Auditing Radicalization Pathways on YouTube

Manoel Horta Ribeiro∗

EPFL
manoel.hortaribeiro@ep�.ch

Raphael Ottoni
UFMG

rapha@dcc.ufmg.br

Robert West
EPFL

robert.west@ep�.ch

Virgílio A. F. Almeida
UFMG, Berkman Klein Center

virgilio@dcc.ufmg.br

Wagner Meira Jr.
UFMG

meira@dcc.ufmg.br

ABSTRACT

Non-pro�ts, as well as the media, have hypothesized the existence

of a radicalization pipeline on YouTube, claiming that users system-

atically progress towards more extreme content on the platform.

Yet, there is to date no substantial quantitative evidence of this

alleged pipeline. To close this gap, we conduct a large-scale audit of

user radicalization on YouTube. We analyze 330,925 videos posted

on 349 channels, which we broadly classi�ed into four types: Media,

the Alt-lite, the Intellectual DarkWeb (I.D.W.), and the Alt-right. Ac-

cording to the aforementioned radicalization hypothesis, channels

in the I.D.W. and the Alt-lite serve as gateways to fringe far-right

ideology, here represented by Alt-right channels. Processing 72M+

comments, we show that the three channel types indeed increas-

ingly share the same user base; that users consistently migrate

from milder to more extreme content; and that a large percentage

of users who consume Alt-right content now consumed Alt-lite

and I.D.W. content in the past. We also probe YouTube’s recom-

mendation algorithm, looking at more than 2M video and channel

recommendations between May/July 2019. We �nd that Alt-lite con-

tent is easily reachable from I.D.W. channels, while Alt-right videos

are reachable only through channel recommendations. Overall, we

paint a comprehensive picture of user radicalization on YouTube.

CCS CONCEPTS

•Human-centered computing→ Empirical studies in collab-

orative and social computing.

KEYWORDS

Radicalization, hate speech, extremism, algorithmic auditing

∗Work done mostly while at UFMG.

1 INTRODUCTION

Video channels that discuss social, political and cultural subjects

have �ourished on YouTube. Frequently, the videos posted in such

channels focus on highly controversial topics such as race, gender,

and religion. The users who create and post such videos span a wide

spectrum of political orientation, from proli�c podcast hosts like

Joe Rogan to outspoken advocates of white supremacy like Richard

Spencer. These individuals not only share the same platform but

often publicly engage in debates and conversations with each other

on the website [24]. This way, even distant personalities can be

linked in chains of pairwise co-appearances. For instance, Joe Rogan

interviewed YouTuber Carl Benjamin [35], who debated with white

supremacist Richard Spencer [6].

According to Lewis [24], this proximity may create “radicaliza-

tion pathways” for audience members and content creators. Exam-

ples of these journeys are plenty, including content creator Roosh

V’s trajectory from pick-up artist to Alt-right supporter [23, 37] and

Caleb Cain’s testimony of his YouTube-driven radicalization [36].

The claim that there is a “radicalization pipeline” on YouTube

should be considered in the context of decreasing trust in main-

stream media and increasing in�uence of social networks. Across

the globe, individuals are skeptical of traditional media vehicles

and growingly consume news and opinion content on social me-

dia [21, 31]. In this setting, recent research has shown that fringe

websites (e.g., 4chan) and subreddits (e.g., /r/TheDonald) have great

in�uence over which memes [43] and news [44] are shared in large

social networks, such as Twitter. YouTube is extremely popular,

especially among children and teenagers [5], and if the streaming

website is actually radicalizing individuals this could push fringe

ideologies like white supremacy further into the mainstream [41].

A key issue in dealing with topics like radicalization and hate

speech is the lack of agreement over what is “hateful” or “ex-

treme” [38]. A workaround is to perform analyses based on com-

munities, large sets of loosely associated content creators (here

represented by their YouTube channels). For the purpose of this

work, we consider three “communities” that have been associated

with user radicalization [24, 36, 42] and that di�er in the extremity

of their content: the “Intellectual Dark Web” (I.D.W.), the “Alt-lite”

and the “Alt-right”. While users in the I.D.W. discuss controversial

subjects like race and I.Q. [42] without necessarily endorsing ex-

treme views, members of the Alt-right sponsor fringe ideas like

that of a white ethnostate [18]. Somewhere in the middle, individ-

uals of the Alt-lite deny to embrace white supremacist ideology,

although they frequently �irt with concepts associated with it (e.g.,

the “Great Replacement”, globalist conspiracies).

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Horta Ribeiro et al.

Present work. In this paper, we audit whether users are indeed

becoming radicalized on YouTube and whether the recommenda-

tion algorithms contribute towards this radicalization. We do so

by examining three prominent communities: the Intellectual Dark

Web, the Alt-lite and the Alt-right. More speci�cally, considering

Alt-right content as a proxy for extreme content, we ask:

RQ1 How have these channels grown on YouTube in the last

decade?

RQ2 To which extent do users systematically gravitate towards

more extreme content?

RQ3 Do algorithmic recommendations steer users towards more

extreme content?

We develop a data collection process where we (i) acquire a large

pool of relevant channels from these communities; (ii) collect meta-

data and comments for each of the videos in the channels; (iii) an-

notate channels as belonging to several di�erent communities; and

(iv) collect YouTube video and channel recommendations. We also

collect traditional and alternative media channels for additional

comparisons. We use these as a sanity check to capture the growth

of other content on YouTube, rather than trying to obtain similar

users in other channels. These e�orts resulted in a dataset with

more than 72M comments in 330,925 videos of 349 channels and

with more than 2M video and 10K channel recommendations. Im-

portantly, our recommendations do not account for personalization.

We analyze this large dataset extensively:

• We look at the growth of the I.D.W., the Alt-lite and the

Alt-right throughout the last decade in terms of videos, likes

and views, �nding a steep rise in activity and engagement

in the communities of interest when compared with the

media channels. Moreover, comments per view seem to be

particularly high in more extreme content, reaching near to

1 comment for every 5 views in Alt-right channels in 2018

(Sec. 4).

• We inspect the intersection of commenting users across the

communities, �nding they increasingly share the same user

base. Analyzing the overlap between the sets of comment-

ing users, we �nd that approximately half of the users who

commented on Alt-right channels in 2018 also comment on

Alt-lite and on I.D.W. channels (Sec. 5).

• We also �nd that the intersection is not only growing due

to new users but that there is signi�cant user migration

among the communities being studied. Users that initially

comment only on content from the I.D.W. or the Alt-lite

throughout the years consistently start to comment on Alt-

right content. These users are a signi�cant fraction of the

Alt-right commenting user base. This e�ect is much stronger

than for the large traditional and alternative media channels

we collected (Sec. 6).

• Lastly, we take a look at the impact of YouTube’s recommen-

dation algorithms, running simulations on recommendation

graphs. Our analyses show that, particularly through the

channel recommender system, Alt-lite channels are easily

discovered from I.D.W. channels, and that Alt-right channels

may be reached from the two other communities (Sec. 7).

This is, to our best knowledge, the �rst large scale quantitative

audit of user radicalization on YouTube. We �nd strong evidence

for radicalization among YouTube users, and that YouTube’s recom-

mender system enables Alt-right channels to be discovered, even

in a scenario without personalization. We discuss our �ndings and

our limitations further in Sec. 8. We argue that commenting users

are a good enough proxy to measure the user radicalization, as

more extreme content seems to beget more comments. Moreover,

regardless of the degree of in�uence of the recommender system in

the process of radicalizing users, there is signi�cant evidence that

users are reaching content sponsoring fringe ideologies from the

Alt-lite and the Intellectual Dark Web.

2 BACKGROUND

Contrarian communities. We discuss three of YouTube’s promi-

nent communities: the Intellectual Dark Web, the Alt-lite and the

Alt-right. We argue that all of them are contrarians, in the sense

that they often oppose mainstream views or attitudes. According

to Nagle, these communities �ourished in the wave of “anti-PC”

culture of the 2010s, where social-political movements (e.g. the

transgender rights movement, the anti-sexual assault movement)

were portrayed as hysterical, and their claims, as absurd [30].

According to the Anti Defamation League [3], the Alt-Right is a

loose segment of the white supremacist movement consisting of

individuals who reject mainstream conservatism in favor of politics

that embrace racist, anti-Semitic and white supremacist ideology.

The Alt-right skews younger than other far-right groups, and has

a big online presence, particularly on fringe web sites like 4chan,

8chan and certain corners of Reddit [2].

