Auditing radicalization pathways on YouTube
Open accessAbstract
Non-profits, as well as the media, have hypothesized the existence of a radicalization pipeline on YouTube, claiming that users systematically 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 classified into four types: Media, the Alt-lite, the Intellectual Dark Web (I.D.W.), and the Alt-right. According 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 increasingly 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 recommendation algorithm, looking at more than 2M video and channel recommendations between May/July 2019. We find that Alt-lite content 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.
AI snapshot
Written from the paper’s full text.
TL;DR
This paper audits whether YouTube users radicalize across I.D.W., Alt-lite, and Alt-right channels, using 330,925 videos from 349 channels and 72M+ comments. It finds that the three communities increasingly share commenters, that users migrate from milder to more extreme content, and that Alt-right channels are reachable through YouTube’s channel recommendations even without personalization.
Key findings
- The commenting user bases of the I.D.W., Alt-lite, and Alt-right increasingly overlap: in 2018, the Jaccard similarity between Alt-lite and I.D.W. reached almost 30%, and the Overlap Coefficient of Alt-right with Alt-lite and I.D.W. reached around 50%, meaning about half of users who commented on Alt-right channels also commented in the other communities.
- Users systematically migrate toward more extreme content: among users who in 2006–2012 commented only on I.D.W. or Alt-lite content (227,945 users), by 2018 around 10% were lightly exposed to Alt-right content and roughly 4% were mildly or severely exposed, about 9K users total; among those who in 2017 commented only on Alt-lite or I.D.W. videos (1,251,674 users), approximately 12% were exposed, more than 60K users.
- In 2018, roughly 40% of Alt-right commenting users could be traced back to cohorts that had commented only on Alt-lite or I.D.W. videos in the past; media-channel users never surpassed 6% for any exposure level. For light exposure in 2018, 23.3% traced back to prior Alt-lite-only commenters versus 7.6% to prior I.D.W.-only commenters.
- Recommendation simulations show Alt-lite content is easily reachable from I.D.W. channels, while Alt-right videos are reachable only through channel recommendations: channel reachability@5 for Alt-right was about 4% from Alt-lite and 1.5% from I.D.W.; starting from I.D.W., there was about a 10% chance of being in an Alt-lite channel at the next step and 25% chance of having found at least one Alt-lite channel in 5 steps. Video recommendations made Alt-right much less reachable, with reachability@5 around 0.05% from Alt-lite.
Methods
- Large-scale audit of YouTube channels classified into Media, Alt-lite, I.D.W., and Alt-right. Data collection used seed channels, keywords, and iterative featured/related-channel expansion; two authors manually annotated channels over 3 weeks with interannotator agreement of 75.57% (95% CI [67.5, 82.5]), yielding 85 I.D.W., 112 Alt-lite, 84 Alt-right, and 68 media channels.
- Dataset: 349 channels, 330,925 videos, 72,069,878 comments, 5,980,709 commenting users, 2,474,044 video recommendations, and 14,283 channel recommendations. Data collection occurred 19–30 May 2019, with recommendations collected May–July 2019 using crawlers, 22 channel-recommendation rounds, 19 video-recommendation rounds, VPNs from 7 locations, random channel visit order, and no logged-in personalization.
- Analyses included Jaccard and Overlap Coefficient set similarities across commenting-user sets, cohort tracking of users who commented only on milder communities and later commented on Alt-right videos, and random-walk simulations on weighted recommendation graphs (10K runs, 5 steps) to measure probability and reachability.
Limitations
- The paper states that it cannot obtain the entire history of YouTube recommendations, so algorithmic analyses are limited to a time slice of a constantly changing black box; it also cannot fully tease apart the algorithm’s influence in earlier periods, and the recommendation analysis does not account for personalization.
- The paper states that it provides little insight into why radicalization pipelines exist, and that its focus is on basic statistics and user trajectories rather than tracing the evolution of speech or narratives over time.
- The paper states that its radicalization findings rely on the assumptions that commenting users are a good enough proxy for radicalization and that comments are supportive of the videos they accompany; it supports these assumptions with a manual check of 900 randomly selected comments, finding only 5 that could be interpreted as criticisms, and with higher like proportions for the communities of interest (>91% mean, >96% median) than for media channels (85% mean, 93% median). Evident from the text: channel labeling is fuzzy and conservative, and the video-recommendation simulation is less realistic because it ignores untracked videos.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
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- Findingown data§ 1 INTRODUCTION · p. 2
The migration effect is much stronger than for the large traditional and alternative media channels collected.
“This effect is much stronger than for the large traditional and alternative media channels we collected (Sec. 6).”
Quote in the paperSubstantial claim - Findingown data§ 1 INTRODUCTION · p. 2
Alt-right channels may be reached from the I.D.W. and Alt-lite communities.
“and that Alt-right channels may be reached from the two other communities (Sec. 7).”
Quote in the paperSubstantial claim - Findingreasoning§ 5 USER INTERSECTION · p. 6
In 2018, there is a sharp growth in similarity with media channels.
“However, in 2018, there is a sharp growth in the similarity with media channels.”
Quote in the paperNot a substantial claim - Findingown data§ 5 USER INTERSECTION · p. 6
The communities of interest have many more highly active commenters.
“as we can see that the communities of interest have many more highly active commenters.”
Quote in the paperSubstantial claim - Findingown data§ 1 INTRODUCTION · p. 2
Comments per view are particularly high in more extreme content, reaching near 1 comment for every 5 views in Alt-right channels in 2018.
“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).”
- Also stated in: § 4 THE RISE OF CONTRARIANS · p. 5 · plain
“Notably, Alt-right channels have, since 2017, become the ones with the highest number of comments per view, with nearly 1 comment per 5 views by 2018.”
