Auditing Radicalization Pathways on YouTube
machine learning data science empirical study youtube radicalization alt-right alt-lite idw intellectual dark web alt right alt lite jordan peterson joe rogan pipeline recommended network diffusion social graph infected ideology radical analysis suggested filter bubble fringe
This paper claims that there is a radicalization pipeline on YouTube pushing people towards the Alt-Right, backing up their claims with empirical analysis of channel recommendations and commenting behavior. I suggest that there is a much simpler explanation of this data: A basic diffusion process.

Abstract:
Non-profits and the media claim there is a radicalization pipeline on YouTube. Its content creators would sponsor fringe ideas, and its recommender system would steer users towards edgier content. Yet, the supporting evidence for this claim is mostly anecdotal, and there are no proper measurements of the influence of YouTube's recommender system. In this work, we conduct a large scale audit of user radicalization on YouTube. We analyze 331,849 videos of 360 channels which we broadly classify into: control, the Alt-lite, the Intellectual Dark Web (I.D.W.), and the Alt-right ---channels in the I.D.W. and the Alt-lite would be gateways to fringe far-right ideology, here represented by Alt-right channels. Processing more than 79M comments, we show that the three communities 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 million recommendations for videos and channels between May and July 2019. We find that Alt-lite content is easily reachable from I.D.W. channels via recommendations and that Alt-right channels may be reached from both I.D.W. and Alt-lite channels. Overall, we paint a comprehensive picture of user radicalization on YouTube and provide methods to transparently audit the platform and its recommender system.

Authors: Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virgílio A. F. Almeida, Wagner Meira

https://arxiv.org/abs/1908.08313

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 Hi there! Today we're going to look at Auditing Radicalization Pathways on YouTube by Manuel Horta-Riberio at AL. So this paper is a bit different than the one we're usually looking at, but since I'm a YouTuber and this is in the kind of a data science realm, I thought it fits neatly. So yeah, we'll have a look. And this is mostly going to be an analysis and my opinion on it, so take that for what it is. This is, in my opinion, a paper where you can see very well what it looks like when you deceive yourself. So when you have a hypothesis of something and then only collect data that matches that, and you don't think of simpler solutions that explain the data, and therefore you don't think of experiments that could differentiate the simple solutions from what you propose. So it's a good example of how you can kind of trick yourself into believing you found something. And this isn't now about YouTube or anything. This happened to me so many times. It always pays off to take a step back and say, is there a simpler explanation for what's happening? And this is what I think is exactly happening here. So I'll present to you their hypothesis and then I'll present to you my kind of what I think is going on and a model that explains the data much much much easier and simpler and actually better. So let's dive in. This paper basically claims the following. So on YouTube there are channels and channels are, you know, independent channels. They make videos and you can actually arrange these channels. So each dot here is a channel. You can arrange these channels in kind of a network. And two channels you can claim they're connected and they can be a connection strength or whatever. For simplicity they can be connected if, for example, their topics are similar, if they reference each other, if they are recommended by YouTube from each other, if they have the same users watching those same channels or the videos of these channels. There are a number of metrics where you could make channels connected but all of them will turn out similar, like will give you the similar structure of channels being connected. Oh that's connected twice. So you can kind of build a graph of how these channels are connected and what you can do then is you can cluster them. You don't have to build a graph to cluster them but you can cluster the channels and what will emerge are parts of the graph that are very well connected. Right here this might be connected with this and with this. Parts of graph that are very well connected and are kind of well connected within and more sparsely connected to others, like also have a larger distance in between them. So if you start out from one channel and you're kind of watching recommended videos and recommended channels and so on, you'll stroll along here, you will get much faster to these things than to the other things. So these are called communities usually in these kind of social network analysis. So on YouTube you know there is a community for makeup, there's a community for sports, within sports there is a community for soccer, there's one for basketball and so on. So these are all these kind of communities that you can discover by clustering. This paper mainly deals with three communities. Namely the first of all is the IDW, which is the intellectual dark web. They discuss this here. So the intellectual dark web is they describe as a group of individuals that are in a rolling conversation with each other about topics that are, let's say, usually kind of difficult to talk about, such as gender differences or intelligence research in certain areas or even you know regular politics, but kind of the intellectual dark web are a wide variety of people that basically are conversing with each other about topics. The description is a bit vague but the main aspect is conversation and maybe topics that are kind of on the edge of what's acceptable to talk about. But the opinions range widely on these topics. The second group is the alt-right. And the alt-right here is kind of the, they're defined as ethno-nationalists. For example, here is an example, the fringe ideas such as white ethno-state, white supremacist ideology and so on. So specifically ethno-nationalists, nationalists that I think nations should be organized to along the lines of ethnicity. And the goal of the paper is actually to show that there is a kind of a dangerous pipeline on YouTube that will drive people to the alt-right and drive people into these radical ideas of the alt-right. Kind of in between is the alt-light, which is here defined as civic