The term Alt-lite was created to di�erentiate right-wing activists

who deny embracing white supremacist ideology. Atkison argues

that the Unite the Rally in Charlottesville was deeply related to this

change, as participants of the rally revealed the movement’s white

supremacist leanings and a�liations [8]. Alt-right writer and white

supremacist Greg Johnson [3] describes the di�erence between

Alt-right and Alt-lite by the origin of its nationalism: "The Alt-

lite is de�ned by civic nationalism as opposed to racial nationalism,

which is a de�ning characteristic of the Alt-right". This distinction

was also highlighted in [28]. Yet it is important to point out that

the line between the Alt-right and the Alt-lite is blurry [3], as

many Alt-liters are accused of dog-whistling: attenuating their real

beliefs to appeal to a more general public and to prevent getting

banned [22, 25]. To address this problem, in this paper we take

a conservative approach to our labeling, naming only the most

extreme content creators as Alt-right.

The “Intellectual Dark Web” (I.D.W.) is a term coined by Eric

Weinstein to refer to a group of academics and podcast hosts [42].

The neologism was popularized in a New York Times opinion arti-

cle [42], where it is used to describe “iconoclastic thinkers, academic

renegades and media personalities who are having a rolling con-

versation about all sorts of subjects, [. . . ] touching on controversial

issues such as abortion, biological di�erences between men and

women, identity politics, religion, immigration, etc.”

The group described in the NYT piece includes, among others,

Sam Harris, Jordan Peterson, Ben Shapiro, Dave Rubin, and Joe



Auditing Radicalization Pathways on YouTube

Rogan, and also mentions a website with an uno�cial list of mem-

bers [7]. Members of the so-called I.D.W. have been accused of

espousing politically incorrect ideas [9, 15, 26]. Moreover, a re-

cent report by the Data & Society Research Institute has claimed

these channels are “pathways to radicalization” [24], acting as entry

points to more radical channels, such as those in Alt-right. Broadly,

members of this loosely de�ned movement see these criticisms as

a consequence of discussing controversial subjects [42], and some

have explicitly dismissed the report [40]. Similarly to what happens

between Alt-right and Alt-lite, there are also blurry lines between

the I.D.W. and the Alt-lite, especially for non-core members, such

as those listed on the aforementioned website [7]. To break ties, we

label borderline cases as Alt-lite.

Radicalization. We consider the de�nition given by McCauley

and Moskalenko [29]: (“Functionally, political radicalization is in-

creased preparation for and commitment to intergroup con�ict.

Descriptively, radicalization means change in beliefs, feelings, and

behaviors in directions that increasingly justify intergroup violence

and demand sacri�ce in defense of the ingroup.”) and use increased

consumption of Alt-right content as a proxy for radicalization. This

is reasonable since the Alt-right’s rhetoric has been invoked by the

perpetrators of some recent terrorist attacks (e.g. the Christchurch

mosque shooting [27]), and since it champions ideas promoting

intergroup con�ict (e.g. a white ethnostate [18]). Our conserva-

tive strategy when labeling channels is of particular importance

here: Alt-right channels are closely related to these ideas, while the

Alt-lite/I.D.W. are given the bene�t of doubt.

Auditing the web. As algorithms play an ever-larger role in our

lives, it is increasingly important for researchers and society at large

to reverse engineer algorithms’ input-output relationships [13]. Pre-

vious large scale algorithmic auditing include measuring discrimi-

nation on AirBnB [14], personalization onweb search [19] and price

discrimination on e-commerce web sites [20]. We argue this work

is an audit in the sense that it measures a troublesome phenome-

non (user radicalization) in a content-sharing social environment

heavily in�uenced by algorithms (YouTube). Unfortunately, it is

not possible to obtain the entire history of YouTube recommenda-

tion, so we must limit the algorithmic analyses to a time slice of

a constantly changing black-box. Although comments may give

us insight into the past, it is challenging to tease apart the in�u-

ence of the algorithm in previous times. Another limitation of our

auditing is that we do not account for user personalization. De-

spite these �aws, we argue that: (i) our analyses provide answers

to important questions related with impactful societal processes

that are allegedly happening in YouTube (regardless of the impact

of the recommender system), and (ii) our framework for auditing

user radicalization can be replicated through time and expanded to

handle personalization.

Previous research from/on YouTube. Previous work by Google

sheds light into some of the high-level technicalities of YouTube’s

recommender system [11, 12]. Their latest paper indicates they

use embeddings for video searches and video histories as inputs

for a dense neural network [12]. There also exists a large body of

work studying violent [16], hateful or extremist [4, 39] and dis-

turbing content [34] on the platform. Much of the existing work

focuses on creating detection algorithms for these types of content

using features of the comments, the commenting users and the

videos [4, 16]. Sureka et al. [39] use a seed-expanding methodology

to track extremist user communities, which yielded high precision

in including relevant users. This is somewhat analogous to what

we do, although we use YouTube’s recommender system while they

use user friends, subscriptions and favorites. Ottoni et al. perform

an in-depth textual analysis of 23 channels (13 broadly de�ned

as Alt-right), �nding signi�cantly di�erent topics across the two

groups [32]. O’Callegan et al. [33] simulate a recommender sys-

tem with channels tweeted in an extreme right dataset. They show

that a simple non-negative matrix factorization metadata-based

recommender system would cluster extreme right topics together.

3 DATA COLLECTION

We are interested in three communities on YouTube: the I.D.W., the

Alt-lite, and the Alt-right. Identifying such communities and the

channels which belong to them is no easy task: the membership of

channels to these communities is volatile and fuzzy, and there is

disagreement between how members of these communities view

themselves, and how they are considered by scholars and the media.

These particularities make our challengemulti-faceted: on one hand,

we want to study user radicalization, and determine, for example,

if users who start watching videos by communities like the I.D.W.

eventually go on to consume Alt-right content. On the other, there

is often no clear agreement on who belongs to which community.

Due to these nuances, we devise a careful methodology to (a) col-

lect a large pool of relevant channels; (b) collect data and the rec-

ommendations given by YouTube for these channels; (c) manually

labeling these channels according to the communities of interest.

(a) For each community, we create a pool of channels as follows.

We refer to channels obtained in the ğ-th step as Type ğ channels.

(1) We choose a set of seed channels. Seeds were extracted from the

I.D.W. uno�cial website [7], Anti Defamation League’s report

on the Alt-lite/the Alt-right [3] and Data & Society’s report on

YouTube Radicalization [24]. We pick popular channels that

are representative of the community we are interested in. Each

seed was independently annotated two times and discarded

in case there was any disagreement. We further detail the

annotation process later in this section.

(2) We choose a set of keywords related to the sub-communities.

For each keyword, we use YouTube’s search functionality and

consider the �rst 200 results in English. We then add channels

that broadly relate in topic to the community in question. For

example, for the Alt-right, keywords included both terms asso-

ciated with their narratives, such as The Jewish Question and

White Genocide, as well as the names or nicknames of famous

Alt-righters, such as weev and Christopher Cantwell.

(3) We iteratively search the related and featured channels col-

lected in steps (1) and (2), adding relevant channels (as de�ned

in 2). Note that these are two ways channel can link to each

other. Featured channels may be chosen by YouTube content

creators: if your friend has a channel and you want to support

it, you can put it on your "Featured Channels" tab. Related

channels are created by YouTube’s recommender system.

(4) We repeat step (3), iteratively collecting another hop of fea-

tured/recommended channels from those obtained in (3).



Horta Ribeiro et al.

Table 1: Top 16 YouTube channels with the most views per each community and for media channels.

