“How over is it?” Understanding the Incel Community on YouTube*
Kostantinos Papadamou?, Savvas Zannettou∓, Jeremy Blackburn†
Emiliano De Cristofaro‡, Gianluca Stringhini, Michael Sirivianos?
?Cyprus University of Technology, ∓Max Planck Institute, †Binghamton University
‡University College London, Boston University
firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
email@example.com, firstname.lastname@example.org, email@example.com
YouTube is by far the largest host of user-generated video con-
tent worldwide. Alas, the platform has also come under ﬁre for
hosting inappropriate, toxic, and hateful content. One commu-
nity that has often been linked to sharing and publishing hateful
and misogynistic content are the Involuntary Celibates (Incels),
a loosely deﬁned movement ostensibly focusing on men’s is-
sues. In this paper, we set out to analyze the Incel community
on YouTube by focusing on this community’s evolution over the
last decade and understanding whether YouTube’s recommen-
dation algorithm steers users towards Incel-related videos. We
collect videos shared on Incel communities within Reddit and
perform a data-driven characterization of the content posted on
Among other things, we ﬁnd that the Incel community on
YouTube is getting traction and that, during the last decade,
the number of Incel-related videos and comments rose sub-
stantially. We also ﬁnd that users have a 6.3% chance of being
suggested an Incel-related video by YouTube’s recommenda-
tion algorithm within ﬁve hops when starting from a non Incel-
related video. Overall, our ﬁndings paint an alarming picture of
online radicalization: not only Incel activity is increasing over
time, but platforms may also play an active role in steering users
towards such extreme content.
While YouTube has revolutionized the way people discover and
consume video content online, it has also enabled the spread
of inappropriate and hateful content. The platform, and in par-
ticular its recommendation algorithm, has been repeatedly ac-
cused of promoting offensive and dangerous content, and even
of helping radicalize users [53,65,68,75].
One fringe community active on YouTube are the so-called
Involuntary Celibates, or Incels . While not particularly
structured, Incel ideology revolves around the idea of the
“blackpill” – a bitter and painful truth about society – which
roughly postulates that life trajectories are determined by how
attractive one is and that things that are largely out of personal
control, like facial structure, are more “valuable” than those un-
der our control, like the ﬁtness level. Incels are one of the most
*To appear at the 24th ACM Conference on Computer-Supported Coopera-
tive Work and Social Computing (CSCW 2021). Please cite the CSCW version.
extreme communities of the Manosphere , a larger collec-
tion of movements discussing men’s issues  (see Section 2).
When taken to the extreme, these beliefs can lead to a dystopian
outlook on society, where the only solution is a radical, poten-
tially violent shift towards traditionalism, especially in terms of
women’s role in society .
Overall, Incel ideology is often associated with misogyny
and anti-feminist viewpoints, and it has also been linked to mul-
tiple mass murders and violent offenses [14,22]. In May 2014,
Elliot Rodger killed six people and himself in Isla Vista, CA.
This incident was a harbinger of things to come. Rodger up-
loaded a video on YouTube with his “manifesto,” as he planned
to commit mass murder due to his belief in what is now gener-
ally understood to be Incel ideology . He served as an ap-
parent “mentor” to another mass murderer who shot nine peo-
ple at Umpqua Community College in Oregon the following
year . In 2018, another mass murderer drove his van into a
crowd in Toronto, killing nine people, and after his interroga-
tion, the police claimed he had been radicalized online by Incel
ideology . More recently, 22-year-old Jake Davison shot
and killed ﬁve people, including a 3-year-old girl, in Plymouth,
England . Thus, while the concepts underpinning Incels’
principles may seem absurd, they also have grievous real-world
Motivation. Online platforms like Reddit became aware of
the problem and banned several Incel-related communities on
the platform . However, prior work suggests that ban-
ning subreddits and their users for hate speech does not solve
the problem, but instead makes these users someone else’s
problem , as banned communities migrate to other plat-
forms . Indeed, the Incel community comprising several
banned subreddits ended up migrating to various other online
communities such as new subreddits, stand-alone forums, and
YouTube channels [63,64].
The research community has mostly studied the Incel com-
munity and the broader Manosphere on Reddit, 4chan, and on-
line discussion forums like Incels.me or Incels.co [23,38,50,
54,63,64]. However, the fact that YouTube has been repeatedly
accused of user radicalization and promoting offensive and in-
appropriate content [40,53,56,65,68] prompts the need to
study the extent to which Incels are exploiting the YouTube
platform to spread their views.
Research Questions. With this motivation in mind, this pa-
arXiv:2001.08293v7 [cs.CY] 23 Aug 2021
per explores the footprint of the Incel community on YouTube.
More precisely, we identify two main research questions:
1. RQ1: How has the Incel community evolved on YouTube
over the last decade?
2. RQ2: Does YouTube’s recommendation algorithm con-
tribute to steering users towards Incel communities?
Methods. We collect a set of 6.5K YouTube videos shared
on Incel-related subreddits (e.g., /r/incels, /r/braincels, etc.), as
well as a set of 5.7K random videos as a baseline. We then build
a lexicon of 200 Incel-related terms via manual annotation, us-
ing expressions found on the Incel Wiki. We use the lexicon
to label videos as “Incel-related,” based on the appearance of
terms in the transcript, which describes the video’s content, and
comments on the videos. Next, we use several tools, includ-
ing temporal and graph analysis, to investigate the evolution of
the Incel community on YouTube and whether YouTube’s rec-
ommendation algorithm contributes to steering users towards
Incel content. To build our graphs, we use the YouTube Data
API, which lets us analyze YouTube’s recommendation algo-
rithm’s output based on video item-to-item similarities, as well
as general user engagement and satisfaction metrics .
Main Findings. Overall, our study yields the following main
• We ﬁnd an increase in Incel-related activity on YouTube
over the past few years and in particular concerning Incel-
related videos, as well as comments that include pertinent
terms. This indicates that Incels are increasingly exploit-
ing the YouTube platform to broadcast and discuss their
• Random walks on the YouTube’s recommendation graph
using the Data API and without personalization reveal that
with a 6.3% probability a user will encounter an Incel-
related video within ﬁve hops if they start from a random
non-Incel-related video posted on Reddit. Simultaneously,
Incel-related videos are more likely to be recommended
within the ﬁrst two to four hops than in the subsequent
• We also ﬁnd a 9.4% chance that a user will encounter an
Incel-related video within three hops if they have visited
Incel-related videos in the previous two hops. This means
that a user who purposefully and consecutively watches
two or more Incel-related videos is likely to continue being
recommended such content and with higher frequency.
Overall, our ﬁndings indicate that Incels are increasingly ex-
ploiting YouTube to spread their ideology and express their
misogynistic views. They also indicate that the threat of recom-
mendation algorithms nudging users towards extreme content
is real and that platforms and researchers need to address and
mitigate these issues.
Paper Organization. We organize the rest of the paper as fol-
lows. The next section presents an overview of Incel ideology
and the Manosphere and a review of the related work. Section 3
provides information about our data collection and video anno-
tation methodology, while Section 4analyzes the evolution of
the Incel community on YouTube. Section 5presents our analy-
sis of how YouTube’s recommendation algorithm behaves with
respect to Incel-related videos. Finally, we discuss our ﬁndings
and possible design implications for social media platforms,
and conclude the paper in Section 6.
2 Background & Related Work
Incels are a part of the broader “Manosphere,” a loose collection
of groups revolving around a common shared interest in “men’s
rights” in society . While we focus on Incels, understanding
the overall Manosphere movement provides relevant context. In
this section, we provide background information about Incels
and the Manosphere. We also review related work focusing on
understanding Incels on the Web, YouTube’s recommendation
algorithm and user radicalization, as well as harmful activity on
2.1 Incels and the Manosphere
The Manosphere. The emergence of the so-called Web 2.0
and popular social media platforms have been crucial in en-
abling the Manosphere . Although the Manosphere had
roots in anti-feminism [24,52], it is ultimately a reactionary
community, with its ideology evolving and spreading mainly
on the Web . Blais et al.  analyze the beliefs concern-
ing the Manosphere from a sociological perspective and refer
to it as masculinism. They conclude that masculinism is: “a
trend within the anti-feminist counter-movement mobilized not
only against the feminist movement but also for the defense
of a non-egalitarian social and political system, that is, patri-
archy.” Subgroups within the Manosphere actually differ signif-
icantly. For instance, Men Going Their Own Way (MGTOWs)
are hyper-focused on a particular set of men’s rights, often in
the context of a bad relationship with a woman. These sub-
groups should not be seen as distinct units. Instead, they are
interconnected nodes in a network of misogynistic discourses
and beliefs . According to Marwick and Lewis , what
binds the manosphere subgroups is “the idea that men and boys
are victimized; that feminists, in particular, are the perpetrators
of such attacks.”
Overall, research studying the Manosphere has been mostly
theoretical and qualitative in nature [28,30,33,43]. These
qualitative studies are important because they guide our study
in terms of framework and conceptualization while motivating
large-scale data-driven work like ours.