Quote in the paperNot a substantial claim - Also stated in: § 4 THE RISE OF CONTRARIANS · p. 5 · plain
- Findingown data§ ABSTRACT · p. 1
Alt-lite content is easily reachable from I.D.W. channels.
“We find that Alt-lite content is easily reachable from I.D.W. channels”
- Also stated in: § 1 INTRODUCTION · p. 2 · plain
“Our analyses show that, particularly through the channel recommender system, Alt-lite channels are easily discovered from I.D.W. channels”
Quote in the paperSubstantial claim - Also stated in: § 1 INTRODUCTION · p. 2 · plain
- Findingown data§ ABSTRACT · p. 1
The three channel types (I.D.W., Alt-lite, Alt-right) increasingly share the same user base.
“Processing 72M+ comments, we show that the three channel types indeed increasingly share the same user base”
- Also stated in: § 1 INTRODUCTION · p. 2 · plain
“We inspect the intersection of commenting users across the communities, finding they increasingly share the same user base.”
- Also stated in: § 5 USER INTERSECTION · p. 6 · plain
“We again see that their users are more engaged, and, notably, find that the I.D.W, the Alt-lite, and the Alt-right increasingly share the same commenting user base.”
Quote in the paperSubstantial claim - Also stated in: § 1 INTRODUCTION · p. 2 · plain
- Findingown data§ ABSTRACT · p. 1
Users consistently migrate from milder to more extreme content.
“that users consistently migrate from milder to more extreme content”
- Also stated in: § 1 INTRODUCTION · p. 2 · plain
“We also find that the intersection is not only growing due to new users but that there is significant user migration among the communities being studied.”
Quote in the paperSubstantial claim - Also stated in: § 1 INTRODUCTION · p. 2 · plain
- Findingown data§ 1 INTRODUCTION · p. 2
These migrating users are a significant fraction of the Alt-right commenting user base.
“These users are a significant fraction of the Alt-right commenting user base.”
Quote in the paperSubstantial claim - Findingown data§ ABSTRACT · p. 1
Alt-right videos are reachable only through channel recommendations.
“while Alt-right videos are reachable only through channel recommendations.”
Quote in the paperSubstantial claim - Findingown data§ 1 INTRODUCTION · p. 2
Users that initially comment only on I.D.W. or Alt-lite content consistently start to comment on Alt-right content.
“Users that initially comment only on content from the I.D.W. or the Alt-lite throughout the years consistently start to comment on Altright content.”
Quote in the paperSubstantial claim - Findingown data§ ABSTRACT · p. 1
A large percentage of users who consume Alt-right content now consumed Alt-lite and I.D.W. content in the past.
“and that a large percentage of users who consume Alt-right content now consumed Alt-lite and I.D.W. content in the past.”
Quote in the paperSubstantial claim - Findingown data§ 4 THE RISE OF CONTRARIANS · p. 5
The communities of interest have more engagement (likes per video, comments per video, comments per view) than media channels.
“For all these metrics, the communities of interest have more engagement than the media channels”
- Also stated in: § 4 THE RISE OF CONTRARIANS · p. 5 · plain
“Although media channels have more views per video, as shown in Figs. 1(g), these views are less often converted into likes and comments.”
Quote in the paperNot a substantial claim - Also stated in: § 4 THE RISE OF CONTRARIANS · p. 5 · plain
- Findingown data§ 4 THE RISE OF CONTRARIANS · p. 5
4 out of the 5 current top Alt-right channels (accumulating approximately 150M views) had already been created by 2013.
“4 out of the 5 current top Alt-right channels (accumulating approximately 150M views) had already been created by 2013.”
Quote in the paperSubstantial claim - Findingown data§ 5 USER INTERSECTION · p. 6
The retention of users among the three communities is growing with time.
“We find that the retention of users among the three communities is growing with time for both metrics.”
Quote in the paperNot a substantial claim - Findingown data§ 5 USER INTERSECTION · p. 6
Commenting users from communities of interest seem to go back more often than those in media channels.
“Commenting users from the communities of interest seem to go back more often than those in media channels.”
Quote in the paperNot a substantial claim - Findingnone given§ 4 THE RISE OF CONTRARIANS · p. 5
Many channels now dedicated to Alt-right content have initial videos related to other subjects.
“Moreover, it is noteworthy that many of the channels now dedicated to Alt-right content have initial videos related to other subjects.”
Quote in the paperNot a substantial claim - Findingown data§ 1 INTRODUCTION · p. 2
The growing intersection between communities is not only due to new users.
“We also find that the intersection is not only growing due to new users but that there is significant user migration among the communities being studied.”
Quote in the paperSubstantial claim - Findingown data§ 5 USER INTERSECTION · p. 6
The Alt-right community is much younger than Alt-lite or I.D.W.
“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”
Quote in the paperNot a substantial claim - Findingown data§ 1 INTRODUCTION · p. 2
Approximately half of users who commented on Alt-right channels in 2018 also commented on Alt-lite and I.D.W. channels.
“Analyzing the overlap between the sets of commenting users, we find 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).”
- Also stated in: § 5 USER INTERSECTION · p. 6 · plain
“This means around half of the users who commented in Alt-right channels commented in the other communities.”
Quote in the paperSubstantial claim - Also stated in: § 5 USER INTERSECTION · p. 6 · plain
- Findingown data§ 5 USER INTERSECTION · p. 6
The communities overlap more with themselves than with media channels, particularly since 2015.
“there we have that the communities overlap more with themselves than with the media channels, particularly since 2015.”
Quote in the paperSubstantial claim - Findingown data§ 5 USER INTERSECTION · p. 6
The Jaccard similarity between I.D.W./Alt-lite and media channels is not so different from similarity between these communities and Alt-right.
