 1, the number of unique authors 
spiked in both subreddits in November 2016, when the 
American presidential election took place. The number of 
unique authors also spiked in the free speech space r/lgbt in 
January 2017, when President Trump was sworn into office, 
and in June 2017, gay pride month in the United States.

To track subscribers in the subreddits over time, I used the 
Internet Archive’s Wayback Machine. In my 14-month date 
range, I found 50 archived versions of the safe space r/lgbt 
and 28 archived versions of the free speech space r/ainbow. 
Each archived page displayed the number of subscribers to 
the subreddit on the day on which the page was archived.

From those data of dates and subscribers, I performed 
simple linear regressions to model the rate at which each 
subreddit gained subscribers, predicting the number of 
subscribers over time. Both communities grew at almost 

Table 3. Comment Deletion Rate in Subreddits.

Subreddit Comments 
deleted by author

Total 
comments

Comments 
deleted (%)

r/lgbt  
(safe space)

9,228 183,093 5.0

r/ainbow  
(free speech)

4,452 92,313 4.8

Table 4. Comment Removal Rate in Subreddits.

Subreddit Comments removed 
by moderator

Total 
comments

Comments 
removed (%)

r/lgbt  
(safe Space)

14,910 1,83,093 8.1

r/ainbow  
(free Speech)

1,670 92,313 1.8

Table 5. Summary Statistics of Deletion and Removal Rate in Subreddits.

r/lgbt (safe space) r/ainbow (free speech) χ2 test p

Comments deleted by author 9,228 (5.0%) 4,452 (4.8%) χ2(1) = 5.52 .019**
Comments removed by moderator 14,910 (8.1%) 1,670 (1.8%) χ2(1) = 3,937.6 <.001***

Figure 1. Number of unique authors in each subreddit per month June 2016–July 2017.



Gibson 9

perfectly linear rates with significant regression results. In 
the safe space r/lgbt, F(1, 48) = 2332, with an R2 = 0.98, 
p < .001. In the free speech space r/ainbow, F(1, 26) = 10,330 
with an R2 = 0.998, p < .001.

I then estimated the number of subscribers for each sub-
reddit using the first day of each month to represent the esti-
mated subscribers for that month. (That is, the estimate for 
subscribers in June is based on the number of subscribers on 
1 June.) I then divided the number of unique authors in that 
month by the number of subscribers to see what proportion 
of subscribers contributed a comment. The relative participa-
tion in each subreddit is graphed in Figure 2. Averaged by 
month over my 14-month window, 2.92% of subscribers 
commented in safe space r/lgbt and 3.33% of subscribers 
commented in free speech space r/ainbow. Overall, I found 
no significant difference in authorship rates between the two 
subreddits, t(23.9) = –1.65, p = .11.

Research Question 3: Language Use Across 
Subreddits

I used LIWC2015 to compare linguistic measures between 
comments in the two subreddits over my 14-month sample. 
LIWC2015 classifies the percentage of words in each text 
file that fit into predetermined and validated categories 
(Pennebaker et al., 2015). Each comment body was pro-
cessed as a separate file. As the category percentages would 
be especially high for short comments, I excluded all com-
ments with a word count less than 10 from the data set (fol-
lowing Ireland & Iserman, 2018).

Like many online spaces, very few users are responsible 
for a large percentage of comments (Sun, Rau, & Ma, 2014; 
Van Mierlo, 2014). In this sample, the top 1% most prolific 
comment authors created more than one-third of the total 
comments collected (34.5%), and the top 10% wrote two-
thirds of the total comments (66.8%).

Thus, to create a normal distribution of comments and 
prevent the possibility that this small percentage of users was 
overly skewing the results, I removed comments by the top 
9.44% most prolific posters from the data set, including com-
ments by authors who deleted their accounts, leaving only 
named users who posted 10 or fewer comments over the 
14-month sample (N = 30,734 authors).

To analyze the data, I constructed linear mixed models fit 
by REML for each linguistic dimension to minimize error 
arising from individual differences (Barr et al., 2013) using 
the lme4 package for R (Bates, Maechler, Bolker, & Walker, 
2015). In each model the estimate for the LIWC measure 
( )Ysi  was calculated using subreddit as a fixed effect ( )β0  
and comment author as a random effect ( )S s0 , thus allowing 
intercepts to vary by author. I constructed a model that would 
allow slopes to vary as well, but the model did not converge 
for all data. The simpler model, below, was therefore used 
for all estimates in Tables 6 and 7. The t-test results were 
calculated using Satterthwaite’s method

Y S X esi s i si= + + +β β0 0 1 ( )equation from Barr et al., 2013

Hypothesis 4: Language Use Among Crossposters

The same statistical tests were then run using only the “cross-
poster” subset of the data analyzed above.

Discussion

First, I found significant differences in moderator deletion 
rates between the two subreddits, with the safe space subred-
dit, r/lgbt, demonstrating a higher rate of both post removal 
by moderators and users deleting their own comments. This 
result affirms that moderators effectively implemented dif-
fering moderation policies in my sample.