Incels. Incels are arguably the most extreme subgroup of the
Manosphere . Incels appear disarmingly honest about what
is causing their grievances compared to other radical ideolo-
gies. They openly put their sexual deprivation, which is sup-
posedly caused by their unattractive appearance, at the fore-
front, thus rendering their radical movement potentially more
persuasive and insidious . Incel ideology differs from the
other Manosphere subgroups in the signiﬁcance of the “invol-
untary” aspect of their celibacy. They believe that society is
rigged against them in terms of sexual activity, and there is
no solution at a personal level for the systemic dating prob-
lems of men [35,51,62]. Further, Incel ideology differs from,
for example, MGTOW, in the idea of voluntary vs. involuntary
celibacy. MGTOWs are choosing to not partake in sexual ac-
tivities, while Incels believe that society adversarially deprives
them of sexual activity. This difference is crucial, as it gives rise
to some of their more violent tendencies .
Incels believe to be doomed from birth to suffer in a modern
society where women are not only able but encouraged to focus
on superﬁcial aspects of potential mates, e.g., facial structure
or racial attributes. Some of the earliest studies of “involuntary
celibacy” note that celibates tend to be more introverted and
that, unlike women, celibate men in their 30s tend to be poorer
or even unemployed . In this distorted view of reality, men
with these desirable attributes (colloquially nicknamed Chads
by Incels) are placed at the top of society’s hierarchy. While a
perusal of inﬂuential people in the world would perhaps lend
credence to the idea that “handsome” white men are indeed at
the top, the Incel ideology takes it to the extreme.
Incels rarely hesitate to call for violence . For example,
when they seek advice from other Incels about their physical
appearance using the phrase “How over is it?,” they may be
encouraged to “rope” (to hang oneself) . Occasionally they
call for outright gendercide. Zimmerman et al.  associate
Incel ideology to white-supremacy, highlighting how it should
be taken as seriously as other forms of violent extremism.
2.2 Related Work
Incels and the Web. Massanari  performs a qualitative
study of how Reddit’s algorithms, policies, and general com-
munity structure enables, and even supports, toxic culture. She
focuses on the #GamerGate and Fappening incidents, both of
which had primarily female victims, and argues that speciﬁc
design decisions make it even worse for victims. For instance,
the default ordering of posts on Reddit favors mobs of users
promoting content over a smaller set of victims attempting to
have it removed. She notes that these issues are exacerbated in
the context of online misogyny because many of the perpetra-
tors are extraordinarily techno-literate and thus able to exploit
more advanced features of social media platforms.
Baele et al.  study content shared by members of the In-
cel community, focusing on how support and motivation for vi-
olence result from their worldview. Farell et al.  perform
a large-scale quantitative study of the misogynistic language
across the Manosphere on Reddit. They create nine lexicons of
misogynistic terms to investigate how misogynistic language
is used in 6M posts from Manosphere-related subreddits. Jaki
et al.  study misogyny on the Incels.me forum, analyzing
users’ language and detecting misogyny instances, homopho-
bia, and racism using a deep learning classiﬁer that achieves up
to 95% accuracy.
Furthermore, Ribeiro et al.  focus on the evolution of
the broader Manosphere and perform a large-scale character-
ization of multiple Manosphere communities mainly on Reddit
and six other Web forums associated with these communities.
They ﬁnd that older Manosphere communities, such as Men’s
Rights Activists and Pick Up Artists, are becoming less pop-
ular and active. In comparison, newer communities like Incels
and MGTOWs attract more attention. They also ﬁnd a substan-
tial migration of users from old communities to new ones, and
that newer communities harbor more toxic and extreme ideolo-
gies. In another study, Ribeiro et al.  investigate whether
platform migration of toxic online communities compromises
content moderation. To do this, they focus on two communities
on Reddit, namely, /r/Incels and /r/The Donald, and use them to
assess whether community-level moderation measures were ef-
fective in reducing the negative impact of toxic communities.
They conclude that a given platforms’ moderation measures
may create even more radical communities on other platforms.
Instead, in our work we focus on the most extreme subgroup
of the Manosphere, the Incel community, and we provide the
ﬁrst study of this community on YouTube, a platform where
misogynistic ideologies, like Incel ideology, are relatively un-
studied. We focus on analyzing the footprint of this community
on YouTube aiming to quantify its growth over the last decade.
More importantly, we also investigate how the opaque nature of
YouTube’s recommendation algorithm enables the discovery of
Incel-related content by both random users of the platform and
users who purposefully choose to see such content.
Harmful Activity on YouTube. YouTube’s role in harmful ac-
tivity has been studied mostly in the context of detection. Agar-
wal et al.  present a binary classiﬁer trained with user and
video features to detect videos promoting hate and extremism
on YouTube, while Giannakopoulos et al.  develop a k-
nearest classiﬁer trained with video, audio, and textual features
to detect violence on YouTube videos. Jiang et al.  investi-
gate how channel partisanship and video misinformation affect
comment moderation on YouTube, ﬁnding that comments are
more likely to be moderated if the video channel is ideologi-
cally extreme. Sureka et al.  use data mining and social net-
work analysis techniques to discover hateful YouTube videos,
while Ottoni et al.  analyze video content and user com-
ments on alt-right channels. Zannettou et al.  present a deep
learning classiﬁer for detecting videos that use manipulative
techniques to increase their views, i.e., clickbait. Papadamou
et al. , and Tahir et al.  focus on detecting inappropri-
ate videos targeting children on YouTube. Mariconti et al. 
build a classiﬁer to predict, at upload time, whether or not a
YouTube video will be “raided” by hateful users.
Calls for action. Additional studies point to the need for a bet-
ter understanding of misogynistic content on YouTube. Wotanis
et al.  show that more negative feedback is given to female
than male YouTubers by analyzing hostile and sexist comments
on the platform. D¨
oring et al.  build on this study by empir-
ically investigating male dominance and sexism on YouTube,
concluding that male YouTubers dominate YouTube, and that
female content producers are prone to receiving more negative
and hostile video comments.
To the best of our knowledge, our work is the ﬁrst to pro-
vide a large-scale understanding and analysis of misogynistic
content on YouTube generated by the Manosphere subgroups.
In particular, we investigate the role of YouTube’s recommen-
dation algorithm in disseminating Incel-related content on the
Subreddit #Videos #Users #Posts Min. Date Max. Date #Incel-related #Other
Braincels 2,744 2,830,522 51,443 2017-10 2019-05 175 2,569
ForeverAlone 1,539 1,921,363 86,670 2010-09 2019-05 45 1,494
IncelTears 1,285 1,477,204 93,684 2017-05 2019-05 56 1,229
Incels 976 1,191,797 39,130 2014-01 2017-11 48 928
IncelsWithoutHate 223 163,820 7,141 2017-04 2019-05 16 207
ForeverAloneDating 92 153,039 27,460 2011-03 2019-05 0 92
askanincel 25 39,799 1,700 2018-11 2019-05 2 23
BlackPillScience 25 9,048 1,363 2018-03 2019-05 5 20
ForeverUnwanted 23 24,855 1,136 2016-02 2018-04 4 19
Incelselﬁes 17 60,988 7,057 2018-07 2019-05 1 16
Truecels 15 6,121 714 2015-12 2016-06 1 14
gymcels 5 1,430 296 2018-03 2019-04 2 3
MaleForeverAlone 3 6,306 831 2017-12 2018-06 0 3
foreveraloneteens 2 2,077 450 2011-11 2019-04 0 2
gaycel 1 117 43 2014-02 2018-10 0 1
SupportCel 1 6,095 474 2017-10 2019-01 0 1
Truefemcels 1 311 95 2018-09 2019-04 0 1
Foreveralonelondon 0 57 19 2013-01 2019-01 0 0
IncelDense 0 2,058 388 2018-06 2019-04 0 0
Total (Unique) 6,977 7,897,007 320,094 - - 290 6,162
Table 1: Overview of our Reddit dataset. We also include, for each
subreddit, the number of videos from our Incel-derived labeled dataset.
The total number of videos reported in the individual subreddits dif-
fers from the unique videos collected since multiple videos have been
shared in more than one subreddit.
YouTube Recommendations. YouTube determines the ranks
of the videos recommended to users based on various user en-
gagement (e.g., user clicks, degree of engagement with rec-
ommended videos, etc.) and satisfaction metrics (e.g., likes,
dislikes, etc.). Aiming to increase the time that a user spends
watching a particular video, the platform also considers vari-
ous other user personalization factors, such as demographics,
geolocation, or the watch history of the user .
Covington et al.  describe YouTube’s recommendation
algorithm, using a deep candidate generation model to retrieve
a small subset of videos from a large corpus and a deep ranking
model to rank those videos based on their relevance to the user’s
activity. Zhao et al.  propose a large-scale ranking system
for YouTube recommendations. The proposed model ranks the
candidate recommendations based on user engagement and sat-
Others focus on analyzing YouTube recommendations on
speciﬁc topics. Ribeiro et al.  perform a large-scale audit
of user radicalization on YouTube: they analyze videos from
Intellectual Dark Web, Alt-lite, and Alt-right channels, show-
ing that they increasingly share the same user base. They also
analyze YouTube’s recommendation algorithm ﬁnding that Alt-
right channels can be reached from both Intellectual Dark Web
and Alt-lite channels. St¨
ocker et al.  analyze the effect of
extreme recommendations on YouTube, ﬁnding that YouTube’s
auto-play feature is problematic. They conclude that prevent-
ing inappropriate personalized recommendations is technically
infeasible due to the nature of the recommendation algorithm.