“We have that the Jaccard similarity between the I.D.W. and the Alt-lite and the media channels is not so different from the similarity between these communities and the Alt-right.”
Quote in the paperSubstantial claim - Findingown data§ 5 USER INTERSECTION · p. 6
The Overlap Coefficient of Alt-right with Alt-lite and I.D.W. reached around 50% in 2018.
“Moreover, the Overlap Coefficient of the Alt-right with the Alt-lite and the I.D.W is high: reaching around 50% in 2018.”
Quote in the paperSubstantial claim - Findingown data§ 8 DISCUSSION · p. 10
The number of comments per view seems to be particularly high for extreme content.
“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).”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 7
For users who in 2006-2012 commented only on I.D.W. or Alt-lite content, by 2018 roughly 4% were severely or mildly exposed.
“By 2018, around 10% were lightly exposed, and roughly 4% severely or mildly so - which amounts to approximately 9K users in total.”
Quote in the paperSubstantial claim - Findingnone given§ 8 DISCUSSION · p. 10
The growth in views, likes, videos and comments since 2015 also seems to be the case for the larger channels in the media group.
“However, this seems to be the case not only for these communities, but also for the larger channels in the media group.”
Quote in the paperNot a substantial claim - Findingown data§ 8 DISCUSSION · p. 10
A key difference between the communities and media channels lies in the engagement of their users.
“A key difference between the communities and media channels lies in the engagement of their users.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 7
Media channels present lower exposure rates than Alt-lite or I.D.W. channels.
“Wealso find that media channels present lower exposure rates, as can be seen in the last row of the figure.”
Quote in the paperSubstantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
Alt-right videos are not significantly recommended in video recommendations.
“Lastly, Alt-right videos are not significantly recommended here.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 8
The intersection between the Alt-lite and the I.D.W. is increasing with time.
“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 significant.”
Quote in the paperSubstantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
Alt-right channels are only recommended by Alt-lite channels (3.08%).
“Alt-right channels are only recommended by Alt-lite channels (3 . 08%).”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
In 5 steps, there is 25% chance that the user has found at least one Alt-lite channel.
“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.”
Quote in the paperSubstantial claim - Findingown data§ 8 DISCUSSION · p. 10
Users in all three communities are more assiduous commentators than in the media channels.
“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).”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10
From the Alt-lite, following the recommender system 5 times, approximately 1 out of each 25 times we will have spotted an Alt-right channel.
“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)).”
Quote in the paperSubstantial claim - Findingown data§ 8 DISCUSSION · p. 10
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.
“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.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 7
The pattern of lower and increasingly lower exposure applies to media channels.
“The same applies to the media channels.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 8
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.
“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.”
Quote in the paperSubstantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
Alt-lite and I.D.W. recommend each other roughly 2% of the time in video recommendations.
“We also find that media channels are more often recommended in this setting ( ∼ 3% for all communities), while the Alt-lite and the I.D.W. recommend each other roughly 2% of the time.”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10
From the Alt-lite, reachability@5 of Alt-right is around 0.05%.
“From the Alt-lite, reachability@5 is of around 0 . 05%.”
Quote in the paperSubstantial claim - Findingown data§ 6 USER MIGRATION · p. 8
Systematically, users who commented only on I.D.W. or Alt-lite content go on to comment on Alt-right channels.
“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.”
Quote in the paperSubstantial claim - Findingown data§ 6 USER MIGRATION · p. 7
For users tracked in 2006-2012, around 15% were exposed in both Alt-lite-only and I.D.W.-only scenarios.
“For users who were tracked in 2006 -2012, around 15% were exposed in both scenarios, while for those tracked in 2017, this difference grew farther apart ( ∼ 12% Alt-lite vs. ∼ 6% I.D.W.).”
Quote in the paperSubstantial claim - Findingown data§ 6 USER MIGRATION · p. 7
For users who in 2017 commented only on Alt-lite or I.D.W. videos, more than 60K users were exposed.
“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 first row, approximately 12% of them were exposed - more than 60K users altogether.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 7
For users who in 2017 commented only on Alt-lite or I.D.W. videos, approximately 12% were exposed.
“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 first row, approximately 12% of them were exposed - more than 60K users altogether.”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
For channel recommendations, media channels are recommended scarcely by the communities of interest.
“For channel recommendations, we have that media channels are recommended scarcely by the communities of interest.”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
Starting from an I.D.W. channel, users have approximately 10% chance of being in an Alt-lite channel at the next step.
“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.”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10
For video recommendations, reaching Alt-right channels from other communities is less likely.
“For video recommendations, reaching Alt-right channels from other communities is less likely.”
Quote in the paperSubstantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10
Going from the I.D.W. to the Alt-lite, reachability@5 is roughly 7%.
“Going from the I.D.W. to the Alt-lite is more difficult: the reachability@5 is roughly 7%.”
Quote in the paperSubstantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
Media channels are more often recommended in the video setting (∼3% for all communities).
“We also find that media channels are more often recommended in this setting ( ∼ 3% for all communities), while the Alt-lite and the I.D.W. recommend each other roughly 2% of the time.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 7
For users who in 2006-2012 commented only on I.D.W. or Alt-lite content, approximately 9K users in total were exposed by 2018.
“By 2018, around 10% were lightly exposed, and roughly 4% severely or mildly so - which amounts to approximately 9K users in total.”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
There are more edges flowing out of media channels towards Alt-lite/I.D.W. channels than the other way around.
“In fact, there are more edges flowing out of media channels towards Altlite/I.D.W. channels than the other way around.”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
For video recommendations, more than 75% of outgoing edges from all communities pointed towards the 'Other' node.
“For video recommendations, 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).”
Quote in the paperNot a substantial claim - Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9
Alt-lite and I.D.W. channels recommend channels from the same community around 50% of the time.