nationalists, which is simply as I understand it means that people should be organized into nations, not along ethnicity, but just should organize themselves into sovereign communities. And it would be more of your libertarian, classically liberal people, whereas the alt-right would be more of your, let's say, authoritarian right-wing person. So these three communities, they have a fourth community which is what they call a control group. And the control group consists of what they say are kind of mainstream channels on YouTube, simply to differentiate them from these three and two, see what's going on with them and if there is a difference. So this is kind of the setup and as I said the hypothesis is the following. People go on YouTube, so YouTube is here, YouTube, people come on YouTube, they go around, they explore a bit and all of a sudden they find IDW videos. These are recommended by YouTube on a fairly regular basis. That may mean they're interesting, people find it, they find it interesting and so on. And then there from the IDW there are recommendations and links to the alt-light. And the alt-light are still, so as I read this paper there is kind of an undertone, kind of the IDW and the alt-light are still okay. Like they discuss ideas that are sometimes political and so on, but the real worry is the alt-right, the kind of radical right-wing ethnic nationalists. And I mean yes, the formulation I can agree with. And then they claim, so you find IDW, that they have links to the alt-light or links, I mean recommendations and so on. And from the alt-light and to a certain degree also from the IDW you can then find the alt-right. So even though a user that goes on YouTube at first isn't likely to find the alt-right videos because it's fringe, it's extreme and so on, by through the YouTube recommendation algorithm basically by going to the IDW finding this, then from there they'll find the alt-light and from there and from the IDW they will then find the alt-right. So they claim that there's this pathway of radicalization here that kind of pushes people towards the alt-right. And that's their hypothesis. And they claim that they have evidence to support this and I claim that there is a simpler solution, namely... So first of all let me state I don't like the alt-right. I think their ideas are despicable. I should go without saying, though I have said it now, so you know, just as a disclaimer I'm not defending anyone here. I'm simply saying this paper has a simpler explanation for their data. Namely, what I think is happening here is YouTube again is channels. Each dot here is a channel. Channels can be clustered as such, right there, as we saw before. I'm just drawing more of them right now. Channels, channels, channels, channels, channels, channels, channels. So what I think is happening is there is a control group, what they call the control group. It's over here, it's large control, right? It's a bunch of channels. Then, which is kind of mainstream media, then over here there is, let's say, alternative media where all of these three groups belong into. So at some point you will have the IDW, then maybe a bit further away from the control group, but very close to the IDW you would have the alt-light, and very close to the two, maybe here you would have the alt-right. So notably, in my model, the IDW and the alt-light are kind of close together. They are in terms of comparative distance. So if you cluster these channels, let's say audience or topics or and so on, it will turn out that all of these three are far, far away from the control group. Those two are very close to each other and then here there is some distance, but how much distance is a question? But of course it's going to be smaller distance than the distance to the control group here. I mean I could draw the alt-right, maybe a more accurate picture would be something like this. So whatever, I mean it doesn't matter the details, but the distance here is smaller than the distance to the control group. In this model a second thing is also important, namely the alt-right, as you can see here, is much much smaller than the IDW and the alt-light. And these again are much smaller than the control group. And this I think accounts for most, so the distance relations between these and the size of the clusters account for most. So with size I mean mainly number of channels and also audience. This accounts for most of the data better than their model. So just keep this in mind. And my model of course doesn't include any kind of pipeline that they suggest. So first of all they go ahead and they say, alright, they collect channels. So they collect data for this and you know we could go over how they collect the data and criticize that and so on. They do human annotation and they start from already published reports and so on, which themselves can be criticized. I'm not gonna go into their data collection methodology. It can have mistakes, but then any collection methodology can have mistakes. What they end up with is a number of channels and here are the top channels from each category. And as you can see alt-right, alt-light, intellectual dark web, and control. So already here you can see pretty clearly the model I have in mind. They acknowledge all of this by the way. Look at the size of the alt-right channels, the biggest ones, compared to the size of the alt-light and the intellectual dark web. They're much much smaller in number of views. And then compare this to the size of the control group. The control group again is again larger than the other two groups. So just keep it in mind. Second thing to keep in mind, look at these channels. Maybe you know some of them. Joe Rogan, Sargon of Akkad, Paul Joseph Watson, Sticks Hexenhammer. These are youtubers. These are individuals making YouTube clips, creating content for YouTube, being on this platform. Whereas if you compare it with the control group, what's here? Vox, GQ, Wired, Business Insider. These aren't youtubers. These are websites or traditional media companies or their own kind of blogs and so on that have a YouTube channel where YouTube is one of the outlets of this media company. So I think there's a giant discrepancy here in the control group that can explain also some of this data that you see. So keep that in mind. I think the control group, they say they don't try to capture the user dynamic with the control group, but I think that there's many problems with this control group, including the fact that these are kind of traditional mainstream media that just have YouTube as an outlet. Moreover, a lot of these like Vox or