Finally,  focus on measuring misinformation on YouTube
and perform audit experiments considering ﬁve popular top-
ics like 9/11 and chemtrail conspiracy theories to investigate
whether personalization contributes to amplifying misinforma-
tion. They audit three YouTube features: search results, Up-next
video, and Top 5 video recommendations, ﬁnding a ﬁlter bubble
effect  in the video recommendations section for almost all
the topics they analyze. In contrast to the above studies, we fo-
cus on a different societal problem on YouTube. We explore the
footprint of the Incel community, and we analyze the role of the
recommendation algorithm in nudging users towards them. To
the best of our knowledge, our work is the ﬁrst to study the Incel
community on YouTube and the role of YouTube’s recommen-
dation algorithm in the circulation of Incel-related content on
the platform. We devise a methodology for annotating videos
on the platform as Incel-related and using several tools, includ-
ing text and graph analysis. We study the Incel community’s
footprint on YouTube and assess how YouTube’s recommenda-
tion algorithm behaves with respect to Incel-related videos.
We now present our data collection and annotation process to
identify Incel-related videos.
3.1 Data Collection
To collect Incel-related videos on YouTube, we look for
YouTube links on Reddit, since recent work  highlighted
that Incels are particularly active on Reddit.
We start by creating a list of subreddits that are relevant to In-
cels. To do so, we inspect around 15 posts on the Incel Wiki 
looking for references to subreddits and compile a list1of 19
Incel-related subreddits. This list also includes a set of commu-
nities relevant to Incel ideology (even possibly anti-incel like
/r/Inceltears) to capture a broader collection of relevant videos.
We collect all submissions and comments made between
June 1, 2005, and April 30, 2019, on the 19 Incel-related sub-
reddits using the Reddit monthly dumps from Pushshift . We
parse them to gather links to YouTube videos, extracting 5M
posts, including 6.5K unique links to YouTube videos that are
still online and have a transcript available by YouTube to down-
load. Next, we collect the metadata of each YouTube video us-
ing the YouTube Data API . Speciﬁcally, we collect: 1)
transcript; 2) title and description; 3) a set of tags deﬁned by
the uploader; 4) video statistics such as the number of views,
likes, etc.; and 5) the top 1K comments, deﬁned by YouTube’s
relevance metric, and their replies. Throughout the rest of this
paper, we refer to this set of videos, which is derived from Incel-
related subreddits, as “Incel-derived” videos.
Table 1reports the total number of users, posts, linked
YouTube videos, and the period of available information for
each subreddit. Although recently created, /r/Braincels has the
largest number of posts and YouTube videos. Also, even though
it was banned in November 2017 for inciting violence against
women , /r/Incels is fourth in terms of YouTube videos
shared. Lastly, note that most of the subreddits in our sample
were created between 2015 and 2018, which already suggests a
trend of increasing popularity for the Incel community.
Control Set. We also collect a dataset of random videos and
use it as a control to capture more general trends on YouTube
videos shared on Reddit as the Incel-derived set includes only
videos posted on Incel communities on Reddit. To collect Con-
trol videos, we parse all submissions and comments made on
Reddit between June 1, 2005, and April 30, 2019, using the
Reddit monthly dumps from Pushshift, and we gather all links
1Available at https://bit.ly/incel-related- subreddits-list.
Step 1 Step 2
Manual review of a
random sample of
terms in the transcript
and comments of the
Label videos using multiple
combinations of the min.
number of Incel-related terms in
the transcript and comments
Using the labels from Step 2
calculate the performance metrics of
each possible combination in Step 4
and ﬁnd the optimal combination
Step 4 Step 5
Annotate all videos in
the dataset using the
optimal combination of
Figure 1: Overview of our video annotation methodology.
in Transcript in Comments Accuracy Precision Recall F1 Score
≥0≥7 0.81 0.77 0.80 0.78
≥1≥1 0.82 0.78 0.82 0.79
≥0≥3 0.79 0.79 0.79 0.79
≥1≥2 0.83 0.78 0.83 0.79
≥1≥3 0.83 0.79 0.83 0.79
Table 2: Performance metrics of the top combinations of the number
of Incel-related terms in a video’s transcript and comments.
to YouTube videos. From them, we randomly select 5,793 links
shared in 2,154 subreddits2for which we collect their metadata
using the YouTube Data API.
We choose to use a randomly selected set of videos shared on
Reddit as our Control set for a more fair comparison since our
Incel-derived set also includes videos shared on this platform.
We collect random videos instead of videos relevant to another
sensitive topic because this allows us to study the amount of
Incel-related content that can generally be found on YouTube.
At the same time, videos relevant to another sensitive topic or
community (e.g., MGTOW) may have strong similarities with
Incel-related videos, hence they may not be able to capture
more general trends on YouTube.
3.2 Video Annotation
The analysis of Incel-related content on YouTube differs
from analyzing other types of inappropriate content on the plat-
form. So far, there is no prior study exploring the main themes
involved in videos that Incels ﬁnd of interest. This renders the
task of annotating the actual video rather cumbersome. Besides,
annotating the video footage does not by itself allow us to study
the footprint of the Incel community on YouTube effectively.
When it comes to this community, it is not only the video’s
content that may be relevant. Rather, the language that the com-
munity members use in their videos or comments for or against
their views is also of interest. For example, there are videos fea-
turing women talking about feminism, which are heavily com-
mented on by Incels.
Building a Lexicon. To capture the variety of aspects of the
problem, we devise an annotation methodology based on a lex-
icon of terms that are routinely used by members of the Incel
community and use it to annotate the videos in our dataset. Fig-
ure 1depicts the individual steps that we follow in the devised
video annotation methodology.
To create the lexicon (Step 1 in Figure 1), we ﬁrst crawl the
“glossary” available on the Incels Wiki page , gathering 395
2See https://bit.ly/incels-control- videos-subreddits for the list of subreddits and
the number of control videos shared in each subreddit.
terms. Since the glossary includes several words that can also
be regarded as general-purpose (e.g., fuel, hole, legit, etc.), we
employ three human annotators to determine whether each term
is speciﬁc to the Incel community.
We note that all annotators label all the 395 terms of the glos-
sary. The three annotators are authors of this paper and they
are familiar with scholarly articles on the Incel community and
the Manosphere in general. Before the annotation task, a dis-
cussion took place to frame the task and the annotators were
told to consider a term relevant only if it expresses hate, misog-
yny, or is directly associated with Incel ideology. For exam-
ple, the phrase “Beta male” or any Incel-related incident (e.g.,
“supreme gentleman,” an indirect reference to the Isla Vista
killer Elliot Rodgers ). We note that, during the labeling,
the annotators had no discussion or communication whatsoever
about the task at hand.
We then create our lexicon by only considering the terms an-
notated as relevant, based on all the annotators’ majority agree-
ment, which yields a 200 Incel-related term dictionary3. We
also compute the Fleiss’ Kappa Score  to assess the agree-
ment between the annotators, ﬁnding it to be 0.69, which is
considered “substantial” agreement .
Labeling. Next, we use the lexicon to label the videos in our
dataset. We look for these terms in the transcript, title, tags,
and comments of our dataset videos. Most matches are from
the transcript and the videos’ comments; thus, we decide to use
these to determine whether a video is Incel-related. To select
the minimum number of Incel-related terms that transcripts and
comments should contain to be labeled as “Incel-related,” we
devise the following methodology:
1. We randomly select 1K videos from the Incel-derived set,
which the ﬁrst author of this paper manually annotates as
“Incel-related” or “Other” by watching them and looking
at the metadata. Note that Incel-related videos are a subset
of Incel-derived ones (Step 2 in Figure 1).
2. We count the number of Incel-related terms in the tran-
script and the annotated videos’ comments (Step 3 in Fig-
3. For each possible combination of the minimum number
of Incel-related terms in the transcript and the comments,
we label each video as Incel-related or not, and calculate
the accuracy, precision, recall, and F1 score based on the
labels assigned to the videos during the manual annotation
(Steps 4 and 5 in Figure 1).
3See https://bit.ly/incel-related- terms-lexicon for the ﬁnal lexicon with all the
Figure 2: Temporal evolution of the number of YouTube videos shared
on each subreddit per month. (a) depicts the absolute number of videos
shared in each subreddit and (b) depicts the normalized number of
videos shared per active user of each subreddit. We annotate both ﬁg-
ures with the date when Reddit decided to ban the /r/Incels subreddit.
Table 2shows the performance metrics for the top ﬁve com-
binations of the number of Incel-related terms in the transcript
and the comments. We pick the one yielding the best F1 score
(to balance between false positives and false negatives), which
is reached if we label a video as Incel-related when there is at
least one Incel-related term in the transcript and at least three
in the comments. Using this rule, we annotate all the videos in
our dataset (Steps 5 and 6 in Figure 1).
Table 1reports the label statistics of the Incel-derived videos
per subreddit. Our ﬁnal labeled dataset includes 290 Incel-
related and 6,162 Other videos in the Incel-derived set, and 66
Incel-related and 5,727 Other videos in the Control set.