“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.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 7
For users who in 2006-2012 commented only on I.D.W. or Alt-lite content, by 2018 around 10% were lightly exposed.
“By 2018, around 10% were lightly exposed, and roughly 4% severely or mildly so - which amounts to approximately 9K users in total.”
Quote in the paperNot a substantial claim - Findingown data§ 6 USER MIGRATION · p. 8
Consistently, users who consumed Alt-lite or I.D.W. content in a given year go on to become a significant fraction of the Alt-right user base in the following year.
“Moreover, we can observe that, consistently, users who consumed Alt-lite or I.D.W. content in a given year, go on to become a significant fraction of the Alt-right user base in the following year.”
Quote in the paperSubstantial claim - Findingown data§ 8 DISCUSSION · p. 10
YouTube's recommendation algorithms frequently suggest Alt-lite and I.D.W. content.
“Our simulations suggest that YouTube's recommendation algorithms frequently suggest Alt-lite and I.D.W. content.”
Quote in the paperSubstantial claim - Findingown data§ 8 DISCUSSION · p. 10
During the three week annotation period, only 5 out of 900 randomly selected comments (300 for each community of interest) could be interpreted as criticisms to the videos they were associated with.
“Secondly, during the three week annotation period, it was noted that the number of opposing comments is rather small, as we found by manually checking 900 randomly selected comments (300 for each community of interest), finding that only 5 could be interpreted as criticisms to the videos they were associated with.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Findingown data§ 8 DISCUSSION · p. 10
Alt-right channels have a very high percentage of comments per view.
“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.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Findingnone given§ 8 DISCUSSION · p. 10
We manage to measure traces of user radicalization using commenting users.
“Indeed, we manage to measure traces of user radicalization using commenting users.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Findingown data§ 8 DISCUSSION · p. 10
Even without personalization, we were able to find a path in which users could find extreme content from large media channels.
“Still, even without personalization, we were still able to find a path in which users could find extreme content from large media channels.”
Quote in the paperSubstantial claim - Findingown data§ D USER TRAJECTORIES · p. 12
Users from the Alt-right diffuse in similar proportions to the other communities and the media channels.
“We find that users from the Alt-right diffuse in similar propotions to the other communities and the media channels.”
Quote in the paperNot a substantial claim - Study design and conductown data§ 1 INTRODUCTION · p. 2
Commenting users are a good enough proxy to measure user radicalization.
“We argue that commenting users are a good enough proxy to measure the user radicalization, as more extreme content seems to beget more comments.”
Quote in the paperSubstantial claim - Interpretationown data§ 5 USER INTERSECTION · p. 6
The Alt-right, despite fewer views, subscribers and comments, is on par with large media channels in terms of user overlap.
“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.”
Quote in the paperSubstantial claim - Study design and conductreasoning§ 2 BACKGROUND · p. 3
The algorithmic analyses are limited to a time slice of a constantly changing black-box.
“Unfortunately, it is not possible to obtain the entire history of YouTube recommendation, so we must limit the algorithmic analyses to a time slice of a constantly changing black-box.”
Quote in the paperSubstantial claim - Interpretationcitation§ 4 THE RISE OF CONTRARIANS · p. 5
The rise of Alt-Lite and I.D.W. channels created fertile grounds for individuals with fringe ideas to prosper.
“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].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [24] [24] Rebecca Lewis. 2018. Alternative influence: Broadcasting the reactionary right on YouTube . Technical Report. Data and Society. -
Not checked in the free run.
- cites [30] [30] Angela Nagle. 2017. Kill All Normies: Online Culture Wars From 4Chan And Tumblr To Trump And The Alt-Right . -
Not checked in the free run.
- Interpretationown data§ 1 INTRODUCTION · p. 2
YouTube's recommender system enables Alt-right channels to be discovered, even in a scenario without personalization.
“and that YouTube's recommender system enables Alt-right channels to be discovered, even in a scenario without personalization.”
Quote in the paperNot a substantial claim - Study design and conductreasoning§ 1 INTRODUCTION · p. 2
The recommendations collected do not account for personalization.
“Importantly, our recommendations do not account for personalization.”
- Also stated in: § 3 DATA COLLECTION · p. 4 · plain
“As we extensively discuss throughout the paper, this does not include personalization, as we do not log in into any account.”
- Also stated in: § 2 BACKGROUND · p. 3 · plain
“Another limitation of our auditing is that we do not account for user personalization.”
Quote in the paperSubstantial claim - Also stated in: § 3 DATA COLLECTION · p. 4 · plain
- Premisecitation§ 1 INTRODUCTION · p. 1
The lack of agreement over what is 'hateful' or 'extreme' is a key issue.
“A key issue in dealing with topics like radicalization and hate speech is the lack of agreement over what is 'hateful' or 'extreme' [38].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [38] [38] Andrew Sellars. 2016. Defining Hate Speech . SSRN Scholarly Paper. Social Science Research Network. https://papers.ssrn.com/abstract=2882244 -
Not checked in the free run.
- Interpretationown data§ 1 INTRODUCTION · p. 2
There is strong evidence for radicalization among YouTube users.
“We find strong evidence for radicalization among YouTube users”
Quote in the paperSubstantial claim - Interpretationcitation§ 4 THE RISE OF CONTRARIANS · p. 5
The communities of interest gained traction in and fortified Donald Trump's campaign during the 2016 presidential elections.
“Altogether, our data corroborates with the narrative that these communities gained traction in (and fortified) Donald Trump's campaign during the 2016 presidential elections [10, 17].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [10] [10] Tyler Bridges. 2018. "Alt-Lite" Bloggers and the Conservative Ecosystem. https: //shorensteincenter.org/alt-lite-bloggers-conservative-ecosystem/ -
Not checked in the free run.