Alt-right Views Alt-lite View Intellectual Dark Web Views Media Views

1 James Allsup 62M StevenCrowder 727M PowerfulJRE 1B vox 1B

2 Black Pigeon Speaks 50M Rebel Media 405M JRE Clips 717M gq magazine 1B

3 ThuleanPerspective 45M Paul Joseph Watson 356M PragerUniversity 635M vice news 1B

4 Red Ice TV 42M MarkDice 334M The Daily Wire 247M wired magazine 1B

5 The Golden One 12M SargonofAkkad100 258M The Rubin Report 206M vanity fair 639M

6 AmRenVideos 9M Stefan Molyneux 193M ReasonTV 138M the verge 636M

7 NeatoBurrito Productions 7M hOrnsticles3 145M JordanPetersonVideos 90M glamour magazine 620M

8 The Last Stand 7M MILO 133M Bite-sized Philosophy 62M business insider 523M

9 MillennialWoes 6M Styxhexenhammer666 132M Owen Benjamin 35M hu�ngton post 329M

10 Mark Collett 6M OneTruth4Life 112M AgatanFoundation 33M today i found out 328M

11 AustralianRealist 5M No Bullshit 104M Essential Truth 32M cbc news 324M

12 Jean-François Gariépy 5M SJWCentral 90M Ben Shapiro 30M the guardian 300M

13 Prince of Zimbabwe 5M Computing Forever 87M YAFTV 30M people magazine 287M

14 The Alternative Hypothesis 5M The Thinkery 86M joerogandotnet 25M big think 258M

15 Matthew North 4M Bearing 81M TheArchangel911 24M cosmopolitan 256M

16 Faith J Goldy 4M RobinHoodUKIP 64M Clash of Ideas 24M global news 252M

The annotation process done here followed the same instructions as

the one explained in detail for data collection step (c). Steps (2)—(4),

were done by a co-author with more than 50 hours of watch-time

of the communities of interest. Notice that, in steps (2)—(4), we

are not labeling the channels, but creating a pool of channels to be

further inspected and labeled in subsequent steps. The complete

list of seeds obtained from (1) and of keywords used in (2) may

be found in Appendix A. A clear distinction between featured and

recommended channels may be found in Appendix B.

(b) For each channel, we collect the number of subscribers and

views, and for their videos, all the comments and captions. Video

and channel recommendations were collected separately using

custom-made crawlers. We collected multiple "rounds" of recom-

mendations, 22 for channel recommendations and 19 for video

recommendations. Each "round" consists of collecting all recom-

mended channels (on the channel web page) and all recommended

videos (on the video web page). To circumvent possible location

bias in the data we collected we used VPNs from 7 di�erent loca-

tions: 3 in the USA, 2 in Canada, 1 in Switzerland and 1 in Brazil.

Moreover, channels were always visited in random order, to prevent

any biases from arising from session-based recommendations. As

we extensively discuss throughout the paper, this does not include

personalization, as we do not log in into any account.

(c) Channel labeling was done in multiple steps. All channels are

either seeds (Type 1) or obtained through YouTube’s recommen-

dation/search engine (Types 2 and 3). Notice that Type 1 channels

were assigned labels at the time of their collection. For the others,

we had 2 of the authors annotate them carefully. They both had

signi�cant experience with the communities being studied, and

were given the following instructions:

Carefully inspect each one of the channels in this table, tak-

ing a look at the most popular videos, and watching, alto-

gether, at least 5 minutes of content from that channel. Then

you should decide if the channel belongs to the Alt-right, the

Alt-lite, the Intellectual Dark Web (I.D.W.), or whether you

think it doesn’t �t any of the communities. To get a grasp

on who belongs to the I.D.W., read [42], and check out the

website with some of the alleged members of the group [7].

Yet, we ask you to consider the label holistically, including

channels that have content from these creators and with a

similar spirit to also belong in this category. To distinguish

between the Alt-right and the Alt-lite, read [3] and [28]. It

is important to stress the di�erence between civic national-

ism and racial nationalism in that case. Please consider the

Alt-right label only to the most extreme content. You are

encouraged to search on the internet for the name of the

content creator to help you make your decision.

The annotation process lasted for 3 weeks. In case they disagreed,

they had to discuss the cases individually until a conclusion was

reached. Interanotator agreementwas of 75.57% (95%CI [67.5, 82.5]).

We ended up with 85 I.D.W., 112 Alt-lite and 84 Alt-right channels.

Media. We also collect popular media channels. These were ob-

tained from the mediabiasfactcheck.com [1]. For each media source

of the categories on the website (Left, Left-Center, Center, Right-

Center, Right) we search for its name on YouTube and consider it if

there is a match in the �rst page of results [1]. Some of the channels

were not considered because they had too many videos (15, 000+)

and we were not able to retrieve them all (which is important, be-

cause our analyses are temporal). In total, we collect 68 channels

that way. We use these media channels as a sanity check to capture

general trends among more mainstream YouTube channels.

We summarize the dataset collected in the Tab. 2. Data collection

was performed during the 19-30th of May 2019, and the collection

of the recommendations between May-July 2019.

Table 2: Overview of our dataset.

Channels 349 Video Recs rounds 19

Videos 330,925 Video Recs 2,474,044

Comments 72,069,878 Channel Recs Rounds 22

Commenting users 5,980,709 Channel Recs 14,283



Auditing Radicalization Pathways on YouTube

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Figure 1: On the top row �gures (a)—(e), for each community and media channels, we have the cumulative number of active

channels (that posted at least one video), of videos published, of likes, views and of comments. In the bottom row, we have

engagement metrics (accumulated over time), (�gures (f), (g), (i) and (j)) and the CCDF of videos published, zoomed in the

range [40%, 100%] on the y-axis (�gure (h)). Notice that for comments, we know only the year when they were published, and

thus the CDFs granularity is coarser (years rather than seconds). The raw numbers of views, likes, videos published and more

are shown in Appendix C

4 THE RISE OF CONTRARIANS

We present an overview of the channels in the communities of

interest, and show results about their growth in the last years,

setting the stage to more in-depth analyses in later sections. Tab. 1

shows the 16 most viewed YouTubers for each of the communities

and for the media channels, and Figure 1 shows information on

the number of videos published, channels created, likes, views, and

comments per year, as well as several engagement metrics.

Recent rise in activity. Figs. 1(a)—(e) show the rise in channel

creation, video publishing, likes, views, and comments in the last

decade. The four latter are growing exponentially for all the commu-

nities of interest and for the media channels. Noticeably, the rise in

the number of active channels is much more recent for the commu-

nities of interest than for media channels, as shown in Fig. 1(a). In

mid 2015, for example, 66 out of the 68 of the media channels were

active (posted their �rst video), while less than 50% of the Alt-lite,

Alt-right and I.D.W. channels had done so. This growth in the com-

munities of interest during 2015 may also be noted in Fig. 1(i), which

shows the CDF of number comments per videos, and can also be

seen between early 2014 and late 2016 in Figs. 1(f)—(g), which show

the number of likes and views per video, respectively. Notice that

the number of likes and views is obtained during data collection,

and thus, it might be that older videos from those channels became

popular later. Altogether, our data corroborates with the narrative

that these communities gained traction in (and forti�ed) Donald

Trump’s campaign during the 2016 presidential elections [10, 17].

Engagement. A key di�erence between the communities of inter-

est and the media channels is the level of engagement with the

videos, as portrayed by the number of likes per video, comments

per video and comments per view, shown in Figs. 1 (f), (i), and

(j), respectively. For all these metrics, the communities of interest

have more engagement than the media channels: Although media

channels have more views per video, as shown in Figs. 1(g), these

views are less often converted into likes and comments. Notably,

Alt-right channels have, since 2017, become the ones with the high-

est number of comments per view, with nearly 1 comment per 5

views by 2018.

Dormant Alt-right Channels. Although by 2013, approximately

the same number of channels of all three communities had become

active (∼ 30), as it can be seen in Fig. 1(a), the number of videos

they published by the Alt-right was low before 2016. This can be

seen in the CCDF in Fig. 1(h): while media and Alt-lite channels had

published nearly 40% of their content, the Alt-right had published

a bit more than 20%. This is not because the most popular channels

did not yet exist: 4 out of the 5 current top Alt-right channels

(accumulating approximately 150Mviews) had already been created

by 2013. Moreover, it is noteworthy that many of the channels now

dedicated to Alt-right content have initial videos related to other

subjects. Take for example the channel “The Golden One”, number

5 in Tab. 1. Most of the initial videos in the channel are about

working out or video-games, with politics related videos becoming

increasingly occurring. The growth in engagement metrics such as

likes per video and comments per video of the Alt-right succeeds

that of the I.D.W. and of the Alt-lite, resonating with the narrative

that the rise of Alt-Lite and I.D.W. channels created fertile grounds

for individuals with fringe ideas to prosper [24, 30].

Although our data-driven analysis sheds light on existing nar-

ratives on the communities of interest, it is still impossible to de-

termine, from these simple CDFs, whether there is a radicalization

pipeline. To do so, in the following two sections, we dig deeper into

the relationship between these communities looking closely at the

users who commented on them.



Horta Ribeiro et al.

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08 09 10 11 12 13 14 15 16 17 18

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Figure 2: In (a), the number of unique commenting users per year in the top plot and the CDF of comments per user for each

one of the communities in the bottom plot. In (b)—(d) we show two similarity metrics (Jaccard and Overlap Coe�cient) for

di�erent pairs of sets of commenting users across the years. In (b) these pairs are the sets of users of each community in

subsequent years. In (c) these pairs are the sets of users of each one of the communities of interest. In (d) these pairs are the

sets of users of the communities compared with the users who commented in media channels.