Overall, we follow standard ethical guidelines [20,66] re-
garding information research and the use of shared measure-
ment data. In this work, we only collect and process publicly
available data, make no attempt to de-anonymize users, and our
employ. More precisely, we ensure compliance with GDPR’s
“Right of Access”  and “Right to be Forgotten”  princi-
ples. For the former, we give users the right to obtain a copy
of any data that we maintain about them for the purposes of
this research, while for the former we ensure that we delete and
not share with any unauthorized party any information that has
been deleted from the public repositories from which we ob-
tain our data. We also note that we do not share with anyone
any sensitive personal data, such as the actual content of the
Figure 3: Percentage of videos published per month for both Incel-
derived and Control videos. We also depict the date when Reddit de-
cided to ban the /r/Incels subreddit.
comments that we analyze or the usernames of the commenting
users. Instead, we make publicly available for reproducibility
and research purposes all the metadata of the collected and an-
notated videos4that do not include any personal data, as well
as the unique identiﬁers of the comments that we analyze while
ensuring that we abide by GDPR’s “Right to be Forgotten” 
Furthermore, our video annotation methodology abides by
the ethical guidelines deﬁned by the Association of Internet
Researchers (AoIR) for the protection of researchers . Note
that in our video annotation methodology we do not engage any
human subjects other than the three authors of this paper. Since
the annotators are authors of this paper, we do not take into con-
sideration harmful effects on random human annotators due to
inappropriate content. However, we still consider the effect of
the content that we study on the authors and especially the stu-
dent authors. We address this with continuous monitoring and
open discussions with members of the research team, as well
as by properly applying best practices from the psychological
and social scientiﬁc literature on the topic, e.g., [5,8,10]. One
of the primary goals is to minimize the risk that the researchers
become de-sensitized with respect to such content. Finally, we
believe that studying misogynistic and hateful communities in
depth is bound to be beneﬁcial for society at large, as well as
for victims of such abuse.
4 RQ1: Evolution of Incel community
This section explores how the Incel communities on YouTube
and Reddit have evolved in terms of videos and comments
We start by studying the “evolution” of the Incel commu-
nities concerning the number of videos they share. First, we
look at the frequency with which YouTube videos are shared on
various Incel-related subreddits per month; see Figure 2. Fig-
ure 2(a) shows the absolute number of videos shared in each
Incel-related subreddit per month, while Figure 2(b) shows
the number of videos shared in each subreddit per active user
Figure 4: Temporal evolution of the number of comments per month.
We also depict the date when Reddit decided to ban the /r/Incels sub-
Figure 5: Temporal evolution of the number of unique commenting
users per month. We also depict the date when Reddit decided to ban
the /r/Incels subreddit.
of each community. After June 2016, we observe that Incel-
related subreddits users start linking to YouTube videos more
frequently and more in 2018. This trend is more pronounced on
/r/Braincels in both the absolute number of videos shared and
the number of videos shared per active user; see /r/Braincels
in Figure 2(a) and Figure 2(b). This indicates that the use of
YouTube to spread Incel ideology is increasing. Note that the
sharp drop of /r/Incels activity is due to Reddit’s decision to ban
this subreddit for inciting violence against women in November
2017  (see annotation in Figure 2(a) and Figure 2(b)). How-
ever, the sharp increase of /r/Braincels activity after this period
questions the efﬁcacy of Reddit’s decision to ban /r/Incels, and
it can be considered as evidence that the ban was ineffective
in terms of suppressing the activity of the Incel community on
the platform. It also worths noting that Reddit decided to ban
/r/Braincels in September 2019 .
In Figure 3, we plot the percentage of videos published per
month for both Incel-derived and Control videos, while we also
depict the date when Reddit decided to ban the /r/Incels subred-
dit. While the increase in the number of Other videos remains
relatively constant over the years for both sets of videos, this is
not the case for Incel-related ones, as 81% and 64% of them in
the Incel-derived and Control sets, respectively, published after
December 2014. Overall, there is a steady increase in Incel ac-
tivity, especially after 2016, which is particularly worrisome as
we have several examples of users who were radicalized online
and have gone to undertake deadly attacks . An even higher
increase in Incel-activity is also observed after the ban of the
Figure 6: Self-similarity of commenting users in adjacent months for
both Incel-derived and Control videos. We also depict the date when
Reddit decided to ban the /r/Incels subreddit.
Next, we study the commenting activity on both Reddit and
YouTube. Figure 4shows the number of comments posted per
month for both YouTube Incel-derived and Control videos, and
Reddit. Activity on both platforms starts to markedly increase
after 2016, and more after the ban of /r/Incels in November
2017, with Reddit and YouTube Incel-derived videos having
substantially more comments than the Control videos. Once
again, the sharp increase in the commenting activity over the
last few years signals an increase in the Incel user base’s size.
To further analyze this trend, we look at the number of unique
commenting users per month on both platforms; see Figure 5.
On Reddit, we observe that the number of unique users remains
steady over the years, increasing from 10K in August 2017 to
25K in April 2019. This is mainly because most of the sub-
reddits in our dataset (58%) were created after 2016. On the
other hand, for the Incel-derived videos on YouTube, there is
a substantial increase from 30K in February 2017 to 132K in
April 2019. We also observe an increase of the Control videos’
unique commenting users (from 18K in February 2017 to 53K
in April 2019). However, the increase is not as sharp as that of
the Incel-derived videos; 483% vs. 1,040% increase in the av-
erage unique commenting users per month after January 2017
in Control and Incel-derived videos, respectively.
To assess whether the sharp increase in unique commenting
users of the Incel-derived and Control videos after 2017 is due
to the increased interest by random users or to an increased in-
terest in those videos and their discussions by the same users
over the years, we use the Overlap Coefﬁcient similarity met-
ric ; it measures user retention over time for the videos in
our dataset. Speciﬁcally, we calculate, for each month, the sim-
ilarity of commenting users with those doing so the month be-
fore, for both Incel-related and Other videos in the Incel-derived
and Control sets. Note that if the set of commenting users of a
speciﬁc month is a subset of the previous month’s commenting
users or the converse, the overlap coefﬁcient is equal to 1. The
results of this calculation are shown in Figure 6, in which we
again depict the date when Reddit decided to ban /r/Incels. In-
terestingly, for the Incel-related videos of the Incel-derived set,
we ﬁnd a sharp growth in user retention right after the ban of the
/r/Incels subreddit in November 2017, while this is not the case
for the Incel-related videos of the Control set. For the Incel-
related videos of the Control set, we observe a more steady in-
Recommendation Graph Incel-related Other
Incel-derived 1,074 (2.9%) 36,673 (97.1%)
Control 428 (1.5%) 28,866 (98.5%)
Table 3: Number of Incel-related and Other videos in each recommen-
crease in user retention over time. Once again, this might be re-
lated to the increased popularity of the Incel communities and
might indicate that the ban of /r/Incels energized the community
and made participants more persistent. Also, the higher user re-
tention of Other videos in both sets is likely due to the much
higher proportion of Other videos in each set.
Last, we observe a spike in user retention for the Incel-related
videos of the Control set during 2009. However, after checking
the publication dates of these videos in our dataset, we only
ﬁnd three Incel-related videos in the Control set uploaded be-
fore July 2009. Hence, it might be the case that the same users
repeatedly commented on those videos during 2008 and 2009.
At the same time, no other Incel-related videos in the Control
was uploaded between July 2009 and July 2010, hence the drop
in user retention after July 2009.
5 RQ2: Does YouTube’s recommenda-
tion algorithm steer users towards
Next, we present an analysis of how YouTube’s recommen-
dation algorithm behaves with respect to Incel-related videos.
More speciﬁcally, 1) we investigate how likely it is for YouTube
to recommend an Incel-related video; 2) we simulate the behav-
ior of a user who views videos based on the recommendations
by performing random walks on YouTube’s recommendation
graph to measure the probability of such a user discovering
Incel-related content; and 3) we investigate whether the fre-
quency with which Incel-related videos are recommended in-
crease for users who choose to see the content.
5.1 Recommendation Graph Analysis
To build the recommendation graphs used for our analysis,
we use functionality provided by the YouTube Data API. For
each video in the Incel-derived and Control sets, we collect the
top 10 recommended videos associated with it. Note that the use
of the YouTube Data API is associated with a speciﬁc account
only for authentication to the API, and that the API does not
maintain a watch history nor any cookies. Thus, our data collec-
tion does not capture how speciﬁc account features or the view-
ing history affect personalized recommendations. Instead, the
YouTube Data API allows us to collect recommendations pro-
vided by YouTube’s recommendation algorithm based on video
item-to-item similarity, as well as general user engagement and
satisfaction metrics . The collected recommendations are
similar to the recommendations presented to a non-logged-in
user who watches videos on YouTube. We collect the recom-
mendations for the Incel-derived videos between September 20
and October 4, 2019, and the Control between October 15 and
Source Destination Incel-derived Control
Incel-related Incel-related 889 (0.8%) 89 (0.2%)
Incel-related Other 3632 (3.2%) 773 (1.4%)
Other Other 104,706 (93.2%) 54,787 (97.0%)
Other Incel-related 3,160 (2.8%) 842 (1.5%)
Table 4: Number of transitions between Incel-related and Other videos
in each recommendation graph.