- cites [17] [17] Rosie Gray. 2015. How 2015 Fueled The Rise Of The Freewheeling, White Nationalist Alt- Movement. https://www.buzzfeednews.com/article/rosiegray/how2015-fueled-the-rise-of-the-freewheeling-white-nationali -
Not checked in the free run.
- Premisecitation§ 2 BACKGROUND · p. 2
The line between Alt-right and Alt-lite is blurry.
“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”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] [3] [ADL. 2019. From Alt Right to Alt Lite: Naming the Hate. https://web.archive. org/web/20190422202936/https://www.adl.org/resources/backgrounders/fromalt-right-to-alt-lite-naming-the-hate](https://web.archive.org/web/20190422202936/https://www.adl.org/resources/backgrounders/from-alt-right-to-alt-lite-naming-the-hate) -
Not checked in the free run.
- Interpretationreasoning§ 5 USER INTERSECTION · p. 6
The communities do not exist in a vacuum but are part of the existing online information environment.
“These communities do not exist in a vacuum but are part of the existing online information environment.”
Quote in the paperNot a substantial claim - Interpretationreasoning§ 5 USER INTERSECTION · p. 6
Individuals in these communities make up a significant portion of the massive media channels collected.
“it means that individuals in these communities make up a significant portion of the massive media channels we collected, which gather billions of views.”
Quote in the paperSubstantial claim - Study design and conductcitation§ 2 BACKGROUND · p. 3
Increased consumption of Alt-right content is a reasonable proxy for radicalization.
“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 conflict ( e.g. a white ethnostate [18]).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [27] [27] Alex Mann, Kevin Nguyen, and Katherine Gregory. 2019. 'Emperor Cottrell': Accused Christchurch shooter had celebrated rise of the Australian far-right. -
Not checked in the free run.
- cites [18] [18] Keegan Hankes and Alex Amend. 2018. The Alt-Right is Killing People. https: //www.splcenter.org/20180205/alt-right-killing-people -
Not checked in the free run.
- Study design and conductown data§ 3 DATA COLLECTION · p. 4
The annotation process had interannotator agreement of 75.57% (95% CI [67.5, 82.5]).
“Interanotator agreement was of 75 . 57%(95% CI [ 67 . 5 , 82 . 5 ] ).”
Quote in the paperNot a substantial claim - Interpretationown data§ 6 USER MIGRATION · p. 8
Users who consumed only I.D.W. channels seem to behave more similarly to users in media channels than to Alt-lite users.
“Overall, in both analyses, users who consumed only I.D.W. channel seem to behave more similarly to the users in the media channels.”
Quote in the paperSubstantial claim - Interpretationcitation§ 8 DISCUSSION · p. 10
The analyses of the activity of these communities are consistent with the theory that more extreme content 'piggybacked' on the surge in popularity of I.D.W. and Alt-lite content.
“We argue that this finding provides significant 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].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [30] [30] Angela Nagle. 2017. Kill All Normies: Online Culture Wars From 4Chan And Tumblr To Trump And The Alt-Right . -
Not checked in the free run.
- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Interpretationreasoning§ 7 THE RECOMMENDATION ALGORITHM · p. 10
These percentages, although low, may result in a very significant number of views towards fringe content.
“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 significant number of views towards fringe content.”
Quote in the paperNot a substantial claim - Premisecitation§ 2 BACKGROUND · p. 2
There are blurry lines between I.D.W. and Alt-lite, especially for non-core members.
“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].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] [7] Anonymous. [n. d.]. The Intellectual Dark Web. https://web.archive.org/web/ 20190407170300/http://intellectualdark.website/ -
Not checked in the free run.
- Interpretationown data§ 8 DISCUSSION · p. 10
Our work resonates with the narrative that there is a radicalization pipeline.
“Our work resonates with the narrative that there is a radicalization pipeline [36, 41].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [36] [36] [Kevin Roose. 2019. The Making of a YouTube Radical. The New York Times (2019). https://www.nytimes.com/interactive/2019/06/08/technology/youtube-radical. html,https://www.nytimes.com/interactive/2019/06/08/technology/youtuberadical.html](https://www.nytimes.com/interactive/2019/06/08/technology/youtube-radical.html,%20https://www.nytimes.com/interactive/2019/06/08/technology/youtube-radical.html) -
Not checked in the free run.
- cites [41] [41] Zeynep Tufekci. 2018. YouTube, the Great Radicalizer. The New York Times (2018). https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politicsradical.html -
Not checked in the free run.
- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Interpretationown data§ 8 DISCUSSION · p. 10
People interacting with the three communities agree with their videos.
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Interpretationown data§ 8 DISCUSSION · p. 10
Commenting users are a population worth studying.
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
- Study design and conductreasoning§ 7 THE RECOMMENDATION ALGORITHM · p. 10
The video recommendation experiment is less realistic than the channel experiment because it ignores the possibility of the random walker being in an untracked video.
“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.”
Quote in the paperNot a substantial claim
- May combine two claims. 27
- The quote only partly states this claim. 2
- Classified as probably not a substantial claim. 15
May combine two claims.
Findingnone given§ 4 THE RISE OF CONTRARIANS · p. 5The Golden One channel's initial videos are about working out or video-games, with politics-related videos becoming increasingly occurring.
“Most of the initial videos in the channel are about working out or video-games, with politics related videos becoming increasingly occurring.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ 5 USER INTERSECTION · p. 6For media channels, Jaccard similarity is plateauing since 2014 and overlap coefficient only recently started to grow.
“However, for media channels, we find that the Jaccard similarity is plateauing since 2014 and that the overlap coefficient only recently started to grow”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ 4 THE RISE OF CONTRARIANS · p. 5By 2013, media and Alt-lite channels had published nearly 40% of their content, while the Alt-right had published a bit more than 20%.