5 USER INTERSECTION

We begin our in-depth analysis of users who commented on the

channels of interest by analysing the intersection between the users

in di�erent channels and communities. In that context, we use

two set similarity metrics: the Jaccard Similarity
|ý∩þ |
|ý∪þ |

; and the

Overlap Coe�cient
|ý∩þ |

min( |ý |, |þ |)
. Notice that the overlap coe�cient

is particularly useful to compare communities of di�erent sizes.

For example, a small subset of a large set may yield low Jaccard

Similarity, but will necessarily yield an Overlap Coe�cient of 1.

Column (a) of Fig. 2 characterizes commenting users. The top plot

shows the absolute number of commenting users per year, while

the bottom one shows the CDF of the number of comments per user

per community. It is interesting to compare these plots with that of

Fig. 1(e), as we can see that the communities of interest have many

more highly active commenters. This supports the hypothesis that

users who consume content in the communities of interest are more

"engaged" than those who consume the content from the media

channels. Notice also that, although the Alt-right commenters have,

on average, fewer comments than those in Alt-lite or the I.D.W., the

community is much younger (as discussed in Sec. 4), and thus it is

hard to tell whether their users are less engaged.

In columns (b)—(d) of Fig. 2 we consider the intersection between

the commenting users of the I.D.W., the Alt-lite, the Alt-right and

media channels. The top �gure for each column shows the Jaccard

Similarity and the bottom one shows the Overlap Coe�cient.

Column (b) in Fig. 2 shows the similarity measures for a commu-

nity with itself a year before (which here we name self-similarity).

We �nd that the retention of users among the three communities is

growing with time for both metrics. However, for media channels,

we �nd that the Jaccard similarity is plateauing since 2014 and that

the overlap coe�cient only recently started to grow, perhaps due

to the sharp increase in commenting users since 2015. Commenting

users from the communities of interest seem to go back more often

than those in media channels.

Column (c) in Fig. 2 shows the pairwise similarity between the

three communities. Notably, in 2018, the Jaccard Similarity between

the Alt-lite and the I.D.W. reached almost 30%, which is more than

the self-similarity between the two communities. Moreover, the

Overlap Coe�cient of the Alt-right with the Alt-lite and the I.D.W

is high: reaching around 50% in 2018. This means around half of

the users who commented in Alt-right channels commented in the

other communities.

Lastly, column (d) in Fig. 2 shows the similarity of the three com-

munities with the media channels. We have that the Jaccard simi-

larity between the I.D.W. and the Alt-lite and the media channels is

not so di�erent from the similarity between these communities and

the Alt-right. This is a subtle �nding. On one hand, it means that

individuals in these communities make up a signi�cant portion of

the massive media channels we collected, which gather billions of

views. These communities do not exist in a vacuum but are part of

the existing online information environment. On the other, it shows

that the Alt-right, a group of channels with order of magnitudes

fewer views, subscribers and comments, are actually on par with

these large channels. Inspecting the Overlap Coe�cient, however,

we get a di�erent view: there we have that the communities overlap

more with themselves than with the media channels, particularly

since 2015. However, in 2018, there is a sharp growth in the simi-

larity with media channels. A hypothesis for this is that, as these

channels grew more popular (as previously discussed in Sec. 4, they

became more mainstream).

These analyses take us one step further in understanding the

communities being studied. We again see that their users are more

engaged, and, notably, �nd that the I.D.W, the Alt-lite, and the

Alt-right increasingly share the same commenting user base.



Auditing Radicalization Pathways on YouTube

0%

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Start: 2006-2012 Start: 2013-2015

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2013-2015 2016 2017 2018 2016 2017 2018 2017 2018

Figure 3: We show how users migrate towards Alt-right content. For users who consumed only videos in the communities

indicated by the labels in the rows (Alt-lite or I.D.W., Alt-lite, I.D.W., and Media), we show the chance that they go on to

consume Alt-right content. We consider three levels of exposure: light (commented in 1 to 2 Alt-right videos), mild (3 to 5) and

severe (6+). Each column tracks users on a di�erent starting date. Initially, their exposure rates are 0 (as they did not consume

anyAlt-right content). As time passes, we show the exposure rates in the y-axis, for each of the years, in the x-axis. Line widths

represent 95% con�dence intervals.

6 USER MIGRATION

In the previous section, we showed that the commenting user bases

among the I.D.W., the Alt-lite, and the Alt-right are increasingly

similar —and the e�ect is stronger than for media channels. This

indicates that there is a growing percentage of users consuming ex-

treme (Alt-right) content on YouTube while also consuming content

from other milder communities (Alt-lite/I.D.W.). Yet, it does not, per

se, indicate that there is a radicalization pipeline on the website. It

could be, for example, that new users who join the website go on to

consume content from all three communities. To better address this

question, we �nd users who did not comment in Alt-right content

in a given year and track their subsequent activity. Notice that we

do not have the user’s entire activity history, and thus, we track

their activity only in the channels whose videos we collected.

For four time brackets [(2006−2012), (2013−2015), (2016), (2017)]

we track four sets of users: those who only commented on videos

of the Alt-lite or the I.D.W, those who did so only for videos on

the Alt-lite, those who did so only for videos on the I.D.W., and

those who commented only on videos of the media channels. Then,

for subsequent years, we track the same users. Notice that when

users are tracked for one year they are not eligible for selection

in upcoming years. We consider these users to be exposed if they

commented on 1-2 (light), 3-5 (mild) or 6+ (severe) Alt-right videos.

The results for this analysis are shown in Fig. 3. We show the

percentage of users we managed to track that were exposed. The

number of users tracked and exposed at each step may be found

in Appendix C. Consider, for example, users who on 2006 − 2012

commented only on I.D.W. or Alt-lite content (227,945 users), as

shown in the subplot in the �rst column and the �rst row. By

2018, around 10% were lightly exposed, and roughly 4% severely

or mildly so — which amounts to approximately 9K users in total.

From the ones who in 2017 commented only on Alt-lite or I.D.W.

videos (1,251,674 users), as shown in the last column of the �rst

row, approximately 12% of them were exposed — more than 60K

users altogether.

We also �nd that media channels present lower exposure rates, as

can be seen in the last row of the �gure. The di�erence is particularly

large for the last three time brackets. Less than 1% of users in media

channels were mildly or severely exposed, against 3% to 4% for

Alt-lite or I.D.W. users, and roughly 4% were lightly exposed versus

approximately 8% for Alt-lite or I.D.W users.

When teasing apart users that commented only on Alt-lite or

only on I.D.W. content, we �nd that, not only users who commented

only on I.D.W. get less exposed, but increasingly less so. The same

applies to the media channels. For example, the exposure rates of

users who watched only Alt-lite (second row) or only I.D.W. (third

row) content are much more similar for those tracked in 2006−2012

(�rst column) than for those tracked in 2017 (last column). For users

who were tracked in 2006− 2012, around 15% were exposed in both

scenarios, while for those tracked in 2017, this di�erence grew

farther apart (∼ 12% Alt-lite vs. ∼ 6% I.D.W.).

The previous study suggests that the pipeline e�ect does exist,

and that indeed, users systematically go frommilder communities to

the Alt-right. However, it does not give insight into how expressive

the e�ect is in terms of what part of the Alt-right user base has

gone through it. We address this question by tracking users exactly

as we did before, and then analyzing what percentage of exposed



Horta Ribeiro et al.

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(0.0%) (5.9%) (20.4%) (33.0%) (37.8%)

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(0.0%) (7.0%) (25.2%) (38.9%) (44.9%)

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Start: 2006-2012 Start: 2013-2015 Start: 2016 Start: 2017

(0.0%) (8.1%) (25.1%) (36.1%) (43.0%)

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(0.0%) (1.1%) (3.8%) (5.2%) (7.0%) (0.0%) (1.2%) (4.2%) (5.3%) (6.7%) (0.0%) (1.3%) (3.8%) (4.7%) (5.6%)

2006-2012 2013-2015 2016 2017 2018

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2006-2012 2013-2015 2016 2017 2018

(0.0%) (2.3%) (3.7%) (4.0%) (4.9%)

2006-2012 2013-2015 2016 2017 2018

(0.0%) (2.5%) (3.4%) (3.5%) (4.1%)

Figure 4:We showhowexpressive the tracked users are in terms of theAlt-right user base. Each row shows a di�erent condition

for tracking users and each column shows a di�erent level of exposure. Each line corresponds to users tracked at a di�erent

starting date (in the x-axis), and the y-axis shows the percentage of the total Alt-right commenting users they went to become

(notice that all lines begin at 0, because initially they did not consume any Alt-right content).

users at each year can be traced back to users who initially watched

content from other communities. In other terms, for each year we

calculate, of the users who are exposed (i.e. who watched Alt-right

videos), which percentage belongs to each one of the sets of tracked

users we just described.