Vice, they are clickbait media and rage bait media that it has worked for a number of years, but the algorithms are becoming more attuned to clickbait and these are crashing fast. Whereas the more youtuber people, they are not susceptible to that much to kind of the abolishment of clickbait. Alright, so this is the data. They have all these channels, they have all these videos and they first of all give some stats on it. Here you see on the bottom is always the year. So they do this over time and you see the active channels which are channels that have uploaded videos in some time. See the control group again is larger but has started to flatten out in the last few years. Whereas these communities, they are relatively flourishing. Another interesting point is that the paper somehow tries to tie this to the election of Donald Trump in 2016. But I think this is just kind of in there to gain relevance. A lot of these kind of trends and so on you'll see already start before that. So the start of the rise here, if you see these bumps here and so on, a lot of them start before 2016. So as we go through this make up your own mind of how much this is actually tied to the election or not. I think it's much more the years when clickbait started to go down as a business model. Never mind though. So the active channels growing, though the control group not growing as much. Videos published, even though the control group isn't growing so much, they still publish the most videos. But you can see generally the site is growing. Generally YouTube is growing. Like counts. And here you see something interesting starting to happen. Namely these communities, especially the alt-light and the intellectual dark web, they're starting to catch up. And this is one of the things that the paper also states is that if you look at for example comments per video, this light and the intellectual dark web outperform the control group vastly. Also if you look at views per video and likes per video, the control group simply don't have an engaged audience. Which I think first of all is because they produce clickbait. Second of all they're just not that interesting. And third of all they're not youtubers. Like this isn't their thing. They're just simply an outlet. But yeah so that's kind of a one, just kind of a bunch of metrics that they show here. The next table is a bit more interesting. In the next table they do a user intersection. So what they do is they collect all these videos and then they collect all the comments of these videos. And the comment of course always comes with a username. You need to be logged into YouTube to make a comment. And they see which users comment on multiple videos or on videos of multiple categories. And then they can look at how many users of category A also comment in category B and vice versa. So they have two metrics here. Jucard similarity which is for two communities A and B, number of users commenting on A and B divided by number of users commenting on A or B. And the second the overlap coefficient is number of users commenting on A and B divided by the minimum size of A and B. They say that the overlap coefficient is more useful to compare communities of different sizes. So we'll look at that. The top graphs are always always jacquard difference and the jacquard similarity in the bottom one are overlap coefficient. The first graphs though are number of commenting users per year. And you already see that even though the control group has much more views and probably much more videos, much larger, the comments don't... so the again the the users of the all light and the intellectual dark web are much more engaged. Also comments per user. This is the cumulative distribution function. Most people that comment on control group videos maybe comment once and then but these other communities they comment more. Self similarity means year after year. So always compared to the year before how many users are similar. So how well do these communities retain users. And you can already see here the control group is actually very bad at retaining users. It does have this overlap coefficient high but it has the jacquard self similarity low which basically if you think of the formula of the jacquard similarity means that this number is small and this number is high which means that A and B are very disjoint which means that the last year's users aren't this year's users basically. So they they constantly have to appeal to new users because they're losing old users because well I guess they're boring. Whereas the all light and intellectual dark web are much more are much better at retaining users. Interestingly the alt right not as good as retaining users as the other two. This could also be an effect of size like if your community is smaller the users might wander away more quickly. But I think this already speaks against the radicalization pipeline. If the if the alt right if YouTube was radicalizing people towards alt right we I think we would see a the alt right being on top of user retention. Then here they have intersections between communities. So green here is alt light and IDW while the blue is alt right and alt light and the other blue is alt right and IDW. So basically the green is alt light and IDW and the blues are the other two. And we see that the overlap in terms of overlap coefficient is similar. The overlap in terms of jacquard similarity the alt light and the IDW are very much more sharing users which in the picture I painted makes sense if you think my model is valid. My model explains this very well in that these two communities are quite close together therefore share a similar user base. The alt right smaller and a bit further apart therefore not as similar though more similar than the control group which is the last graph. The last graph is sorry the last graph is how similar are these communities to the control group and here we see the IDW and the alt light kind of similar. The alt right not as similar though in the overlap coefficient they're about the same. So the paper here claims oh look at the similarity this is definitely a radicalization. So they don't claim yet this is a radicalization pipeline but they claim that there's a higher similarity. If you actually look at the numbers it's not so I mean here you're around the 50% similarity and here at the end you're also around the 50% similarity with the control group. So this is within these groups and this is here with the control group. Also here if I look at the kind of mean here you're at whatever 20-18% and here you're also you may be a bit lower but you're also going towards