“while media and Alt-lite channels had published nearly 40% of their content, the Alt-right had published a bit more than 20%.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 5 USER INTERSECTION · p. 6In 2018, the Jaccard Similarity between Alt-lite and I.D.W. reached almost 30%, more than the self-similarity between the two 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.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9Alt-lite and I.D.W. channels recommend each other around 14% (Alt-L to I.D.W.) and 23% (I.D.W. to Alt-L) of the time.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 8 DISCUSSION · p. 10The migration phenomenon is not only consistent throughout the years, but also significant in its absolute quantity.
“We show that this migration phenomenon is not only consistent throughout the years, but also that it is significant in its absolute quantity.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
May combine two claims.
Findingown data§ 6 USER MIGRATION · p. 7Less than 1% of users in media channels were mildly or severely exposed, against 3% to 4% for Alt-lite or I.D.W. users.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 6 USER MIGRATION · p. 8In 2018, 23.3% of users who were lightly exposed can be traced back to users who commented on Alt-lite channels in previous years, while only 7.6% can be traced back to I.D.W. channels.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10Starting from media channels, reachability@5 of I.D.W. and Alt-lite channels is around 4.5% and 1.5% respectively.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 6 USER MIGRATION · p. 8The fraction of Alt-right commenting users traceable to Alt-lite/I.D.W. is much larger than that from media channels, which never surpasses 6% for any level of exposure.
“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.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown data§ 6 USER MIGRATION · p. 8Users who initially commented only on I.D.W. channels constitute a much less significant percentage of the Alt-right consumer base in upcoming years than those who commented only on Alt-lite.
“here we see that users who initially commented only on I.D.W. channels constitute a much less significant percentage of the Alt-right consumer base in upcoming years.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 6 USER MIGRATION · p. 7Roughly 4% of media channel users were lightly exposed, versus approximately 8% for Alt-lite or I.D.W. users.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 9For channel recommendations, reachability@5 of Alt-right channels is approximately 4% for simulations starting from Alt-lite and 1.5% for I.D.W. channels.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 6 USER MIGRATION · p. 7For users tracked in 2017, exposure was approximately 12% for Alt-lite-only and approximately 6% for I.D.W.-only.
“For users who were tracked in 2006 -2012, around 15% were exposed in both scenarios, while for those tracked in 2017, this difference grew farther apart ( ∼ 12% Alt-lite vs. ∼ 6% I.D.W.).”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown data§ 6 USER MIGRATION · p. 8For 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 than those traced back to Alt-lite channels.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 6 USER MIGRATION · p. 7Users who commented only on I.D.W. content get less exposed than those who commented only on Alt-lite, and increasingly less so.
“When teasing apart users that commented only on Alt-lite or only on I.D.W. content, we find that, not only users who commented only on I.D.W. get less exposed, but increasingly less so.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10Starting from media channels, reachability@5 of I.D.W. channels is 2.5%, and of Alt-lite channels is slightly less than 1%.
“Starting from the media channels, reachability@5 of I.D.W. channels is of 2 . 5%, and of slightly less than 1% for Alt-lite channels.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ 6 USER MIGRATION · p. 8The migration phenomenon is significant both in terms of the percentage of users tracked and in terms of the total Alt-right commenting user base.
“This phenomenon is significant both in terms of the percentage of the users tracked - as in Fig. 3 - and in terms of the total Alt-right commenting user base - as in Fig. 4.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ 8 DISCUSSION · p. 10The proportion of likes for the communities of interest is higher (>91% mean, >96% median) than for the media channels (85% mean, 93% median).
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
May combine two claims.
Findingown data§ 8 DISCUSSION · p. 10From these two communities, it is possible to find Alt-right content from recommended channels, but not from recommended videos.
“From these two communities, it is possible to find Alt-right content from recommended channels, but not from recommended videos.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoning§ 2 BACKGROUND · p. 2The paper takes a conservative approach to labeling, naming only the most extreme content creators as Alt-right.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone given§ 3 DATA COLLECTION · p. 3Seeds were independently annotated two times and discarded in case of disagreement.
“Each seed was independently annotated two times and discarded in case there was any disagreement.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown data§ 3 DATA COLLECTION · p. 4The annotation process lasted 3 weeks and disagreements were discussed until conclusion.
“The annotation process lasted for 3 weeks. In case they disagreed, they had to discuss the cases individually until a conclusion was reached.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoning§ 2 BACKGROUND · p. 3It is challenging to tease apart the influence of the algorithm in previous times.
“Although comments may give us insight into the past, it is challenging to tease apart the influence of the algorithm in previous times.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown data§ 6 USER MIGRATION · p. 7The study does not have users' entire activity history, so it tracks activity only in the collected channels.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoning§ 5 USER INTERSECTION · p. 6It is hard to tell whether Alt-right users are less engaged.
“and thus it is hard to tell whether their users are less engaged.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoning§ 3 DATA COLLECTION · p. 4Channels were always visited in random order to prevent session-based recommendation biases.
“Moreover, channels were always visited in random order, to prevent any biases from arising from session-based recommendations.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown data§ 3 DATA COLLECTION · p. 4VPNs from 7 different locations were used to circumvent possible location bias.
“To circumvent possible location bias in the data we collected we used VPNs from 7 different locations: 3 in the USA, 2 in Canada, 1 in Switzerland and 1 in Brazil.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10In the video recommender system, Alt-right channels are less recommended, but finding Alt-lite channels from the I.D.W. and I.D.W. channels from the large media channels is feasible.
“In the video recommender system, Alt-right channels are less recommended, but finding 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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ 7 THE RECOMMENDATION ALGORITHM · p. 10This process may also be amplified when taking personalization into account.