November 1, 2019. To annotate the collected videos, we follow
the same approach described in Section 3.2. Since our video
annotation is based on the videos’ transcripts, we only consider
the videos that have one when building our recommendations
Next, we build a directed graph for each set of recommenda-
tions, where nodes are videos (either our dataset videos or their
recommendations), and edges between nodes indicate the rec-
ommendations between all videos (up to ten). For instance, if
video2 is recommended via video1, then we add an edge from
video1 to video2. Throughout the rest of this paper, we refer
to each set of videos’ collected recommendations as separate
First, we investigate the prevalence of Incel-related videos
in each recommendation graph. Table 3reports the number of
Incel-related and Other videos in each graph. For the Incel-
derived graph, we ﬁnd 36,7K (97.1%) Other and 1K (2.9%)
Incel-related videos, while in the Control graph, we ﬁnd 28,9K
(98.5%) Other and 428 (1.5%) Incel-related videos. These ﬁnd-
ings highlight that despite the proportion of Incel-related video
recommendations in the Control graph being smaller, there is
still a non-negligible amount recommended to users. Also, note
that we reject the null hypothesis that the differences between
the two graphs are due to chance via the Fisher’s exact test
(p < 0.001) .
How likely is it for YouTube to recommend an Incel-related
Video? Next, to understand how frequently YouTube recom-
mends an Incel-related video, we study the interplay between
the Incel-related and Other videos in each recommendation
graph. For each video, we calculate the out-degree in terms of
Incel-related and Other labeled nodes. We can then count the
number of transitions the graph makes between differently la-
beled nodes. Table 4reports the percentage of each transition
between the different types of videos for both graphs. Perhaps
unsurprisingly, most of the transitions, 93.2% and 97.0%, re-
spectively, in the Incel-derived and Control recommendation
graphs are between Other videos, but this is mainly because
of the large number of Other videos in each graph. We also
ﬁnd a high percentage of transitions between Other and Incel-
related videos. When a user watches an Other video, if they
randomly follow one of the top ten recommended videos, there
is a 2.9% and 1.5% probability in the Incel-derived and Con-
trol graphs, respectively, that they will end up at an Incel-
related video. Interestingly, in both graphs, Incel-related videos
are more often recommended by Other videos than by Incel-
related videos. On the one hand, this might be due to the larger
number of Other videos compared to Incel-related videos in
both recommendation graphs. On the other hand, this may indi-
% random walks with at least
one Incel-related video
Incel-derived Rec. Graph
Control Rec. Graph
% random walks with at least
one Incel-related video
Incel-derived Rec. Graph
Control Rec. Graph
Figure 7: Percentage of random walks where the random walker encounters at least one Incel-related video for both starting scenarios. Note that
the random walker selects, at each hop, the next video to watch at random.
% unique Incel-related videos
Incel-derived Rec. Graph
Control Rec. Graph
% unique Incel-related videos
Incel-derived Rec. Graph
Control Rec. Graph
Figure 8: Percentage of Incel-related videos across all unique videos that the random walk encounters at hop kfor both starting scenarios. Note
that the random walker selects, at each hop, the next video to watch at random.
cate that YouTube’s recommendation algorithm cannot discern
Incel-related videos, which are likely misogynistic.
5.2 Does YouTube’s recommendation algorithm
contribute to steering users towards Incel
We then study how YouTube’s recommendation algorithm
behaves with respect to discovering Incel-related videos.
Through our graph analysis, we showed that the problem of
Incel-related videos on YouTube is quite prevalent. However, it
is still unclear how often YouTube’s recommendation algorithm
leads users to this type of content.
To measure this, we perform experiments considering a “ran-
dom walker.” This allows us to simulate a random user who
starts from one video and then watches several videos accord-
ing to the recommendations. More precisely, since we build our
recommendation graphs using the YouTube Data API, the ran-
dom walker simulates non-logged-in users who watch videos
on YouTube. The random walker begins from a randomly se-
lected node and navigates the graph choosing edges at ran-
dom for ﬁve hops. We repeat this process for 1,000 random
walks considering two starting scenarios. In the ﬁrst scenario,
the starting node is restricted to Incel-related videos. In the sec-
ond, it is restricted to Other. We perform the same experiment
on both the Incel-derived and Control recommendations graphs.
Next, for the random walks of each recommendation graph,
we calculate two metrics: 1) the percentage of random walks
where the random walker ﬁnds at least one Incel-related video
in the k-th hop; and 2) the percentage of Incel-related videos
over all unique videos that the random walker encounters up
to the k-th hop for both starting scenarios. The two metrics, at
each hop are shown in Figure 7and 8for both recommendation
When starting from an Other video, there is, respectively, a
10.8% and 6.3% probability to encounter at least one Incel-
related video after ﬁve hops in the Incel-derived and Control
recommendation graphs (see Figure 7(a)). When starting from
an Incel-related video, we ﬁnd at least one Incel-related in
43.4% and 36.5% of the random walks performed on the Incel-
derived and Control recommendation graphs, respectively (see
Figure 7(b)). Also, when starting from Other videos, most of
the Incel-related videos are found early in our random walks
(i.e., at the ﬁrst hop), and this number remains almost the same
as the number of hops increases (see Figure 8(a)). The same
stands when starting from Incel-related videos, but in this case,
the percentage of Incel-related videos decreases as the number
Incel-derived Recommendation Graph Control Recommendation Graph
In next 5-M hops,
In next hop, 1
In next 5-M hops,
In next hop, 1
143.4% 4.1% 36.5% 2.1%
246.5% 9.4% 38.9% 5.4%
349.3% 11.4% 41.6% 5.0%
449.7% 18.9% 42.0% 11.2%
547.9% 30.1% 39.7% 17.7%
Table 5: Probability of ﬁnding (a) at least one Incel-related video in the next 5−Mhops having already watched M consecutive Incel-related
videos; and (b) an Incel-related video at hop M+ 1 assuming the user already watched Mconsecutive Incel-related videos for both the Incel-
derived and Control recommendation graphs. Note that in this scenario the random walker chooses to watch Incel-related videos.
of hops increases for both recommendation graphs (see Fig-
As expected, in all cases, the probability of encounter-
ing Incel-related videos in random walks performed on the
Incel-derived recommendation graph is higher than in the ran-
dom walks performed on the Control recommendation graph.
We also verify that the difference between the distribution of
Incel-related videos encountered in the random walks of the
two recommendation graphs is statistically signiﬁcant via the
Kolmogorov-Smirnov test  (p < 0.05). Overall, we ﬁnd
that Incel-related videos are usually recommended within the
two ﬁrst hops. However, in subsequent hops, the number of en-
countered Incel-related videos decreases. This indicates that in
the absence of personalization (e.g., for a non-logged-in user),
a user casually browsing YouTube videos is unlikely to end up
in a region dominated by Incel-related videos.
5.3 Does the frequency with which Incel-related
videos are recommended increase for users
who choose to see the content?
So far, we have simulated the scenario where a user browses
the recommendation graph randomly, i.e., they do not select
Incel-related videos according to their interests or other cues
nudging them to view certain content. Next, we simulate the
behavior of a user who chooses to watch a few Incel-related
videos and investigate whether or not he will get recommended
Incel-related videos with a higher probability within the next
Table 5reports how likely it is for a user to encounter Incel-
related videos assuming he has already watched a few. To do so,
we use the random walks performed on the Incel-derived and
Control recommendation graphs in section 5.2. We consider
only the random walks started from an Incel-related video, and
we zero in on those where the user watches consecutive Incel-
related videos. Speciﬁcally, we report two metrics: 1) the prob-
ability that a user encounters at least one Incel-related video in
5−Mhops, having already seen M consecutive Incel-related
videos; and 2) the probability that the user will encounter an
Incel-related video on the M+ 1 hop, assuming he has already
seen Mconsecutive Incel-related videos. Note that, at each hop
Mof a random walk, we calculate both metrics by only consid-
ering the random walks for which all the videos encountered in
the ﬁrst M hops of the walk were Incel-related. These metrics
0 20 40 60 80 100
% recommended Incel-related videos
Incel-derived Rec. Graph (Incel-related)
Incel-derived Rec. Graph (Other)
Control Rec. Graph (Incel-related)
Control Rec. Graph (Other)
Figure 9: CDF of the percentage of recommended Incel-related videos
per video for both Incel-related and other videos in the Incels-derived
and Control recommendation graphs.
allow us to understand whether the recommendation algorithm
keeps recommending Incel-related videos to a user who starts
watching a few of them.
At every hop M, there is a ≥43.4% and ≥36.5% chance to
encounter at least one Incel-related video within 5−Mhops
in the Incel-derived and Control recommendation graphs, re-
spectively (second and fourth column in Table 5). Furthermore,
by looking at the probability of encountering an Incel-related
video at hop M+ 1, having already watched MIncel-related
videos (third and right-most column in Table 5), we ﬁnd an
increasingly higher chance as the number of consecutive Incel-
related increases. Speciﬁcally, for the Incel-derived recommen-
dation graph, the probability rises from 4.1% at the ﬁrst hop to
30.1% for the last hop. For the Control recommendation graph,
it rises from 2.1% to 17.7%.
These ﬁndings unveil that as users watch Incel-related
videos, the algorithm recommends other Incel-related content
with increasing frequency. In Figure 9, we plot the CDF of
the percentage of Incel-related recommendations for each node
in both recommendation graphs. In the Incel-derived recom-
mendation graph, 4.6% of the Incel-related videos have more
than 80.0% Incel-related recommendations, while 10.0% of
the Incel-related videos have more than 50.0% Incel-related
recommendations. The percentage of Other videos that have
more than 50.0% Incel-related recommendations is negligible.