this. What it looks to me like rather than there being a radicalization pipeline if you look at the shape of this and kind of where it starts in 2013-2014 it starts to go up here and you look at the shape of this it's simply the same shape delayed and I mean there's no reason why this graph wouldn't go up here wouldn't go up here in the future and reach the exact same numbers as here. It seems that the graph is simply shifted which makes total sense if you think these communities are... I'm gonna draw the same picture here... right IDW, alt light and over here control. If you think they're they're like that if you think simply think well YouTube is growing users are growing users are starting somewhere here and then spreading out pretty much randomly like they're spreading out spreading out spreading out users start here spreading out users start here spreading out here spreading out everywhere users just kind of there's a diffusion process going on not in a particular direction like they claim if there is just a diffusion process going on what would you expect you would expect users that started here to reach the IDW and alt right much sooner than they reach the control group but ultimately as the diffusion continues all users will have commented on most videos if you run YouTube infinitely and these numbers would go that's why the numbers go up right if you just let it go the diffusion process will go along and it simply takes a longer time to go from here all the way over here then it goes then between these communities so to me we're looking at a simple diffusion process here that is shifted in time and that explains very much the discrepancy in number but also the shape of the curve that is exactly the same but shifted their model does not explain the shape of the curve they simply say well here it's 75% and here it's only 50% that means that these communities are kind of shipping users towards each other so I think the explanation is easier then so they claim this does not alone kind of show that there is a pipeline what they now do however will show that basically so they claim this is the experiment that really shows that there is it is pipeline so what they do is they define what they call an infection so what they say is okay we are for example this this row here we're taking users that are alt light users at the beginning in this time so basically they only comment on the only comment on alt light videos during this time right so discard all users that comment on anything else just retain the ones that only comment on alt light videos during this time then we're going to follow them over time and see how many of them have at least one comment in an alt right video so this is only directed from the community over here towards the alt right and then they call a user infected specifically if they comment on one or two alt right videos they're lightly infected if they comment on three to five they're mildly infected and if they comment on more they're severely infected so as you can see users starting from the alt light or from the IDW or from both they will become in some will become infected over time namely and I postulate we simply look at the since that the tendencies between the groups are similar we'll simply look at the light infections here so they say okay after you know in 2018 about 8 to 10 percent of the users become infected in these groups you see here here about the same trajectories whereas it so whereas in the control group it's less here though honestly I don't think it's that much less right I think that again I think there's a normal diffusion process here they do this similarly with the with the other ones and to me like to them this makes total sense like oh yeah users that start in these communities they migrate to get infected by the alt right they go towards the alt right because you can find it so easily and to me this simply looks like a normal diffusion process here's what you need if you want and by the way the control group isn't that much different here's what you need if you want to show that there is a pipeline in this direction you need this exact same graph in the other direction and you need to show that people that started in the alt right do not go back in the same fashion towards the alt light or the IDW and they do especially not go to the control group you need to show this basically between each pair of these and you need to show that the direction of infection is only in a single direction namely towards radicalization otherwise you're just looking at a normal diffusion process between differently distance and differently sized groups so they go on to analyze and they say well how much basically how much of the alt right audience makes is made up by people that have been radicalized that have been infected so that this infection is kind of their proxy for what they call a radicalization and if you become infected then basically you're not part of the alt right or something even though you might have you might have commented something negative actually the might engage with their ideas and call them their crap but in any case you're now infected and they ask themselves how much of the alt right audience has are of these infected so basically how much of the alt right audience have our people that in the past have been not alt writers have been exclusively commenting on alt light or IDW videos and they find that for example for alt light 23% of the alt right audience are former alt lighters and have our former alt lighters that have now made one comment on an alt right video so that their claim is well there is a sizable portion of the alt right that at the beginning wasn't alt right that basically became infected and therefore that that kind of shows this radicalization pipeline that the alt right audience is mainly consistent of people that have not been alt right previously but have become so and to me again this is simply a function of the size of these communities right if if you think of this again and you start randomly somewhere on YouTube let's let's make this assumption people start randomly somewhere on YouTube what's the probability that you're going to start in the alt right very small right so what's the the kind of natural let's say the natural size of alt right before users go and migrate is very tiny right so not many users are going to be what you would consult originally alt writers whatever their their first comment basically what this thing measures is where is your first comment and are any of your subsequent comments alt right if your first comment is not in the alt