The results for this analysis are shown in Fig. 4. We �nd that

these users are a considerable fraction of the Alt-right comment-

ing audience. In 2018, for all kinds of exposure, roughly 40% of

commenting users can be traced back from cohorts of users that

commented only on Alt-lite or I.D.W. videos in the past. This can be

seen in the �rst row of the plot. Moreover, we can observe that, con-

sistently, users who consumed Alt-lite or I.D.W. content in a given

year, go on to become a signi�cant fraction of the Alt-right user

base in the following year. This number is much more expressive

than the number of users which came from media channels — in

the last row — which never surpasses 6% for any level of exposure.

Looking at the second and third row of Fig. 4, we �nd a substan-

tial di�erence between the I.D.W. and the Alt-lite. Whereas in Sec. 5

we �nd that the intersection between them both and the Alt-right

are similar, here we see that users who initially commented only

on I.D.W. channels constitute a much less signi�cant percentage

of the Alt-right consumer base in upcoming years. For all levels

of exposure, at all times, the number of exposed users that can

be traced back to commenting exclusively on I.D.W. channels is

around 3 times lower. So, while in 2018, 23.3% of users who were

lightly exposed can be traced back to users who commented on

Alt-lite channels in previous years, only 7.6% can be traced back

to I.D.W. channels. Overall, in both analyses, users who consumed

only I.D.W. channel seem to behave more similarly to the users

in the media channels. Yet, as we see in Sec. 5, the intersection

between the Alt-lite and the I.D.W. is increasing with time, which

means this population is becoming less signi�cant.

The experiments performed show that, not only the commenting

user bases are becoming increasingly similar (as shown in Sec. 5),

but that, systematically, users who commented only on I.D.W. or

Alt-lite content go on to comment on Alt-right channels. This phe-

nomenon is signi�cant both in terms of the percentage of the users

tracked — as in Fig. 3 — and in terms of the total Alt-right comment-

ing user base — as in Fig. 4. We present the raw numbers associated

with these �gures in Appendix D.

7 THE RECOMMENDATION ALGORITHM

In this section, we inspect the impact of YouTube’s recommendation

algorithm. Unfortunately, we have only a snapshot of the recom-

mender system which does not take into account personalization.

Thus, it is hard to reach signi�cant conclusions on what was the

role of the recommender system in the radicalization process we

depicted in Sec. 6. Yet, we argue that analyzing these data is rele-

vant, for it is a blueprint of how the in�uence of the recommender

Table 3: Percentage of edges in-between communities in

the recommendation graphs (normalized per weight). Video

recommendations are in bold. Rows indicate the source of

edges columns indicate their destination.

Src|Dst I.D.W. Alt-lite Alt-right Media Other

I.D.W. 52.78/19.03 22.88/1.57 0/0.03 3.12/3.03 21.23/76.35
Alt-L 13.69/2.46 55.15/12.70 3.38/0.13 2.82/3.24 24.96/81.47
Alt-R 25.73/1.89 42.94/1.15 25.73/8.55 1.35/3.38 21.08/85.03
Media 4.94/0.31 4.36/0.08 0/0 28.78/14.84 61.92/84.77



Auditing Radicalization Pathways on YouTube

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id
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(b)

Figure 5: We show the results for the simulation of random walks for channels (a) and videos (b). We show two metrics, as

described in the text, the probability of the walker being in a given community at each step (solid line) and the reachability at

each step for a given community (dashed line). The di�erent columns portray di�erent starting rules for the initial node in

the simulations. Error bands are 95% con�dence intervals.

system may be measured, and because it allows us to understand

how the recommender system is behaving for our scenario.

We perform our analysis in a recommendation graph, built using

the data collected. The graph is built as follows: for each channel,

we join together all recommendations obtained in all rounds of

data collection. Each channel is a node, and edges between nodes

indicate recommendations from a channel to another (for both

video and channel recommendations). Notice that, in case there

was a recommendation towards a channel or a video we are not

aware of, we add an edge to a special sink node we name “Other”.

Each edge is weighted proportionally to the number of times that

recommendation appeared in the data collection, and weights are

normalized so that outgoing edges of each node sum up to 1.

The percentage of edges between communities (normalized by

their weight) is shown in Tab. 3 for channel and video recommenda-

tions. For channel recommendations, we have that media channels

are recommended scarcely by the communities of interest. In fact,

there are more edges �owing out of media channels towards Alt-

lite/I.D.W. channels than the other way around. Alt-lite and I.D.W.

channels recommend channels from the same community around

50% of the time, and recommend each other around 14% (Alt-L to

I.D.W.) and 23% (I.D.W. to Alt-L) of the time. Alt-right channels

are only recommended by Alt-lite channels (3.08%). For video rec-

ommendations, there is a high prevalence of recommendation to

videos we were not able to track (more than 75% of outgoing edges

from all communities pointed towards the “Other” node). We also

�nd that media channels are more often recommended in this set-

ting ( ∼ 3% for all communities), while the Alt-lite and the I.D.W.

recommend each other roughly 2% of the time. Lastly, Alt-right

videos are not signi�cantly recommended here.

Given these graphs, we experiment with random walks. The

random walker begins in a random node, chosen with chance pro-

portional to the number of subscribers in each channel. Then, the

random walker randomly navigates the graph for 5 steps, choosing

edges at random with probabilities proportional to their weights.

We store the random walks and calculate two metrics: (i) the proba-

bility of it being in a channel from each of the communities, that is,

the probability that there is a channel of a given community in the

ġ-th step. (ii) the reachability of each of the communities at step ġ .

That is, at step ġ , the percentage of times that the random walker

has found a node of a given community. We run the simulation 10K

times for scenarios where the initial node is restricted to one of the

three communities or the media channels.

Importantly, we consider a small di�erence in the experimental

set-up for each of the graphs. In the channel recommendation graph,

we allow the random walker to choose the “Other” node. When

this happens the walk stops, thus at each step there is a probability

this walk is interrupted by this — or by the fact that there are no

recommended channels. In the channel recommendation graph, as

the number of edges to the “Other” node is too high, we do not

allow the random walker to go towards it. Notice that the scenario

for the channels is more realistic, and we give more weight to the

conclusions drawn there. The two aforementioned metrics, at each

step, given di�erent starting conditions, are shown in Fig. 5, for

channel and video recommendations.

For channel recommendations, we have that the reachability@5

of Alt-right channels is of approximately 4% for the simulations

starting from Alt-lite 1.5% for I.D.W. channels. Moreover, starting

from an I.D.W. channel, users have approximately 10% of chance of

being in an Alt-lite channel at the next step, and in 5 steps, there is

25% of chance that the user has found at least one Alt-lite channel.



Horta Ribeiro et al.

Starting from themedia channels, reachability@5 of I.D.W. channels

is of 2.5%, and of slightly less than 1% for Alt-lite channels. These

can be seen on the bottom row of Fig. 5 (a).

For video recommendations, reaching Alt-right channels from

other communities is less likely. From the Alt-lite, reachability@5

is of around 0.05%. Going from the I.D.W. to the Alt-lite is more

di�cult: the reachability@5 is roughly 7%. More relevant, though,

starting from media channels, the reachability@5 of I.D.W. and

Alt-lite channels is of around 4.5% and 1.5% respectively. It is worth

recalling that this experiment is less realistic than the former, as

here we ignore the possibility of the random walker being in a

video we are not aware of.

Overall, we �nd that, in the channel recommender system, it is

easy to navigate from the I.D.W. to the Alt-lite (and vice-versa), and

it is possible to �nd Alt-right channels. From the Alt-lite we follow

the recommender system 5 times, approximately 1 out of each 25

times we will have spotted an Alt-right channel (as seen in Fig. 5

(a)). In the video recommender system, Alt-right channels are less

recommended, but �nding Alt-lite channels from the I.D.W. and

I.D.W. channels from the large media channels in the media group is

also feasible. Considering the sheer amount of views the channels in

the Alt-lite, the I.D.W. and the Alt-lite, these percentages, although

low, may result in a very signi�cant number of views towards

fringe content. This process may also be ampli�ed when taking

personalization into account. Notice that we depict the two graphs

in which we performed our experiments in Appendix E.