“This process may also be amplified when taking personalization into account.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown data§ 7 THE RECOMMENDATION ALGORITHM · p. 10In 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 find Alt-right channels.
“Overall, we find 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 find Alt-right channels.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown data§ 8 DISCUSSION · p. 10This finding provides significant evidence that there has been, and there continues to be, user radicalization on YouTube.
“We argue that this finding provides significant 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].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [30] [30] Angela Nagle. 2017. Kill All Normies: Online Culture Wars From 4Chan And Tumblr To Trump And The Alt-Right . -
Not checked in the free run.
- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
Classified as probably not a substantial claim.
Premisenone given§ 6 USER MIGRATION · p. 7The previous study does not give insight into how expressive the pipeline effect is in terms of what part of the Alt-right user base has gone through it.
“However, it does not give insight into how expressive the effect is in terms of what part of the Alt-right user base has gone through it.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown data§ 7 THE RECOMMENDATION ALGORITHM · p. 8The recommender system data is only a snapshot that does not take into account personalization, making it hard to reach significant conclusions about the recommender system's role in radicalization.
“Unfortunately, we have only a snapshot of the recommender system which does not take into account personalization. Thus, it is hard to reach significant conclusions on what was the role of the recommender system in the radicalization process we depicted in Sec. 6.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisecitation§ 6 USER MIGRATION · p. 7The previous study suggests that the pipeline effect exists and that users systematically go from milder communities to the Alt-right.
“The previous study suggests that the pipeline effect does exist, and that indeed, users systematically go from milder communities to the Alt-right.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone given§ 1 INTRODUCTION · p. 2The paper is the first large-scale quantitative audit of user radicalization on YouTube.
“This is, to our best knowledge, the first large scale quantitative audit of user radicalization on YouTube.”
Quote in the paperNot a substantial claim- Rests on an assumption: There is to date no substantial quantitative evidence of the alleged radicalization pipeline on YouTube.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoning§ 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. · p. 16Removing the channel 'theglassblindspot' from the analysis has no noticeable impact on the results presented in the paper.
“Since the channel is small, removing it has no noticeable impact on the results presented in this paper.”
Quote in the paperNot a substantial claim- Rests on an assumption: The removed channel 'theglassblindspot' is small.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoning§ 8 DISCUSSION · p. 10Our work provides little insight on why these radicalization pipelines exist.
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Commenting users are a good enough proxy for radicalization.
- The authors state this as an assumption: Comments in YouTube channels are supportive of the videos they are associated with.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Study design and conductnone given§ 7 THE RECOMMENDATION ALGORITHM · p. 9The channel recommendation scenario is more realistic than the video scenario, so more weight is given to conclusions drawn there.
“Notice that the scenario for the channels is more realistic, and we give more weight to the conclusions drawn there.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown data§ 8 DISCUSSION · p. 10The recommendation analysis has shortcomings: it looks only at a tiny fraction of actual recommendations, and does not take into account personalization.
“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 recommendations - 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 different”
Quote in the paperSubstantial claim
The paper relies on this without support.
There is to date no substantial quantitative evidence of the alleged radicalization pipeline on YouTube.
“Yet, there is to date no substantial quantitative evidence of this alleged pipeline.”
The authors state this as an assumption.
Commenting users are a good enough proxy for radicalization.
“Noticeably, the findings related to this research question make the implicit assumption that commenting users are a good enough proxy for radicalization, and that comments in YouTube channels are supportive of the videos they are associated with.”
The authors state this as an assumption.
Comments in YouTube channels are supportive of the videos they are associated with.
“Noticeably, the findings related to this research question make the implicit assumption that commenting users are a good enough proxy for radicalization, and that comments in YouTube channels are supportive of the videos they are associated with.”
The paper relies on this without support.
The removed channel 'theglassblindspot' is small.
“Since the channel is small”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Such path dependencies may have transformative effects, changing the preferences and values of the users themselves and leading to radicalization.
“Such path dependencies may have transformative effects, changing the preferences and values of the users themselves and leading to radicalization”
From Stewardship of global collective behavior (2021) · cites this paper as [128]
Research has revealed that algorithms lead users to radical or age-inappropriate content.
“Despite the opacity, research has revealed how algorithms lead users to radical or age-inappropriate content (128, 164), exacerbate disparities in health (123), and increase bias in policing (124).”
From Stewardship of global collective behavior (2021) · cites this paper as [128]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Alt-right channel-recommendation row exceeds 100%
MinorThe five channel-recommendation percentages in the Alt-R row of Table 3 sum to 116.83%, although they describe the distribution of outgoing recommendations among destinations. The corresponding video-recommendation percentages sum to 100%.
Reported 100 · recomputed 116.83
“| Alt-R | 25.73/ 1.89 | 42.94/ 1.15 | 25.73/ 8.55 | 1.35/ 3.38 | 21.08/ 85.03 |”
Alt-lite-to-Alt-right recommendation percentage differs from Table 3
MinorThe text gives 3.08% for Alt-lite channel recommendations to Alt-right channels, while Table 3 gives 3.38% for that source and destination.
“Alt-right channels are only recommended by Alt-lite channels (3 . 08%).”
“| Alt-L | 13.69/ 2.46 | 55.15/ 12.70 | 3.38/ 0.13 | 2.82/ 3.24 | 24.96/ 81.47 |”
Video-recommendation reachability contradicts the claim that it is impossible
MinorThe abstract says Alt-right videos are reachable only through channel recommendations, but Section 7 reports nonzero Alt-right reachability through video recommendations from Alt-lite.
“Alt-right videos are reachable only through channel recommendations.”
“For video recommendations, reaching Alt-right channels from other communities is less likely. From the Alt-lite, reachability@5 is of around 0 . 05%.”
Reverse Alt-right-to-Alt-lite percentage is incorrect
MinorIn Table 6, 2,962 infected users out of 5,691 tracked users is approximately 52.05%, not 525%.