right then you become a potential candidate for infection and if any comment is on the alt right then you're infected so what's the probability that your first comment is not alt right well you're gonna land somewhere on YouTube YouTube is huge the alt right is very small thus that probability is extremely small and then you let you simply let people diffuse let them diffuse let them diffuse some will end up in the alt right and since the alt right is so small to begin with actually most people that will comment at some point on an alt right video will will have their first comment from somewhere outside the alt right videos simply simply a numbers game right simply the alt right is so small that this is virtually guaranteed so what they find here is again simply an evidence of a regular diffusion process between these differently sized groups and the claims they make from this are just over the top again that their comparison to the control group if you if you look at the numbers they're actually not that different from this from the IDW numbers there they're different than the alt light here substantially different but again that simply a function of distance in my opinion in these in these clusters lastly they look at the YouTube recommender system and they say okay if we look at these videos and the channels and we look at on these videos what other videos are recommended and what other channels are recommended so if you have like a video on YouTube you have the video here and here you have like recommended videos similarly when you have a channel right you have a channel this is a person yeah I'm this person the person can have first of all they can have featured channels where they say look these are channels that I find cool I go check them out and then they also have recommended channels that are kind of given by YouTube as recommendations so here YouTube controls basically everything here the creator controls part and the YouTube controls dollar part so they look to both first of all the channels channels recommend recommendations so these are both sections here and they look at if you start on a alt light video how likely if you do a random walk are you to end up in the alt right or in the intellectual dark web or control group after one step two steps three steps four steps so that the big line is the random Walker and actually the dashed line is the distance if you were to target Lee go into the direction of such a video like what's the minimum number of clicks you need and you can see here the the if you start at alt light after one or two steps the random Walker is kind of a 2% chance to end up at an alt right video and about a 25% chance here of ending up in a intellectual dark web video and about a 50% chance of ending up again at an alt light video the scales here really different so it's very difficult to judge how it compares to the control group which is kind of at zero here but to me again this is a reflection of the size of these communities and I think it's a bit you know we are to to then claim oh these are reachable basically so 2% chance of landing on an alt right video um I'm not sure but again if you compare if you start from the control group there's almost no chance you'll end up in a alt right video so I guess the comparison is is okay if you compare to control group if you start look at videos however again if you start at alt light after one step you are approximately 25% likely to be in an IDW video you're a bit over 50% likely to stay in an alt light video however compare this to channels you're almost super unlikely to end at a control channel if you start at an alt light channel but in video recommendations you're actually also about 25% chance of ending in a control group video where as look at the scale here you're only about 0.03% likely to end up in an alt right video and also here so here even look at this if you start an IDW video the chance that you're going to end up in a control again super high much higher than an alt light video whereas with the channel recommendations this was completely turned around so we see the alt right completely loses when it comes to video recommendations and mainly the control group gains compared to the channel recommendations I think here's what I think I think this is due to this section here this section here where the creators have power and also this section here YouTube recommending I think they're putting a lot of work into the video recommendations I think they're putting not that much work into these recommendations and by work I mean actually manually intervening and deciding what's kind of good videos and bad videos and the the control group they're probably there's probably big advertisement money in that so they might be pushed up a bit in the video recommendations since most people are going by video recommendations I've actually never used the channel recommendations feature and the channel recommendations first of all the creator has power over part of it and then also YouTube may not put as much work into these related channels so both have in the effect that I would say that that the data here first of all it doesn't doesn't convince me of a radicalization pipeline it simply convinces me that some communities are larger smaller and closer together but second of all that this down here if you forget about the alt-right for a moment yeah they're irrelevant this down here actually compared to up here shows maybe a bit of evidence of an algorithmic promotion of these mainstream media channels compared to how the communities are actually clustering which I think this this up here might be a much more accurate picture so you know that it's just kind of a funky thing in the data yeah that alt-right is irrelevant to this part because they're they're just too small so this is this is kind of my take on this they didn't give recommendations and is this a pipeline and so on and I don't think so you've now heard my idea and you've heard their idea decide for yourself but I think it's a good example of how if you are convinced of an underlying mechanism you're going to collect evidence in support of that mechanism and if you catch yourself doing that really really think isn't there an easier explanation for this all right that was it for me have fun
 Hi there! Today we're going to look at Auditing Radicalization Pathways on
 YouTube by Manuel Horta-Riberio at AL. So this paper is a bit different than the
 one we're usually looking at, but since I'm a YouTuber and this is in the kind
 of a data science realm, I thought it fits neatly. So yeah, we'll have a look.