Although the percentage of Incel-related recommendations is
lower, we see similar trends for the Control recommendation
graph: 8.6% of the Incel-related videos have more than 50.0%
Arguably, the effect we observe may be a contributor to
the anecdotally reported echo chamber effect. This effect en-
tails a viewer who begins to engage with this type of content
and likely falls into an algorithmic rabbit hole, with recom-
mendations becoming increasingly dominated by such harm-
ful content and beliefs, which also becomes increasingly ex-
treme [16,53,56,65,68]. However, the degree to which the
above-inferred algorithm characteristics contribute to a possi-
ble echo chamber effect depends on: 1) personalization factors;
and 2) the ability to measure whether recommendations become
Overall, our analysis of YouTube’s recommendation algo-
rithm yields the following main ﬁndings:
1. We ﬁnd a non-negligible amount of Incel-related videos
(2.9%) within YouTube’s recommendation graph being
recommended to users (see Table 3);
2. When a user watches a non-Incel-related video, if they
randomly follow one of the top ten recommended videos,
there is a 2.8% chance they will end up with an Incel-
related video (see Table 4);
3. By performing random walks on YouTube’s recommen-
dation graph, we ﬁnd that when starting from a random
non-Incel-related video, there is a 6.3% probability to en-
counter at least one Incel-related video within ﬁve hops
(see Figure 7(a));
4. As users choose to watch Incel-related videos, the algo-
rithm recommends other Incel-related videos with increas-
ing frequency (see the third and the right-most column of
Our analysis points to an increase in Incel-related activity on
YouTube over the past few years. More importantly, our rec-
ommendation graph analysis shows that Incel-related videos
are recommended with increasing frequency to users who keep
watching them. This indicates that recommendation algorithms,
to an extent indeed, nudge users towards extreme content. This
section discusses our results in more detail and how they align
with existing research in the area. We also discuss the technical
challenges we faced and how we addressed them and highlight
Our data collection and annotation efforts faced many chal-
lenges. First, there was no available dataset of YouTube videos
related to the Incel community or any other Manosphere
groups. Guided by other studies using Reddit as a source for
collecting and analyzing YouTube videos , and based on
evidence suggesting that Incels are particularly active on Red-
dit [23,63], we build our dataset by collecting videos shared
on Incel-related communities on Reddit. Second, devising a
methodology for the annotation of the collected videos is not
trivial. Due to the nature of the problem, we hypothesize that
using a classiﬁer on the video footage will not capture the
various aspects of Incel-related activity on YouTube. This is
because the misogynistic views of Incels may force them to
heavily comment on a seemingly benign video (e.g., a video
featuring a group of women discussing gender issues) .
Hence, we devise a methodology to detect Incel-related videos
based on a lexicon of Incel-related terms that considers both the
video’s transcript and its comments.
We believe that the scientiﬁc community can use our text-
based approach to study other misogynistic ideologies on the
platform, which tend to have their particular glossary.
Our video annotation methodology might ﬂag some benign
videos as Incel-related. This can be a false positive or due to
Incels that heavily comment on (or even raid ) a benign
video (e.g., a video featuring a group of women discussing gen-
der issues). However, by considering the video’s transcript in
our video annotation methodology, we can achieve an accept-
able detection accuracy that uncovers a substantial proportion
of Incel-related videos (see Section 3.2). Despite this limita-
tion, we believe that our video annotation methodology allows
us to capture and analyze various aspects of Incel-related activ-
ity on the platform. Another limitation of this approach is that
we may miss some Incel-related videos. One reason for this
is that the members of web-based misogynistic communities
often shift or obscure their language to avoid being detected.
Notwithstanding such limitation, our approach approaches the
lower bound of the Incel-related videos available in our dataset,
allowing us to conclude that the implications of YouTube’s rec-
ommendation algorithm on disseminating misogynistic content
are at least as profound as we observe.
Moreover, our work does not consider per-user personal-
ization; the video recommendations we collect represent only
some of the recommendation system’s facets. More precisely,
we analyze YouTube recommendations generated based on
content relevance and the user base’s engagement in aggregate.
However, we believe that the recommendation graphs we ob-
tain do allow us to understand how YouTube’s recommenda-
tion system is behaving in our scenario. Also, note that a simi-
lar methodology for auditing YouTube’s recommendation algo-
rithm has been used in previous work .
6.3 The footprint of the Incel community on
As mentioned earlier, prior work suggests that Reddit’s de-
cision to ban subreddits did not solve the problem , as
users migrated to other platforms [55,64]. At the same time,
other studies show that Incels are particularly active on Red-
dit [24,63], pinpointing the need to develop methodologies
that identify and characterize Manosphere-related activities on
YouTube and other social media platforms. Realizing the threat,
Reddit took measures to tackle the problem by banning sev-
eral subreddits associated with the Incel community and the
Manosphere in general. Driven by that, we set out to study the
evolution of the Incel community, over the last decade, on other
platforms like YouTube.
Our results show that Incel-related activity on YouTube in-
creased over the past few years, in particular, concerning the
publication of Incel-related videos, as well as in comments that
include pertinent terms. This indicates that Incels are increas-
ingly exploiting YouTube to spread their ideology and express
their misogynistic views. Although we do not know whether
these users are banned Reddit users that migrated to YouTube or
whether this increase in Incel-related activity is associated with
the increased interest in Incel-related communities on Reddit
over the past few years, our ﬁndings are still worrisome. Also,
Reddit’s decision to ban /r/Incels for inciting violence against
women  and the observed sharp increase in Incel-related
activity on YouTube after this period aligns with the theoretical
framework proposed by Chandrasekharan et al. . The in-
crease in Incel-related activity also indicates that Reddit’s deci-
sion may have energized the community and made its members
Despite YouTube’s attempts to tackle hate , our results
show that the threat is clear and present. Also, considering that
the Incel ideology is often associated with misogyny, and anti-
feminist views, as well as with multiple mass murders and vio-
lent offenses [14,22], we urge that YouTube develops effective
content moderation strategies to tackle misogynistic content on
6.4 The role of YouTube’s recommendation al-
gorithm in steering users towards the Incel
Driven by the fact that members of the Incel community are
prone to radicalization  and that YouTube has been repeat-
edly accused of contributing to user radicalization and promot-
ing offensive content [40,56], we set out to assess whether
YouTube’s recommendation algorithm nudges users towards
Incel communities. Using graph analysis, we analyze snapshots
of YouTube’s recommendation graph, ﬁnding that there is a
non-negligible amount of Incel-related content being suggested
to users. Also, by simulating a user who casually browses
YouTube, we see a high chance that a user will encounter at
least one Incel-related video ﬁve hops after he starts from a
non-Incel-related video. Next, we simulate a user who, upon
encountering an Incel-related video, becomes interested in this
content and purposefully starts watching these types of videos.
We do this to determine whether YouTube’s recommendation
graph steers such users into regions where a substantial portion
of the recommended videos are Incel-related. Once users enter
such regions, they are likely to consider such content as increas-
ingly legitimate as they experience social proof of these narra-
tives. They may ﬁnd it difﬁcult to escape to more benign con-
tent . Interestingly, we ﬁnd that once a user follows Incel-
related videos, the algorithm recommends other Incel-related
videos to him with increasing frequency. Our results point to
the echo chamber effect [16,44]. However, the echo chamber
effect deﬁnition includes the notion that the extremist nature of
the improper videos increases along with the frequency with
which they are recommended. Since we do not assess whether
the videos suggested in subsequent hops are becoming increas-
ingly extreme, we cannot conclude that we ﬁnd a statistically
signiﬁcant indication of this effect. Nevertheless, even if we do
not ﬁnd strong evidence of an echo chamber, our ﬁndings are
worrisome especially when considering the extreme misogynis-
tic beliefs of the Incel community.
To mitigate the harm caused to users by certain recom-
mended videos and to incorporate community well-being into
the objectives of its recommendation algorithm , YouTube
introduced “user satisfaction” metrics as input to the recom-
mendation algorithm . However, our ﬁndings show that
misogynistic and harmful content is still being recommended
to users and the recommendation algorithm is not able to dis-
cern and marginalize such content. Hence, we believe that more
effort is required by researchers and platforms to effectively de-
tect and suppress such content in a proactive and timely manner.
6.5 Design Implications
Prior work has shown apparent user migration to increas-
ingly extreme subcommunities within the Manosphere on Red-
dit , and indications that YouTube recommendations serve
as a pathway to radicalization. When taken along with our re-
sults, a more complete picture with respect to online extremist
communities begins to emerge.
Radicalization and online extremism is clearly a multi-
platform problem. Social media platforms like Reddit, designed
to allow organic creation and discovery of subcommunities,
play a role, and so do platforms with algorithmic content rec-
ommendation systems. The immediate take away is that while
the radicalization process and the spread of extremist content
generalize (at least to some extent) across different online ex-
tremist communities, the speciﬁc mechanism likely does not
generalize across different platforms, which has implications
for the design of moderation systems and strategies.