Figure 2. Proportion of subscribers who authored comments June 2016–July 2017.



10 Social Media + Society

I operationalized spiral of silence effects slightly differ-
ently than they have been treated in the literature, a distinc-
tion captured by the difference between expressive and 
withdrawal behaviors as defined in Chen (2018). Generally, 
spiral of silence predicts a limit of expressive behaviors, that 
is, the decision to limit future posting. However, my data 
only allows me to observe withdrawal behaviors, that is, 
deleting comments that have already been posted.

The results described above suggest a greater spiral of 
silence in the safe space subreddit, as we see a significantly 
greater proportion of comment deletion. However, this must 
be tested experimentally, as the spiral of silence may also be 

affected by individual differences (Gearhart & Zhang, 2018) 
and social norms (Neubaum & Krämer, 2018), and we have 
no way to know how individuals self-sorted into these sub-
reddits. The difference in deletion rate may be an effect of 
the population of the safe space having a relatively higher 
willingness to self-censor, or it may be that the social sanc-
tions in the safe space for minority opinions are relatively 
more severe than in the free speech space. In either case, the 
relationship between ideology and self-censorship should be 
further investigated.

I did not find any significant difference in participation 
rates between the two subreddits. This result is interesting, 

Table 6. Comments by Lower 90% of All Non-deleted Authors.

LIWC measure r/lgbta (safe space) r/ainbowb (free speech) t p

Summary variables
 Word count 52.47 54.65 2.78 **
 Words/sentence 15.09 14.73 3.79 ***
Linguistic dimensions
 1st person singular 4.34 3.62 −13.67 ***
 1st person plural 0.59 0.69 5.46 ***
 2nd person 2.57 2.35 −5.30 ***
 3rd person singular 1.25 1.27 0.59  
 3rd person plural 1.15 1.17 1.10  
 Impersonal pronouns 6.98 6.67 −6.02 ***
 Articles 5.84 6.06 4.97 ***
 Prepositions 11.78 11.91 2.31 *
 Auxiliary verbs 10.88 10.64 −4.21 ***
 Common adverbs 5.91 5.77 −1.34  
 Conjunctions 6.27 6.03 −5.50 ***
 Negations 2.66 2.75 2.65 **
Affective processes
 Overall affect 6.96 6.74 –3.81 ***
 Positive emotion 4.25 3.64 –12.01 ***
 Negative emotion 2.54 2.92 10.20 ***
 Anxiety 0.35 0.36 0.54  
 Anger 1.06 1.30 9.56 ***
 Sadness 0.35 0.34 –0.46  
Personal concerns
 Work 1.56 1.77 6.78 ***
 Leisure 0.82 0.78 –1.98 *
 Home 0.23 0.23 –0.20  
 Money 0.43 0.48 3.21 **
 Religion 0.62 0.64 1.16  
 Death 0.17 0.21 4.80 ***
Informal language
 Swear words 0.52 0.63 6.05 ***
 Netspeak 0.97 0.90 –2.59 **
 Assent 0.46 0.44 –1.32  
 Nonfluencies 0.28 0.26 –1.84  
 Fillers 0.05 0.04 –2.19 *

LIWC: Linguistic Inquiry and Word Count.
aN = 49,393 comments.
bN = 14,356 comments.
***p < .001; **p < .01; *p < .05.



Gibson 11

but ultimately inconclusive in this context, as I theorized 
that participation might be driven by inherent inclusiveness 
of safe spaces or salience of group identification. Either 
mechanism, or both, might be at play here. In the future, 
studies that carefully control for safe space moderation 
policy as well as salience of group identity may be able to 
find a more specific mechanism. Participation may also be 
more carefully studied over shorter periods of time for 
more granularity, which might reveal more meaningful 
results. The participation rates were not found to have a 
main effect, but rather spiked at various times. This could 
be further studied to understand what kind of comments 

were being made in each community at the times of these 
spikes.

The statistical models revealed many significant differ-
ences in language use between r/lgbt and r/ainbow. First, 
there were many differences along linguistic dimensions. 
Users in the safe space used more first-person singular pro-
nouns (I or me), second person pronouns (you), impersonal 
pronouns, auxiliary verbs, and conjunctions. In the free 
speech space, users used more words in every comment, 
more words per sentence, more first-person plural pronouns 
(we or us), more articles, more prepositions, and more nega-
tions. There were also differences in affect. In the safe space, 

Table 7. Comments by Crossposters in Lower 90% of All Non-deleted Authors.