 And this is mostly going to be an analysis and my opinion on it, so take
 that for what it is. This is, in my opinion, a paper where you can see very
 well what it looks like when you deceive yourself. So when you have a
 hypothesis of something and then only collect data that matches that, and you
 don't think of simpler solutions that explain the data, and
 therefore you don't think of experiments that could differentiate the simple
 solutions from what you propose. So it's a good example of how you can kind of
 trick yourself into believing you found something. And this isn't
 now about YouTube or anything. This happened to me so many times. It always
 pays off to take a step back and say, is there a simpler explanation for what's
 happening? And this is what I think is exactly happening here. So I'll present
 to you their hypothesis and then I'll present to you my kind of what I think
 is going on and a model that explains the data much much much easier and
 simpler and actually better. So let's dive in. This paper basically claims
 the following. So on YouTube there are channels and channels are, you know,
 independent channels. They make videos and you can actually arrange these
 channels. So each dot here is a channel. You can arrange these channels in kind
 of a network. And two channels you can claim they're connected and they can be
 a connection strength or whatever. For simplicity they can be connected if, for
 example, their topics are similar, if they reference each other, if they are
 recommended by YouTube from each other, if they have the same users watching
 those same channels or the videos of these channels. There are a number of
 metrics where you could make channels connected but all of them
 will turn out similar, like will give you the similar structure of channels
 being connected. Oh that's connected twice. So you can kind of build a
 graph of how these channels are connected and what you can do then is you
 can cluster them. You don't have to build a graph to cluster them but you
 can cluster the channels and what will emerge are parts of the graph that are
 very well connected. Right here this might be connected with this and with
 this. Parts of graph that are very well connected and are kind of well
 connected within and more sparsely connected to others, like also have a
 larger distance in between them. So if you start out from one channel and you're
 kind of watching recommended videos and recommended channels and so on, you'll
 stroll along here, you will get much faster to these things than to the other
 things. So these are called communities usually in these kind of
 social network analysis. So on YouTube you know there is a community for
 makeup, there's a community for sports, within sports there is a community for
 soccer, there's one for basketball and so on. So these are all these kind of
 communities that you can discover by clustering. This paper mainly deals with
 three communities. Namely the first of all is the IDW, which is the
 intellectual dark web. They discuss this here. So the intellectual dark web is
 they describe as a group of individuals that are in a rolling conversation with
 each other about topics that are, let's say, usually kind of difficult to talk
 about, such as gender differences or intelligence research in certain areas
 or even you know regular politics, but kind of the intellectual dark web are a
 wide variety of people that basically are conversing with each other about
 topics. The description is a bit vague but the main aspect is conversation
 and maybe topics that are kind of on the edge of what's acceptable to talk
 about. But the opinions range widely on these topics. The second group is the alt-right.
 And the alt-right here is kind of the, they're defined as ethno-nationalists.
 For example, here is an example, the fringe ideas such as white ethno-state,
 white supremacist ideology and so on. So specifically ethno-nationalists,
 nationalists that I think nations should be organized to along the lines of
 ethnicity. And the goal of the paper is actually to show that there is a
 kind of a dangerous pipeline on YouTube that will drive people to the alt-right
 and drive people into these radical ideas of the alt-right. Kind of in between is
 the alt-light, which is here defined as civic nationalists, which is simply as I
 understand it means that people should be organized into nations, not along
 ethnicity, but just should organize themselves into sovereign communities.
 And it would be more of your libertarian, classically liberal people, whereas the
 alt-right would be more of your, let's say, authoritarian right-wing person.
 So these three communities, they have a fourth community which is what they call a
 control group. And the control group consists of what they say are kind of
 mainstream channels on YouTube, simply to differentiate them from these three
 and two, see what's going on with them and if there is a difference. So this is
 kind of the setup and as I said the hypothesis is the following.
 People go on YouTube, so YouTube is here, YouTube, people come on YouTube, they go
 around, they explore a bit and all of a sudden they find IDW videos. These are
 recommended by YouTube on a fairly regular basis. That may mean they're
 interesting, people find it, they find it interesting and so on. And then there from
 the IDW there are recommendations and links to the alt-light. And the alt-light
 are still, so as I read this paper there is kind of an undertone, kind of the IDW
 and the alt-light are still okay. Like they discuss ideas that are
 sometimes political and so on, but the real worry is the alt-right, the
 kind of radical right-wing ethnic nationalists. And I mean yes, the
 formulation I can agree with. And then they claim, so you find IDW,
 that they have links to the alt-light or links, I mean recommendations and so on.
 And from the alt-light and to a certain degree also from the IDW you can then
 find the alt-right. So even though a user that goes on YouTube at first isn't
 likely to find the alt-right videos because it's fringe, it's extreme and so
 on, by through the YouTube recommendation algorithm basically by
 going to the IDW finding this, then from there they'll find the alt-light and
 from there and from the IDW they will then find the alt-right. So they claim
 that there's this pathway of radicalization here that kind of pushes
 people towards the alt-right. And that's their hypothesis. And they claim
 that they have evidence to support this and I claim that there is a simpler
 solution, namely... So first of all let me state I don't like the alt-right. I think
 their ideas are despicable. I should go without saying, though I have said it now,
 so you know, just as a disclaimer I'm not defending anyone here. I'm simply saying
 this paper has a simpler explanation for their data. Namely, what I think is
 happening here is YouTube again is channels. Each dot here is a channel.
 Channels can be clustered as such, right there, as we saw before. I'm just drawing
 more of them right now. Channels, channels, channels, channels, channels, channels, channels.
 So what I think is happening is there is a control group, what they call the
 control group. It's over here, it's large control, right? It's a bunch of channels.