In particular, it implies that platform oriented-solutions
should not exist in a vacuum, and indeed it is quite likely that
information sharing between platforms could bolster overall ef-
fectiveness. For example, an approach that could beneﬁt both
platforms we study involves using Reddit activity to help tune
the YouTube recommendation algorithm and using informa-
tion from the recommendation algorithm to help Reddit per-
form content moderation. In such a hypothetical arrangement,
Reddit, whose content moderation team is intimately familiar
with the troublesome communities, could help YouTube under-
stand how the content these communities consume ﬁts within
the recommendation graph. Similarly, Reddit’s moderation ef-
forts could be bolstered with information from the YouTube
recommendation graph. The discovery of emerging dangerous
communities could be aided by understanding where the con-
tent posted by them ﬁts within the YouTube recommendation
graph compared to the content posted by known troublesome
At the same time, our ﬁndings suggest that researchers who
study radicalization and online extremism can beneﬁt by per-
forming cross-platform analysis as studying across multiple
platforms can help in better understanding the footprint and
evolvement of emerging dangerous communities.
6.6 Future Work
We plan to extend our work by studying other Manosphere
communities on YouTube (e.g., Men Going Their Own Way)
and user migration between Manosphere and other reactionary
communities. We also plan to implement crawlers that will al-
low us to simulate real users and perform random walks on
YouTube with user personalization. This will enable measure-
ments of YouTube’s recommendation graph while also assess-
ing the effect of various personalization factors (e.g., gender,
a user’s watch history, etc.) on the amount of misogynistic
content being recommended to a user. Note that this task is
not straightforward as it requires understanding and replicating
multiple meaningful characteristics of Incels’ behavior.
Another interesting direction for future research is to perform
a survey study on YouTube with real users and even collecting
their qualitative feedback. Last, an important direction for fu-
ture work is to study the effect of the COVID-19 pandemic on
the growth of web-based misogynistic communities.
This paper presented a large-scale data-driven characteriza-
tion of the Incel community on YouTube. We collected 6.5K
YouTube videos shared by users in Incel-related communities
within Reddit. We used them to understand how Incel ideology
spreads on YouTube and study the evolution of the community.
We found a non-negligible growth in Incel-related activity on
YouTube over the past few years, both in terms of Incel-related
videos published and comments likely posted by Incels. This
result suggests that users gravitating around the Incel commu-
nity are increasingly using YouTube to disseminate their views.
Overall, our study is a ﬁrst step towards understanding the In-
cel community and other misogynistic ideologies on YouTube.
We argue that it is crucial to protect potential radicalization
“victims” by developing methods and tools to detect Incel-
related videos and other misogynistic activities on YouTube.
Our analysis shows growth in Incel-related activities on Red-
dit and highlights how the Incel community operates on mul-
tiple platforms and Web communities. This also prompts the
need to perform more multi-platform studies to understand
Manosphere communities further.
We also analyzed how YouTube’s recommendation algo-
rithm behaves with respect to Incel-related videos. By perform-
ing random walks on the recommendation graph, we estimated
a6.3% chance for a user who starts by watching non-Incel-
related videos to be recommended Incel-related ones within ﬁve
recommendation hops. At the same time, users who have seen
two or three Incel-related videos at the start of their walk see
recommendations that consist of 9.4% and 11.4% Incel-related
videos, respectively. Moreover, the portion of Incel-related rec-
ommendations increases substantially as the user watches an
increasing number of consecutive Incel-related videos.
Our results highlight the pressing need to further study and
understand the role of YouTube’s recommendation algorithm
in users’ radicalization and content consumption patterns. Ide-
ally, a recommendation algorithm should avoid recommending
potentially harmful or extreme videos. However, our analysis
conﬁrms prior work showing that this is not always the case on
Acknowledgments. This project has received funding from
the European Union’s Horizon 2020 Research and Innova-
tion program under the Marie Skłdowska-Curie ENCASE
project (GA No. 691025) and the CONCORDIA project (GA
No. 830927), the US National Science Foundation (grants:
1942610, 2114407, 2114411, and 2046590), and the UK’s Na-
tional Research Centre on Privacy, Harm Reduction, and Ad-
versarial Inﬂuence Online (UKRI grant: EP/V011189/1). This
work reﬂects only the authors’ views; the funding agencies are
not responsible for any use that may be made of the information
 GDPR: Right of Access. https://gdpr-info.eu/issues/right- of-
 GDPR: Right to be Forgotten. https://gdpr-info.eu/issues/right-
 S. Agarwal and A. Sureka. A Focused Crawler for Mining Hate
and Extremism Promoting Videos on YouTube. In Proceed-
ings of the 25th ACM conference on Hypertext and social media,
 S. J. Baele, L. Brace, and T. G. Coan. From ”Incel” to ”Saint”:
Analyzing the violent worldview behind the 2018 Toronto attack.
In Terrorism and Political Violence. Routledge, 2019.
 N. Bashir. Doing Research in Peoples’ Homes: Fieldwork, Ethics
and Safety–On the Practical Challenges of Researching and Rep-
resenting Life on the Margins. In Qualitative Research, 2018.
 J. Baumgartner, S. Zannettou, B. Keegan, M. Squire, and
J. Blackburn. The Pushshift Reddit Dataset. In Proceedings of
the International AAAI Conference on Web and Social Media,
 BBC. How rampage killer became misogynist “hero”. https:
 B. Beale, R. Cole, S. Hillege, R. McMaster, and S. Nagy. Impact
of In-depth Interviews on the Interviewer: Roller Coaster Ride.
In Nursing & health sciences, 2004.
 Z. Beauchamp. Incel, the misogynist ideology that inspired the
deadly Toronto attack, explained. https://www.vox.com/world/
 N. Blagden and S. Pemberton. The Challenge in Conducting
Qualitative Research With Convicted Sex Offenders. In The
Howard Journal of Criminal Justice, 2010.
 M. Blais and F. Dupuis-D´
eri. Masculinism and the Antifemi-
nist Countermovement. In Social Movement Studies. Taylor &
 J. Bratich and S. Banet-Weiser. From Pick-Up Artists to In-
cels: Con(ﬁdence) Games, Networked Misogyny, and the Failure
of Neoliberalism. In International Journal of Communication,
 L. Cecco. Toronto van attack suspect says he was ’radicalized’
online by ’incels’. https://www.theguardian.com/world/2019/
 S. P. L. Center. Male Supremacy. https://www.splcenter.org/
 E. Chandrasekharan, U. Pavalanathan, A. Srinivasan, A. Glynn,
J. Eisenstein, and E. Gilbert. You Can’t Stay Here: The Efﬁcacy
of Reddit’s 2015 Ban Examined Through Hate Speech. In Pro-
ceedings of the ACM on Human-Computer Interaction, number
CSCW. ACM New York, NY, USA, 2017.
 M. Cinelli, G. D. F. Morales, A. Galeazzi, W. Quattrociocchi,
and M. Starnini. The Echo Chamber Effect on Social Media. In
Proceedings of the National Academy of Sciences, 2021.
 A. Conti. Learn to Decode the Secret Language of the In-
cel Subculture. https://www.vice.com/en/article/7xmaze/learn-
to-decode-the-secret- language-of-the-incel-subculture, 2018.
 J. Cook. Inside Incels’ Looksmaxing Obsession: Penis Stretch-
ing, Skull Implants And Rage. https://www.huffpost.com/entry/
incels-looksmaxing-obsession n 5b50e56ee4b0de86f48b0a4f,
 P. Covington, J. Adams, and E. Sargin. Deep Neural Networks
for YouTube Recommendations. In Proceedings of the 10th ACM
conference on recommender systems, 2016.
 D. Dittrich, E. Kenneally, et al. The Menlo Report: Ethical Prin-
ciples Guiding Information and Communication Technology Re-
search. Technical report, US Department of Homeland Security,
 N. D¨
oring and M. R. Mohseni. Male Dominance and Sexism on
YouTube: Results of Three Content Analyses. In Feminist Media
Studies. Taylor & Francis, 2019.
 T. F. Estate. Why incels are a ’real and present threat’ for Cana-
 T. Farrell, M. Fernandez, J. Novotny, and H. Alani. Exploring
Misogyny Across the Manosphere in Reddit. In Proceedings of
the 10th ACM Conference on Web Science, 2019.
 W. Farrell. The Myth of Male Power. Berkeley Publishing Group,
 R. A. Fisher. On the Interpretation of χ2 From Contingency
Tables, and the Calculation of P. Journal of the Royal Statistical
 J. L. Fleiss. Measuring Nominal Scale Agreement Among Many
Raters. In Psychological bulletin, 1971.
 T. Giannakopoulos, A. Pikrakis, and S. Theodoridis. A Multi-
modal Approach to Violence Detection in Video Sharing Sites.
In 2010 20th International Conference on Pattern Recognition.
 D. Ging. Alphas, Betas, and Incels: Theorizing the Masculinities
of the Manosphere. In Men and Masculinities. SAGE Publica-
tions Sage CA: Los Angeles, CA, 2019.
 Google Developers. YouTube Data API. https://developers.
 L. Gotell and E. Dutton. Sexual Violence in the “Manosphere”:
Antifeminist Men’s Rights Discourses on Rape. In International
Journal for Crime, Justice and Social Democracy. Queensland
University of Technology, 2016.