8 DISCUSSION

We performed a through analysis of three YouTube communities

— the I.D.W., the Alt-lite, and the Alt-right — inspecting a large

dataset with millions of comments and recommendations from

thousands of videos. In this section, we discuss how the insights of

our analyses shed light into our research questions. We also talk

about the limitations and potential implications of this work.

RQ1.Howhave these channels grown on YouTube in the last

decade? The three communities studied sky-rocketed in terms of

views, likes, videos published and comments, particularly, since

2015, coinciding with the presidential election of that year, as shown

in Sec. 4. However, this seems to be the case not only for these

communities, but also for the larger channels in the media group.

A key di�erence between the communities and media channels lies

in the engagement of their users. The number of comments per

view seems to be particularly high for extreme content (Sec. 4), and

users in all three communities are more assiduous commentators

than in the media channels (Sec. 5).

RQ2. To which extent do users systematically gravitate to-

wardsmore extreme content?We �nd that the commenting user

bases for the three communities are increasingly similar (Sec. 5), and,

considering Alt-right channels as a proxy for extreme content, that

a signi�cant amount of commenting users systematically migrates

from commenting exclusively on milder content to commenting on

more extreme content (Sec. 7). We argue that this �nding provides

signi�cant evidence that there has been, and there continues to be,

user radicalization on YouTube, and our analyses of the activity

of these communities (Sec. 4) is consistent with the theory that

more extreme content “piggybacked” on the surge in popularity

of I.D.W. and Alt-lite content [30]. We show that this migration

phenomenon is not only consistent throughout the years, but also

that it is signi�cant in its absolute quantity. Noticeably, the �nd-

ings related to this research question make the implicit assumption

that commenting users are a good enough proxy for radicaliza-

tion, and that comments in YouTube channels are supportive of

the videos they are associated with. We established the validity of

these assumptions as follows. First, the sheer number of comments

and high prevalence of comments per views in Alt-right videos

suggest that commenting users are a population worth studying,

especially when in Sec. 4 we found that Alt-right channels have

a very high percentage of comments per view. Secondly, during

the three week annotation period, it was noted that the number of

opposing comments is rather small, as we found by manually check-

ing 900 randomly selected comments (300 for each community of

interest), �nding that only 5 could be interpreted as criticisms to

the videos they were associated with. Moreover, we note that the

proportion of likes for the communities of interest is higher for

the communities of interest (> 91% mean, > 96% median) than for

the media channels (85% mean, 93% median), which suggests the

people interacting with the three communities agree with their

videos.

RQ3. Do algorithmic recommendations steer users towards

more extreme content? Our simulations suggest that YouTube’s

recommendation algorithms frequently suggest Alt-lite and I.D.W.

content. From these two communities, it is possible to �nd Alt-right

content from recommended channels, but not from recommended

videos. Noticeably, our analysis has several shortcomings which

do not allow us to make bold claims about this research question.

Firstly, we are able to look only at a tiny fraction of actual recom-

mendations — it could very well be that Alt-right content was being

more widely promoted in the past. Secondly, our analysis does not

take into account personalization, which could reveal a completely

di�erent picture. Still, even without personalization, we were still

able to �nd a path in which users could �nd extreme content from

large media channels.

Limitations and future work. Our work resonates with the nar-

rative that there is a radicalization pipeline [36, 41]. Indeed, we

manage to measure traces of user radicalization using commenting

users. Although we argue this is strong evidence for the existence

of radicalization pathways on YouTube, our work provides little

insight on why these radicalization pipelines exist. Elucidating the

causes of radicalization is an important direction to better under-

stand user radicalization and the in�uence of social media in our

lives. Moreover, in this paper we focused exclusively on basic sta-

tistics (likes, views and comments) and on the trajectory of users,

be they inferred through comments or simulated in the recommen-

dation graphs. Another interesting direction would be to trace the

evolution of the speech of content creators and commenting users

throughout the years, to study what are the narratives that arose

and how their tone has changed.

Acknowledgements. We gratefully acknowledge support from

the Brazilian agencies CNPq, Capes and Fapemig, from the projects

Atmosphere, INCT-Cyber and MASWEB, from a Google Research

Award for Latin America (Manoel Horta Ribeiro). We thank Jeremy

(Jimmy) Blackburn for helpful discussions.



Auditing Radicalization Pathways on YouTube

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Horta Ribeiro et al.

Figure 6: Recommendation graph of YouTube channels.

Figure 7: Recommendation graph of YouTube videos. Colors

for communities are the same as those in the paper.

A DATA COLLECTION

We give some details in the data collection process. Tab. 7 and

Tab. 8 show for channels labeled as Alt-right, Alt-lite and I.D.W.,

their communities and data collection steps. Tab 9 shows all me-

dia channels we obtained. Fig. 9 highlights what was collected on

YouTube. Below, we enumerate the keywords employed to search

for channels of each of the communities:

Figure 8: Example of YouTube channel with featured chan-

nel on the side.

For the I.D.W. Stephen Hicks, Camille Paglia, Carl Benjamin, Elon

Musk, Akira the Don, Nicholas Christakis, Claire Lehmann, Matt

Christiansen, Steven Pinker, RebelWisdom, Tim Pool, Quillette, Jonathan

Haidt, Peter Thiel, Lindsay Shepherd, James Damore

For the Alt-lite Brittany Pettibone, Jack Posobiec, Gavin McInnes,

Kyle Chapman, Kyle Prescott, Lucian Wintrich, Mike Cernovich, Milo

Yiannopoulos, Stefan Molyneaux, Vee, Blonde in the Belly of the Beast,

Paul Joseph Watson, Styxhenxenhammer666, Rebel Media, Lauren

Chen, Computing Forever, Andy Warski, Owen Benjamin, Steven

Crowder

For the Alt-right Evola, Evropa, The Jewish Question, White Geno-

cide, Mass immigration, Andrew Anglin, weev, Andy Nowicki, Au-

gustus Invictus, Christopher Cantwell, Collin Liddell, Daniel J. Kleve,

Daniel Friberg, Dillon Irizarry, Greg Johnson, Jared Taylor, Jason

Kessler, Jason Reza Jorjani, Johnny Monoxide, Lana Lokte�, Matt

Forney, Matthew Heimbach, Matthew Parrott, Mike Enoch, Nathan

Damigo, Pax Dickinson, Richard Spencer, Tara McCarthy, Vox Day,

Baked Alaska

B FEATURED VS RECOMMENDED

We illustrate the di�erence between featured and recommended

channel. In Fig. 8 you may see an example of featured channels,

these are chosen by the channel owner. In Fig. 9, letter (e) shows

related channels, these are recommendations made by YouTube.

C LIKES, VIDEOS, VIEWS, COMMENTS

Tab. 4 shows, for the three communities, the number of likes, views,

videos and commenting users across the years.

D USER TRAJECTORIES

Tab. 5 shows the absolute numbers of users tracked and infected

(at all levels, as mentioned in Sec. 6. It also shows what percentage

of the total number of users who watched Alt-right the number of

users infected was. Additionally, In Tab. 6, we show the trajectories

from the Alt-right to the other two communities and media channel

(that is, we repeat the exact same procedure tracking users from

the Alt-right and checking if they commented in the other com-

munities). We �nd that users from the Alt-right di�use in similar

propotions to the other communities and the media channels.

E RECOMMENDATION GRAPHS

In Figs. 6 and 7 we show the recommendation graphs used for the

experiment in Section 7.



Auditing Radicalization Pathways on YouTube

Table 4: For all categories, we list the number of likes, views, videos and commenting users across the years.

Category Year Like Count View Count Video Count Comment Count

Alt-lite 2008 272639 18145720 1392 129130

2009 585060 32993863 929 197934

2010 503744 30519109 1498 248432

2011 527221 28400257 2344 236961

2012 805166 61779929 4142 360688

2013 1237131 101136564 2476 531614

2014 2574586 158822748 3319 824757

2015 8227303 398745164 7299 2787437

2016 27277364 1001985084 9442 8983525

2017 55014745 1393845365 15858 12322105

2018 54723719 1092143577 20681 21341673

Alt-right 2008 559 73159 29 332

2009 8389 1236895 313 1135

2010 14124 1897892 363 2136

2011 15992 1778120 174 6076

2012 75092 4925868 434 10452

2013 160494 11219639 654 25825

2014 233381 12718956 725 47032

2015 434925 17148672 958 127319

2016 1411778 44177307 2334 529821

2017 4253888 108482909 3548 1262549

2018 5773031 106455102 5843 2725573

Media 2008 348137 128986765 2115 7932

2009 511468 196992273 3939 41492

2010 573299 203399250 6531 94379

2011 1824078 350120542 12748 200385

2012 3432239 454969357 25716 447302

2013 5238196 716009326 18135 756691

2014 9217725 1538251895 18836 814124

2015 16569182 2015671151 24168 830655

2016 27807514 2481994316 30119 1317648

2017 46467022 3102590498 35678 2931209

2018 54106314 2997876294 30951 13667470

I.D.W. 2008 54185 7034287 447 5263

2009 61340 8661426 488 11249

2010 135205 15457288 549 29932

2011 269021 22797055 884 129453

2012 365241 23918023 1067 154322

2013 1085086 109350887 2520 226833

2014 2451712 230853763 2304 367374

2015 3297678 261930446 2053 858816

2016 6616069 447000398 3291 2056423

2017 18820727 1135173733 8789 4291180

2018 25625709 1575685392 14842 11013421



Horta Ribeiro et al.