Reported 525 · recomputed 52.047092
“| | | 2017 | 2962 | 5691 | 0.64% | 525% |”
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Deep Neural Networks for YouTube Recommendations
Covington, P., Adams, J., & Sargin, E. (2016) · Proceedings of the 10th ACM Conference on Recommender Systems · doi:10.1145/2959100.2959190 · Cited by 3,539 · see citing works
Mechanisms of Political Radicalization: Pathways Toward Terrorism
McCauley, C. R., & Moskalenko, S. (2008) · Terrorism and Political Violence · doi:10.1080/09546550802073367 · Cited by 1,222 · see citing works
The YouTube video recommendation system
Davidson, J., Liebald, B., Liu, J., Nandy, P., Vleet, T. V., Gargi, U., Gupta, S. K., He, Y. H., Lambert, M., Livingston, B., et al. (2010) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1864708.1864770 · Cited by 1,221 · see citing works
(2018) · Cited by 900 · see citing works
Digital Discrimination: The Case of Airbnb.com
Edelman, B. G., & Luca, M. (2014) · doi:10.2139/ssrn.2377353 · Cited by 471 · see citing works
Measuring personalization of web search
Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488435 · Cited by 396 · see citing works
Measuring price discrimination and steering on e-commerce web sites
Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663744 · Cited by 368 · cited 1× · see citing works
“Previous large scale algorithmic auditing include measuring discrimination on AirBnB [14], personalization onweb search [19] and price discrimination on e-commerce web sites [20].”
BackgroundKill All Normies: Online Culture Wars from 4chan and Tumblr to Trump and the Alt-Right
Nagle, A. (2017) · Cited by 306 · see citing works
Alternative Influence: Broadcasting the Reactionary Right on YouTube
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On the Origins of Memes by Means of Fringe Web Communities
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Vombatkere, K., Mousavi, S., Zannettou, S., Roesner, F., & Gummadi, K. P. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645600 · Cited by 29 · see citing works
Hate begets Hate: A Temporal Study of Hate SpeechPreprint
Mathew, B., Illendula, A., Saha, P., Sarkar, S., Goyal, P., & Mukherjee, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10966 · Cited by 29 · see citing works
Papakyriakopoulos, O., Engelmann, S., & Winecoff, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580644 · Cited by 31 · see citing works
Examining Video Recommendation Bias on YouTube
Kirdemir, B., Kready, J., Mead, E. L., Hussain, M. N., & Agarwal, N. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-78818-6_10 · Cited by 27 · see citing works
Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels
Ribeiro, M. H., Jhaver, S., Zannettou, S., Blackburn, J., Stringhini, G., Cristofaro, E. D., & West, R. (2021) · UCL Discovery (University College London) · Cited by 27 · see citing works
Self-citationAI Challenges for Society and Ethics
Whittlestone, J., & Clarke, S. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.3 · Cited by 27 · see citing works
A Survey of the Potential Long-term Impacts of AI
Clarke, S., & Whittlestone, J. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534131 · Cited by 27 · see citing works
Algorithmic Gatekeeping for Professional Communicators
van Dalen, A. (2023) · Routledge eBooks · doi:10.4324/9781003375258 · Cited by 32 · see citing works
Don't Let Netflix Drive the Bus: User's Sense of Agency Over Time and Content Choice on Netflix
Schaffner, B., Stefanescu, A., Campili, O., & Chetty, M. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579604 · Cited by 26 · see citing works
Mendonça, R. F., Almeida, V. A. F., & Filgueiras, F. D. B. (2023) · doi:10.1093/oso/9780192870070.001.0001 · Cited by 26 · see citing works
Self-citationConspiracy Brokers: Understanding the Monetization of YouTube Conspiracy Theories
Ballard, C., Goldstein, I., Mehta, P., Smothers, G., Take, K., Zhong, V., Greenstadt, R., Lauinger, T., & McCoy, D. (2022) · The Web Conference · doi:10.1145/3485447.3512142 · Cited by 28 · see citing works
Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes
Efstratiou, A., & Cristofaro, E. D. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555589 · Cited by 26 · see citing works
Liu, N., Hu, X. E., Savas, Y., Baum, M., Berinsky, A. J., Chaney, A. J. B., Lucas, C., Mariman, R., Benedictis-Kessner, J. D., Guess, A. M., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2318127122 · Cited by 23 · see citing works
Albadi, N., Kurdi, M., & Mishra, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555618 · Cited by 23 · see citing works
Picturing Opaque Power: How Conspiracy Theorists Construct Oppositional Videos on YouTube
Grusauskaite, K., Harambam, J., & Aupers, S. (2022) · Social Media + Society · doi:10.1177/20563051221089568 · Cited by 23 · see citing works
- Authors
- Ribeiro, M. H., Ottoni, R., West, R., Almeida, V. A. F., & Meira, W.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Universidade Federal de Minas Gerais (BR)
- UFMG
- Venue
- Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2020-01-27
- Pages
- 131-141
- OpenAlex
- W2969399596
- Semantic Scholar
- 57911e835999c5b0fc8aea382bd7569a180503ae
- Cited by
- 580
- References
- 23
- Citations by year
- 2026: 110 · 2025: 70 · 2024: 89 · 2023: 105 · 2022: 78 · 2021: 69 · 2020: 52 · 2019: 6 · 2012: 1
- FWCI
- 161.71
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Atmosphere
- Capes
- INCT-Cyber
- Google Research Award
- Fapemig
- CNPq
- MASWEB
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Political Science and International Relations › Populism, Right-Wing Movements
- Social Sciences › Communication › Social Media and Politics
- Keywords
- radicalization, YouTube, far-right ideology, alt-right, user engagement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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