 Then, which is kind of mainstream media, then over here there is, let's say,
 alternative media where all of these three groups belong into. So at some
 point you will have the IDW, then maybe a bit further away from the control group,
 but very close to the IDW you would have the alt-light, and very close to the two,
 maybe here you would have the alt-right. So notably, in my model, the
 IDW and the alt-light are kind of close together. They are in terms of
 comparative distance. So if you cluster these channels, let's say audience or
 topics or and so on, it will turn out that all of these three are far, far
 away from the control group. Those two are very close to each other and then
 here there is some distance, but how much distance is a question?
 But of course it's going to be smaller distance than the distance to the
 control group here. I mean I could draw the alt-right, maybe a more
 accurate picture would be something like this. So whatever, I mean
 it doesn't matter the details, but the distance here is smaller
 than the distance to the control group. In this model a second thing is
 also important, namely the alt-right, as you can see here, is much much smaller
 than the IDW and the alt-light. And these again are much smaller than the
 control group. And this I think accounts for most, so the distance relations
 between these and the size of the clusters account for most. So with
 size I mean mainly number of channels and also audience. This accounts
 for most of the data better than their model. So just keep this in mind.
 And my model of course doesn't include any kind of pipeline that they
 suggest. So first of all they go ahead and they say, alright, they collect
 channels. So they collect data for this and you know we could go over how they
 collect the data and criticize that and so on. They do human annotation and they
 start from already published reports and so on, which themselves can be criticized.
 I'm not gonna go into their data collection methodology. It can have
 mistakes, but then any collection methodology can have mistakes. What they
 end up with is a number of channels and here are the top channels from each
 category. And as you can see alt-right, alt-light, intellectual dark web,
 and control. So already here you can see pretty clearly the model I have in mind.
 They acknowledge all of this by the way. Look at the size of the alt-right
 channels, the biggest ones, compared to the size of the alt-light and the
 intellectual dark web. They're much much smaller in number of views. And then
 compare this to the size of the control group. The control group again is again
 larger than the other two groups. So just keep it in mind. Second thing to keep in
 mind, look at these channels. Maybe you know some of them. Joe Rogan, Sargon of
 Akkad, Paul Joseph Watson, Sticks Hexenhammer. These are
 youtubers. These are individuals making YouTube clips, creating content for
 YouTube, being on this platform. Whereas if you compare it with the control group,
 what's here? Vox, GQ, Wired, Business Insider. These aren't youtubers. These are
 websites or traditional media companies or their own kind of
 blogs and so on that have a YouTube channel where YouTube is one of the
 outlets of this media company. So I think there's a giant discrepancy
 here in the control group that can explain also some of this data that you
 see. So keep that in mind. I think the control group, they say they don't try to
 capture the user dynamic with the control group, but I think that there's
 many problems with this control group, including the fact that these are
 kind of traditional mainstream media that just have YouTube as an outlet.
 Moreover, a lot of these like Vox or Vice, they are clickbait media and
 rage bait media that it has worked for a number of years, but the algorithms
 are becoming more attuned to clickbait and these are crashing fast.
 Whereas the more youtuber people, they are not susceptible to
 that much to kind of the abolishment of clickbait. Alright, so this is
 the data. They have all these channels, they have all these videos and they first of
 all give some stats on it. Here you see on the bottom is always the year.
 So they do this over time and you see the active channels which are channels
 that have uploaded videos in some time. See the control group again is larger
 but has started to flatten out in the last few years. Whereas these
 communities, they are relatively flourishing. Another interesting point
 is that the paper somehow tries to tie this to the election of Donald Trump in
 2016. But I think this is just kind of in there to gain
 relevance. A lot of these kind of trends and so on you'll see already start
 before that. So the start of the rise here, if you see these
 bumps here and so on, a lot of them start before 2016. So as we go through this
 make up your own mind of how much this is actually tied to the election
 or not. I think it's much more the years when clickbait started to go
 down as a business model. Never mind though. So the active channels
 growing, though the control group not growing as much. Videos published, even
 though the control group isn't growing so much, they still publish the most
 videos. But you can see generally the site is growing. Generally YouTube is
 growing. Like counts. And here you see something interesting starting to happen.
 Namely these communities, especially the alt-light and the intellectual dark web,
 they're starting to catch up. And this is one of the things that the paper also
 states is that if you look at for example comments per video, this
 light and the intellectual dark web outperform the control group vastly.
 Also if you look at views per video and likes per video, the control
 group simply don't have an engaged audience. Which I think first of all is
 because they produce clickbait. Second of all they're just not that interesting.
 And third of all they're not youtubers. Like this isn't their thing. They're
 just simply an outlet. But yeah so that's kind of a one, just kind of a
 bunch of metrics that they show here. The next table is a bit more
 interesting. In the next table they do a user intersection. So what they do is they
 collect all these videos and then they collect all the comments of these
 videos. And the comment of course always comes with a username. You need to be
 logged into YouTube to make a comment. And they see which users comment on
 multiple videos or on videos of multiple categories. And then they can look at
 how many users of category A also comment in category B and vice versa.