Table 5: We show absolute numbers for users infected and tracked in Sec. 6, as well as what percentage of the total number of

users who watched Alt-right the number of users infected was.

Category Start Year # Users Infected # Users Tracked % of Users Alt-right % Tracked and Infected

Alt-lite 2006-2012 2006-2012 0 170301 0% 0%
2013-2016 2132 43872 146% 4.86%
2016 3426 27045 8.83% 12.67%
2017 4558 28944 6.1% 15.75%
2018 6186 34436 4.37% 17.96%

2013-2016 2013-2016 0 414353 0% 0%
2016 12287 127591 25.8% 9.63%
2017 16345 117181 19.71% 13.95%
2018 22753 126815 15.39% 17.94%

2016 2016 0 718464 0% 0%
2017 31290 301252 32.19% 10.39%
2018 45005 290816 28.4% 15.48%

2017 2017 0 777106 0% 0%
2018 44017 352938 25.22% 12.47%

Alt-lite or I.D.W. 2006-2012 2006-2012 0 227945 0% 0%
2013-2015 3192 64874 215% 4.92%
2016 5016 39527 132% 12.69%
2017 6645 42140 8.94% 15.77%
2018 8895 49748 6.29% 17.88%

2013-2015 2013-2015 0 694155 0% 0%
2016 25848 252962 57.75% 10.22%
2017 33532 230172 41.91% 14.57%
2018 42629 239116 29.27% 17.83%

2016 2016 0 1040872 0% 0%
2017 52610 480309 579% 10.95%
2018 70905 454870 45.91% 15.59%

2017 2017 0 1251674 0% 0%
2018 74534 619501 44.1% 123%

Media 2006-2012 2006-2012 0 248214 0% 0%
2013-2015 1136 50724 7% 2.24%
2016 2331 27168 5.3% 8.58%
2017 3123 30991 3.75% 108%
2018 4629 41913 31% 114%

2013-2015 2013-2015 0 637338 0% 0%
2016 3146 81489 5.75% 3.86%
2017 5159 86127 5.42% 5.99%
2018 8123 116469 4.85% 6.97%

2016 2016 0 365614 0% 0%
2017 2929 75512 2.65% 3.88%
2018 5000 92281 2.79% 5.42%

2017 2017 0 696297 0% 0%
2018 7809 214600 3.86% 3.64%

I.D.W. 2006-2012 2006-2012 0 47914 0% 0%
2013-2015 565 14948 3.54% 3.78%
2016 889 8745 2.26% 10.17%
2017 1240 9424 1.64% 13.16%
2018 1686 11322 1.15% 14.89%

2013-2015 2013-2015 0 212122 0% 0%
2016 4634 72573 9.54% 6.39%
2017 6742 67199 7.79% 103%
2018 8543 70677 5.43% 129%

2016 2016 0 232159 0% 0%
2017 5942 98640 5.83% 62%
2018 8711 97288 5.15% 8.95%

2017 2017 0 420116 0% 0%
2018 14268 206053 7.61% 6.92%



Auditing Radicalization Pathways on YouTube

Table 6: We show absolute numbers for users infected and tracked in Sec. 6, as well as what percentage of the total number of

users who watched Alt-right the number of users infected was.

Category Start Year # Users Infected # Users Tracked % of Users Alt-right % Tracked and Infected

Alt-right to I.D.W. 2006-2012 2006-2012 2006-2012 3276 0% 0%
2013-2015 283 997 0.24% 28.39%
2016 216 569 0.13% 37.96%
2017 290 646 0.1% 44.89%
2018 391 741 07% 52.77%

2013-2015 2013-2015 2006-2012 21578 0% 0%
2016 1421 6384 0.72% 22.26%
2017 1821 5691 0.59% 32%
2018 2565 5922 0.43% 43.31%

2016 2016 2006-2012 41385 0% 0%
2017 4142 15752 1.17% 26.3%
2018 5511 15426 0.87% 35.73%

2017 2017 2006-2012 69241 0% 0%
2018 9024 29987 1.31% 309%

Alt-right to Alt-lite 2006-2012 2006-2012 2006-2012 3276 0% 0%
2013-2015 465 997 0.29% 46.64%
2016 399 569 0.12% 70.12%
2017 410 646 0.1% 63.47%
2018 407 741 06% 54.93%

2013-2015 2013-2015 2006-2012 21578 0% 0%
2016 3047 6384 0.87% 47.73%
2017 2962 5691 0.64% 525%
2018 2884 5922 0.43% 48.7%

2016 2016 2006-2012 41385 0% 0%
2017 7696 15752 1.49% 48.86%
2018 7089 15426 0.98% 45.95%

2017 2017 2006-2012 69241 0% 0%
2018 13435 29987 1.79% 44.8%

Alt-right to Media 2006-2012 2006-2012 0.0 3276.0 0.0% 0.0%
2013-2015 407.0 997.0 0.21% 40.82%
2016 152.0 569.0 0.12% 26.71%
2017 225.0 646.0 0.09% 34.83%
2018 406.0 741.0 0.05% 54.79%

2013-2015 2013-2015 0.0 21578.0 0.0% 0.0%
2016 1043.0 6384.0 0.6% 16.34%
2017 1504.0 5691.0 0.49% 26.43%
2018 2872.0 5922.0 0.29% 48.5%

2016 2016 0.0 41385.0 0.0% 0.0%
2017 4787.0 15752.0 1.41% 30.39%
2018 8639.0 15426.0 0.84% 56.0%

2017 2017 0.0 69241.0 0.0% 0.0%
2018 16269.0 29987.0 1.46% 54.25%



Horta Ribeiro et al.

Table 7: For the three communities, we list all the websites analysed in this paper (part 1). Edit (21-Oct-2021): Upon request on

the channel owner’s behalf, we have removed the channel ‘theglassblindspot’, which was incorrectly labeled, from the table.

Since the channel is small, removing it has no noticeable impact on the results presented in this paper.