 C. Hauser. Reddit bans ”incel” group for inciting vio-
lence against women. https://www.nytimes.com/2017/11/09/
 B. Hoffman, J. Ware, and E. Shapiro. Assessing the Threat of
Incel Violence. In Studies in Conﬂict & Terrorism. Taylor &
 Z. Hunte and K. Engstr¨
om. “Female Nature, Cucks, and
Simps”: Understanding Men Going Their Own Way as Part of
the Manosphere. Master’s thesis, 2019.
 E. Hussein, P. Juneja, and T. Mitra. Measuring Misinformation
in Video Search Platforms: An Audit Study on YouTube. Pro-
ceedings of the ACM on Human-Computer Interaction, (CSCW),
 Incels Wiki. Blackpill. https://incels.wiki/w/Blackpill, 2019.
 Incels Wiki. Incel Forums Term Glossary. https://incels.wiki/w/
Incel Forums Term Glossary, 2019.
 Incels Wiki. The Incel Wiki. https://incels.wiki, 2019.
 S. Jaki, T. De Smedt, M. Gw´
z, R. Panchal, A. Rossa, and
G. De Pauw. Online Hatred of Women in the Incels.me Forum:
Linguistic Analysis and Automatic Detection. In Journal of Lan-
guage Aggression and Conﬂict, 2019.
 S. Jiang, R. E. Robertson, and C. Wilson. Bias Misperceived: The
Role of Partisanship and Misinformation in YouTube Comment
Moderation. In Proceedings of the International AAAI Confer-
ence on Web and Social Media, 2019.
 J. Kaiser and A. Rauchﬂeisch. Unite the right? how youtube’s
recommendation algorithm connects the us far-right. In D&S
Media Manipulation, 2018.
 K. E. Kiernan. Who Remains Celibate? In Journal of Biosocial
Science. Cambridge University Press, 1988.
 J. R. Landis and G. G. Koch. The Measurement of Observer
Agreement for Categorical Data. In Biometrics. JSTOR, 1977.
 J. L. Lin. Antifeminism Online: MGTOW (Men Going Their
Own Way). JSTOR, 2017.
 D. M. Literacy. What is an echo chamber? https://edu.gcfglobal.
 E. Mariconti, G. Suarez-Tangil, J. Blackburn, E. De Cristo-
faro, N. Kourtellis, I. Leontiadis, J. L. Serrano, and G. Stringh-
ini. “You Know What to Do”: Proactive Detection of YouTube
Videos Targeted by Coordinated Hate Attacks. In Proceedings
of the ACM on Human-Computer Interaction, number CSCW.
ACM New York, NY, USA, 2019.
 P. Martineau. YouTube Is Banning Extremist Videos. Will
It Work? https://www.wired.com/story/how-effective-youtube-
 A. Marwick and R. Lewis. Media Manipulation and Disinforma-
tion Online. 2017.
 A. Massanari. # Gamergate and The Fappening: How Reddit’s
Algorithm, Governance, and Culture Support Toxic Technocul-
tures. In New Media & Society. Sage Publications Sage UK:
London, England, 2017.
 F. J. Massey Jr. The Kolmogorov-Smirnov Test for Goodness of
Fit. In Journal of the American statistical Association. Taylor &
 D. Maxwell, S. R. Robinson, J. R. Williams, and C. Keaton. ”A
Short Story of a Lonely Guy”: A Qualitative Thematic Analysis
of Involuntary Celibacy Using Reddit. In Sexuality & Culture.
 L. Menzie. Stacys, Beckys, and Chads: The Construction of
Femininity and Hegemonic Masculinity Within Incel Rhetoric.
In Psychology & Sexuality. Taylor & Francis, 2020.
 M. A. Messner. The Limits of “The Male Sex Role” An Analy-
sis of the Men’s Liberation and Men’s Rights Movements’ Dis-
course. In Gender & Society. SAGE Publications, Inc., 1998.
 Mozila Foundation. Got youtube regrets? thousands do! https:
 A. Nagle. An Investigation into Contemporary Online Anti-
feminist Movements. PhD thesis, Dublin City University, 2015.
 E. Newell, D. Jurgens, H. Saleem, H. Vala, J. Sassine, C. Arm-
strong, and D. Ruths. User Migration in Online Social Networks:
A case study on Reddit during a period of Community Unrest. In
Proceedings of the International AAAI Conference on Web and
Social Media, 2016.
 C. O’Donovan, C. Warzel, L. McDonald, B. Clifton, and
M. Woolf. We Followed YouTube’s Recommendation Algorithm
Down The Rabbit Hole. https://www.buzzfeednews.com/article/
 A. of Internet Research. Internet Research: Ethical Guidelines
3.0. https://aoir.org/reports/ethics3.pdf, 2019.
 A. Ohlheiser. Inside the online world of ”incels,” the dark corner
of the Internet linked to the Toronto suspect. https://tinyurl.com/
 R. Ottoni, E. Cunha, G. Magno, P. Bernardina, W. Meira Jr, and
V. Almeida. Analyzing Right-wing YouTube Channels: Hate,
Violence and Discrimination. In Proceedings of the 10th ACM
Conference on Web Science, 2018.
 K. Papadamou, A. Papasavva, S. Zannettou, J. Blackburn,
N. Kourtellis, I. Leontiadis, G. Stringhini, and M. Sirivianos.
Disturbed YouTube for Kids: Characterizing and Detecting In-
appropriate Videos Targeting Young Children. In Proceedings
of the International AAAI Conference on Web and Social Media,
 E. Pariser. The Filter Bubble: How the New Personalized Web is
Changing What We Read and How We Think. Penguin, 2011.
 Rational Wiki. Incel. https://rationalwiki.org/wiki/Incel, 2019.
 M. H. Ribeiro, J. Blackburn, B. Bradlyn, E. De Cristofaro,
G. Stringhini, S. Long, S. Greenberg, and S. Zannettou. The
Evolution of the Manosphere Across the Web. In Proceedings
of the International AAAI Conference on Web and Social Media,
 M. H. Ribeiro, S. Jhaver, S. Zannettou, J. Blackburn,
E. De Cristofaro, G. Stringhini, and R. West. Does Platform
Migration Compromise Content Moderation? Evidence from
r/The Donald and r/Incels. In arXiv preprint 2010.10397, 2020.
 M. H. Ribeiro, R. Ottoni, R. West, V. A. Almeida, and
W. Meira Jr. Auditing Radicalization Pathways on YouTube. In
Proceedings of the 2020 Conference on Fairness, Accountability,
and Transparency, 2020.
 C. M. Rivers and B. L. Lewis. Ethical Research Standards in a
World of Big Data. In F1000Research, volume 3, 2014.
 A. Robertson. Reddit has broadened its anti-harassment rules
and banned a major incel forum. https://www.theverge.com/
 K. Roose. The Making of a YouTube Radical. https://tinyurl.
 S. Sara, K. Lah, S. Almasy, and R. Ellis. Oregon shooting: Gun-
man a student at Umpqua Community College. https://tinyurl.
 C. St¨
ocker and M. Preuss. Riding the Wave of Misclassiﬁca-
tion: How We End up with Extreme YouTube Content. In Inter-
national Conference on Human-Computer Interaction. Springer,
 J. Stray. Aligning ai optimization to community well-being.
International Journal of Community Well-Being, 3(4):443–463,
 A. Sureka, P. Kumaraguru, A. Goyal, and S. Chhabra. Mining
YouTube to Discover Extremist Videos, Users and Hidden Com-
munities. In Asia Information Retrieval Symposium. Springer,
 R. Tahir, F. Ahmed, H. Saeed, S. Ali, F. Zaffar, and C. Wilson.
Bringing the Kid back into YouTube Kids: Detecting Inappropri-
ate Content on Video Streaming Platforms. In IEEE/ACM Inter-
national Conference on Advances in Social Networks Analysis
and Mining (ASONAM). IEEE, 2019.
 T. Y. Team. Our ongoing work to tackle hate. https://blog.
 Z. Tufekci. YouTube, The Great Radicalizer. In The New York
 M. Vijaymeena and K. Kavitha. A Survey on Similarity Mea-
sures in Text Mining. In Machine Learning and Applications:
An International Journal (MLAIJ), 2016.
 M. Weaver and S. Morris. Plymouth gunman: a hate-ﬁlled
misogynist and ‘Incel’. https://www.theguardian.com/uk-
 Wikipedia. 2014 isla vista killings. https://en.wikipedia.org/
wiki/2014 Isla Vista killings, 2019.
 L. Wotanis and L. McMillan. Performing Gender on YouTube:
How Jenna Marbles Negotiates a Hostile Online Environment.
Taylor & Francis, 2014.
 S. Zannettou, S. Chatzis, K. Papadamou, and M. Sirivianos. The
Good, the Bad and the Bait: Detecting and Characterizing Click-
bait on YouTube. In 2018 IEEE Security and Privacy Workshops
(SPW). IEEE, 2018.
 Z. Zhao, L. Hong, L. Wei, J. Chen, A. Nath, S. Andrews,
A. Kumthekar, M. Sathiamoorthy, X. Yi, and E. Chi. Recom-
mending What Video to Watch Next: A Multitask Ranking Sys-
tem. In Proceedings of the 13th ACM Conference on Recom-
mender Systems, 2019.
 S. Zimmerman, L. Ryan, and D. Duriesmith. Recognizing the
violent extremist ideology of ’incels’. In Women in International
Security Policy, 2018.