LIWC measure r/lgbta (safe space) r/ainbowb (free speech) t p

Summary variables
 Word count 51.19 52.49 0.89  
 Words/sentence 15.51 15.64 0.66  
Linguistic dimensions
 1st person singular 3.93 3.54 −3.80 ***
 1st person plural 0.59 0.72 3.26 **
 2nd person 2.32 2.19 −1.51  
 3rd person singular 1.16 1.33 2.45 **
 3rd person plural 1.20 1.19 −0.25  
 Impersonal pronouns 6.89 6.63 −2.47 *
 Articles 5.94 6.09 1.52  
 Prepositions 11.80 11.97 1.41  
 Auxiliary verbs 10.73 10.60 −1.07  
 Common adverbs 5.91 5.77 −1.34  
 Conjunctions 6.22 6.01 −2.14 *
 Negations 2.65 2.79 2.01 *
Affective processes
 Overall affect 6.51 6.69 1.45  
 Positive emotion 3.95 3.73 –2.21 *
 Negative emotion 2.50 2.89 4.93 ***
 Anxiety 0.31 0.37 2.24 *
 Anger 1.09 1.31 4.27 ***
 Sadness 0.32 0.31 –0.30  
Personal concerns
 Work 1.63 1.77 0.03 *
 Leisure 0.87 0.78 –1.74  
 Home 0.25 0.25 –0.31  
 Money 0.41 0.46 1.70  
 Religion 0.61 0.61 0.17  
 Death 0.19 0.21 0.96  
Informal language
 Swear words 0.53 0.61 0.05 *
 Netspeak 0.99 0.94 –0.87  
 Assent 0.43 0.46 0.75  
 Nonfluencies 0.27 0.26 –0.44  
 Fillers 0.04 0.04 –0.34  

LIWC: Linguistic Inquiry and Word Count.
aN = 5,135 comments.
bN = 4,176 comments.
***p < .001; **p < .01; *p < .05.



12 Social Media + Society

users used more overall affect, driven by a higher use of posi-
tive emotion words. In the free speech space, authors used 
more negative emotion words and more words indicating 
anger. The range of personal topics discussed differed 
between the two spaces. In the safe space, users discussed 
leisure relatively more frequently, whereas in the free speech 
space, users were more likely to discuss work, money, and 
death. Finally, there were also differences in informal lan-
guage. Users in the safe space were relatively more likely to 
use netspeak and fillers, while in the free speech space, users 
used more swear words.

After identifying these differences, I isolated the com-
ments of crossposters to identify which linguistic tendencies 
were driven by space rather than individual differences. 
Among the crossposters, most of the language difference 
persisted. Crossposters in the safe space were more likely to 
use first-person singular pronouns, impersonal pronouns, 
and conjunctions, while in the free speech space they were 
more likely to use first-person plural pronouns, third person 
singular pronouns, and negations. Crossposters were also 
more likely to use positive emotion words in the safe space, 
but in the free speech space use negative emotion words, 
anxiety-related words, and anger-related words. They were 
also more likely to discuss work and use swear words in the 
free speech space.

Pronoun use has been linked to understanding of one’s 
position in social hierarchies, with lower-status individuals 
using “I” much more frequently, and high-status individuals 
using “we” more frequently (Kacewicz, Pennebaker, Davis, 
Jeon, & Graesser, 2014). Difference in pronoun usage 
between these subreddits may reflect normative positions 
about the proper relationship of the individual to the group at 
large. For example, in safe spaces, in accordance with pro-
cesses of relational negotiation as discussed above, individu-
als are encouraged to not make assumptions about groups of 
other people; speaker gaze, therefore, should focus more on 
oneself rather than the group at large, leading to more use of 
the singular first-person pronoun. Correspondingly, there is 
no such norm in free speech spaces, and individuals are given 
free rein to express how they believe wide swaths of people 
and groups behave, leading to more use of the plural first-
person pronoun.

Altogether, these results suggest that implemented mod-
eration policies were able to effectively set norms around 
style, affect, and topic. In our sample of thousands of infre-
quent posters, we found prominent trends. Generally, lan-
guage in the safe space is more positive and discussions are 
more about leisure activities. Language in the free speech 
space is relatively negative and angry, and material personal 
concerns of work, money, and death are more frequently 
discussed.

One limitation of this study is that it lacks access to the 
specific mechanics and practices of the moderators of these 
subreddits, and instead the moderation practices have been 
summed up in broad, ideological terms.

If online spaces are indeed the future of democratic dis-
cussion, then this research suggests that moderation policies, 
on both ideological and practical grounds, should be a fea-
tured issue of inquiry for their role in shaping discussion. 
This applies not only to volunteer moderation on sites like 
Reddit but also to otherwise opaque or invisible corporate 
moderation which takes place at a broad scale. If moderation 
does indeed shape and constrain public discourse, as these 
results suggest, then the ways in which moderation policies 
shape and constrain public discourse for the purposes of a 
democracy at a much larger scale are indeed worth investi-
gating further.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect 
to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, author-
ship, and/or publication of this article.

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Author Biography

Anna Gibson is a PhD candidate in the Communication Department 
at Stanford University. Her research interests include digital media 
and labor.

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