Alt-right channels Step Alt-lite channels Step I.D.W. channels Step

0 AltRight.com 1 America First with Nicholas J Fuentes 1 Ben Shapiro 1

1 AmRen Podcasts 1 Andy Warski 1 Bret Weinstein 1

2 AmRenVideos 1 Blonde in the Belly of the Beast 1 Gad Saad 1

3 Ayla Stewart Wife With A Purpose 1 Brittany Pettibone 1 JRE Clips 1

4 Baked Alaska 2 1 Computing Forever 1 Jordan B Peterson Clips 1

5 Black Pigeon Speaks 1 Gavin McInnes 1 JordanPetersonVideos 1

6 Bre Faucheux 1 Laura Loomer 1 Lindsay Shepherd 1

7 CounterCurrentsTV 1 Lauren Chen 1 Matt Christiansen 1

8 Darkstream 1 Lauren Southern 1 Owen Benjamin 1

9 Faith J Goldy 1 MILO 1 Owen Benjamin Clips 1

10 James Allsup 1 Mike Cernovich 1 PowerfulJRE 1

11 Jason Kessler 1 Nick Fuentes Clips 1 Rebel Wisdom 1

12 Jean-François Gariépy 1 No Bullshit 1 Sam Harris 1

13 Johnny Monoxide 1 No Bullshit 2 1 SargonofAkkad100 1

14 MW Live 1 Paul Joseph Watson 1 The Rubin Report 1

15 Matt Forney 1 Rebel Canada 1 joerogandotnet 1

16 MillennialWoes 1 Rebel Edge 1 1791 2

17 NPI / Radix 1 Rebel Media 1 American Justice 2

18 Red Ice TV 1 Stefan Molyneux 1 Atheist Foundation of Australia Inc 2

19 Staying Woke 1 StevenCrowder 1 AynRandInstitute 2

20 The Golden One 1 Styxhexenhammer666 1 Ben Shapiro Thug Life 2

21 The Reality Calls Show 1 The Thinkery 1 Benjamin A Boyce 2

22 Traditionalist Worker Party 1 Vee 1 Brother Nathanael 2

23 Voxiversity 1 6oodfella 2 CISAus 2

24 augustussolinvictus 1 A1Cvenom 2 Clash of Ideas 2

25 iambakedalaska 1 AIU-Resurrection 2 Conversations with Bill Kristol 2

26 Alt Right 2 AltRight Truth 2 Crysta 2

27 Alt-Right Tankie 2 AustralianNeoCon1 2 Desi-Rae Thinking 2

28 American Pride 2 BlazeTV 2 Douglas Murray Archive 2

29 American Pride 2 2 Brave New World 2 Enlightainment 2

30 ArktosOnline 2 Bull Brand 2 Essential Truth 2

31 Augustus Invictus for United States Senate 2 Carpe Donktum 2 Freedom Speaks 2

32 AustralianRealist 2 Christopher Anderson 2 Glenn Beck 2

33 Be Open MInded 2 Daily Caller 2 Gravitahn 2

34 BigCatKayla Livestreams 2 DailyCallerVideo 2 Informative 2

35 Charles Zeiger 2 DailyKenn 2 Jordan Peterson Fan Channel 2

36 Corpus Mentis 2 Dinesh D’Souza 2 Liberty us 2

37 Dismantle The Matrix 2 DoctorRandomercam 2 MG 2

38 Dissident View 2 Domination Station 2 Maximilien Robespierre 2

39 Engländer 2 Harrison Hill Smith 2 MeaningofLife.tv 2

40 Jan Kerko� 2 Jacob Wohl 2 Mike Nayna 2

41 Mark Collett 2 Kelly Day 2 Motte & Bailey 2

42 Matthew North 2 Leo Stratton 2 MrAndsn 2

43 Nacionalista Blanco del SoCal 2 Liberty Machine News 2 Notes For Space Cadets 2

44 Nationalist Media Network 2 Luke Ford 2 Pangburn 2

45 No White Guilt 2 Luke Ford Livestreams 2 PhilosophyInsights 2

46 Patrick Slattery 2 Make Cringe Great Again 2 Pragmatic Entertainment 2

47 Real McGoy 2 News2Share 2 ReasonTV 2

48 Revcon Media 2 On The O�ensive 2 Savage Facts 2

49 Stand Up Europe 2 Oppressed Media 2 The Daily Truth 2



Auditing Radicalization Pathways on YouTube

Table 8: For the three communities, we list all the websites analysed in this paper (part 2).

Alt-right channels Step Alt-lite channels Step I.D.W. channels Step

50 Steve Trueblue 2 Revenge Of The Cis 2 The Free Speech Club 2

51 The Alternative Hypothesis 2 RobinHoodUKIP 2 The Heritage Foundation 2

52 The Great Dolemite 2 SJW CRINGE MACHINE 2 The New Criterion 2

53 The James Delingpole Channel 2 SJWCentral 2 The Pondering Primate 2

54 The Last Stand 2 Semiogogue 2 The Unplugged Observer 2

55 The Rational Rise 2 Social Justice Fails 2 TheArchangel911 2

56 TheArmenianNation 2 The Fallen State 2 TheAtlasSociety 2

57 This is Europa 2 Transliminal 2

58 ThuleanPerspective 2 The Hateful Gaels 2 Trigger Happy Media 2

59 Traditionalist Youth Network 2 The Iconoclast 2 VikNand 2

60 Truth Against The World 2 TheSchillingShow 2 Washington Watch 2

61 WhiteRabbitRadioTV 2 Tipping Point With Liz Wheeler on OAN 2 WisdomTalks 2

62 andy nowicki 2 Tommy Robinson 2 YAFTV 2

63 eliharman 2 Tree Of Logic 2 ZIEeICoZ 2

64 jackburton2009 2 UNITE AMERICA FIRST 2 ZeroFox Given 2

65 nightmarefuel 2 Western Man 2 battleo�deas 2

66 14 Sacred Words 3 Zach Hing 2 bloggingheads.tv 2

67 Awakened Saxon 3 grapjas60 2 bmdavll 2

68 Borzoi Boskovic 3 hOrnsticles3 2 successcouncil 2

69 Danny 1488 3 ramzpaul 2 tmcleanful 2

70 InvincibleNumanist 3 theovonk 2 wikileaksplus 2

71 LaughingMan0X 3 theturningpointusa 2 xUnlimitedMagz 2

72 Laura Towler 3 thkelly67 2 ybrook 2

73 LibertarianRealist2 3 Actual Justice Warrior 3 AgatanFoundation 3

74 Little Revolution 3 AllNationsParty 3 Bite-sized Philosophy 3

75 Marie Cachet 3 Alt Hype Streams 3 CoolHardLogic 3

76 Morrakiu 3 Aydin Paladin 3 Davie Addison 3

77 NeatoBurrito Productions 3 Beacom Of Light 3 Dose of Truth 3

78 NewEuropeANP 3 Bearing 3 DronetekPolitics 3

79 OnlineWipe 3 Count Dankula 3 Galactic Bubble Productions 3

80 Oswald Spengler 3 Danger�eld 3 HowTheWorldWorks 3

81 Prince of Zimbabwe 3 Demirep 3 ManOfAllCreation 3

82 Serp Kerp 3 Dr. Steve Turley 3 PragerUniversity 3

83 TRS Radio 3 IRmep Stream 3 Rekt Idiots 3

84 The Leftovers 3 Jericho Green 3 Sinatra_Says 3

85 The Lion 3 John Ward 3 Sorting Myself Out 3

86 The Revolutionary Conservative 3 JustInformed Talk 3 The Andrew Klavan Show 3

87 VertigoPolitix 3 Liberty Hangout 3 The Daily Wire 3

88 MR. OBVIOUS 3 The Heartland Institute 3

89 MarkDice 3 The Propertarian Institute 3

90 MichelleRempel 3 Timcast 3

91 Mister Metokur 3

92 NateTalksToYou 3

93 OneTruth4Life 3

94 ProductiehuisEU 3

95 Reverend Simon Sideways 3

96 Sanity 4 Sweden 3

97 Sargon of Akkad Live 3

98 SkidRowRadio 3

99 Slightly O�ens*ve 3

100 Tea Clips 3

101 The Amazing Lucas 3

102 The Weekly Sweat 3

103 TheBechtlo� 3

104 TheIncredibleSaltMine 3

105 Toad McKinley 3

106 TokenLibertarianGirl 3

107 Undoomed 3

108 Vincent James of The Red Elephants 3

109 ataxin 3

110 briano�ondon 3

111 jaydyer 3

112 libertydollshouse 3

113 patcondell 3



Horta Ribeiro et al.

Table 9: Media channels.

Left Center Left-Center Right-Center Right

0 cosmopolitan big think (the)atlantic forbes american enterprise institute

1 democracy now c-span business insider gulf news judicial watch

2 elite daily consumer reports cbc news learn liberty national ri�e association (nra)

3 good magazine �nancial times engadget new york post pj media

4 gq magazine harvard business review feminist frequency ntd.tv (new tang dynasty) project veritas

5 hu�ngton post (hu�post) investopedia glamour magazine russia insider ron paul liberty report

6 mashable makeuseof global citizen

7 merry jane mental �oss global news

8 new york magazine military.com hollywood reporter

9 new yorker recode la times

10 people magazine relevant magazine lifehacker

11 slate the economist new york daily news

12 uproxx the indian express rolling stone

13 upworthy today i found out san francisco globe

14 vanity fair vocativ scoopwhoop

15 vox world economic forum scroll.in

16 sky news

17 techcrunch

18 the guardian

19 the verge

20 vice news

21 washington post

22 wired magazine

23 yahoo news



Auditing Radicalization Pathways on YouTube

(d)

(a)
(b)

(c)

(e)

(f)

Figure 9: Overview of the elements we collected: (a) video captions, when available, (b) video recommendations, (c) video

description and metadata, (d) comments, (e) channel recommendations, and (f) video metadata.


	Abstract
	1 Introduction
	2 Background
	3 Data Collection
	4 The Rise of Contrarians
	5 User Intersection
	6 User Migration
	7 The Recommendation Algorithm
	8 Discussion
	References
	A Data Collection
	B Featured vs Recommended
	C Likes, Videos, Views, Comments
	D User Trajectories
	E Recommendation Graphs