 So they have two metrics here. Jucard similarity which is for two
 communities A and B, number of users commenting on A and B divided
 by number of users commenting on A or B. And the second the overlap coefficient
 is number of users commenting on A and B divided by the minimum size of A and B.
 They say that the overlap coefficient is more useful to compare communities of
 different sizes. So we'll look at that. The top graphs are always always
 jacquard difference and the jacquard similarity in the bottom one are
 overlap coefficient. The first graphs though are number of commenting users
 per year. And you already see that even though the control group has much more
 views and probably much more videos, much larger, the comments don't... so the
 again the the users of the all light and the intellectual dark web are much more
 engaged. Also comments per user. This is the cumulative distribution function.
 Most people that comment on control group videos maybe comment once
 and then but these other communities they comment more. Self similarity
 means year after year. So always compared to the year before how many users are
 similar. So how well do these communities retain users. And you can
 already see here the control group is actually very bad at retaining users. It
 does have this overlap coefficient high but it has the jacquard self similarity
 low which basically if you think of the formula of the jacquard similarity means
 that this number is small and this number is high which means that A and
 B are very disjoint which means that the last year's users aren't this year's
 users basically. So they they constantly have to appeal to new users because
 they're losing old users because well I guess they're boring. Whereas the
 all light and intellectual dark web are much more are much better at retaining
 users. Interestingly the alt right not as good as retaining users as the other two.
 This could also be an effect of size like if your community is smaller the
 users might wander away more quickly. But I think this already speaks against the
 radicalization pipeline. If the if the alt right if YouTube was radicalizing
 people towards alt right we I think we would see a the alt right being on top
 of user retention. Then here they have intersections between communities. So
 green here is alt light and IDW while the blue is alt right and alt light and
 the other blue is alt right and IDW. So basically the green is alt light and IDW
 and the blues are the other two. And we see that the overlap in terms of overlap
 coefficient is similar. The overlap in terms of jacquard similarity the alt
 light and the IDW are very much more sharing users which in the picture I
 painted makes sense if you think my model is valid. My model explains this
 very well in that these two communities are quite close together therefore share
 a similar user base. The alt right smaller and a bit further apart
 therefore not as similar though more similar than the control group which is
 the last graph. The last graph is sorry the last graph is how similar are these
 communities to the control group and here we see the IDW and the alt light
 kind of similar. The alt right not as similar though in the overlap
 coefficient they're about the same. So the paper here claims oh look at the
 similarity this is definitely a radicalization. So they don't claim yet this
 is a radicalization pipeline but they claim that there's a higher similarity.
 If you actually look at the numbers it's not so I mean here you're
 around the 50% similarity and here at the end you're also around the 50%
 similarity with the control group. So this is within these groups and this is
 here with the control group. Also here if I look at the kind of mean here
 you're at whatever 20-18% and here you're also you may be a bit lower but
 you're also going towards this. What it looks to me like rather than there being
 a radicalization pipeline if you look at the shape of this and kind
 of where it starts in 2013-2014 it starts to go up here and you look at the
 shape of this it's simply the same shape delayed and I mean there's no reason why
 this graph wouldn't go up here wouldn't go up here in the future and reach the
 exact same numbers as here. It seems that the graph is simply shifted which makes
 total sense if you think these communities are... I'm gonna draw the same
 picture here... right IDW, alt light and over here control. If you think they're
 they're like that if you think simply think well YouTube is growing users are
 growing users are starting somewhere here and then spreading out pretty much
 randomly like they're spreading out spreading out spreading out users start
 here spreading out users start here spreading out here spreading out
 everywhere users just kind of there's a diffusion process going on not in a
 particular direction like they claim if there is just a diffusion process going
 on what would you expect you would expect users that started here to reach
 the IDW and alt right much sooner than they reach the control group but
 ultimately as the diffusion continues all users will have commented on most
 videos if you run YouTube infinitely and these numbers would go that's why the
 numbers go up right if you just let it go the diffusion process will go along
 and it simply takes a longer time to go from here all the way over here then it
 goes then between these communities so to me we're looking at a simple diffusion
 process here that is shifted in time and that explains very much the discrepancy
 in number but also the shape of the curve that is exactly the same but
 shifted their model does not explain the shape of the curve they simply say well
 here it's 75% and here it's only 50% that means that these communities are
 kind of shipping users towards each other so I think the explanation is
 easier then so they claim this does not alone kind of show that there is a
 pipeline what they now do however will show that basically so they claim this
 is the experiment that really shows that there is it is pipeline so what they do
 is they define what they call an infection so what they say is okay we
 are for example this this row here we're taking users that are alt light users
 at the beginning in this time so basically they only comment on the only
 comment on alt light videos during this time right so discard all users that
 comment on anything else just retain the ones that only comment on alt light
 videos during this time then we're going to follow them over time and see how
 many of them have at least one comment in an alt right video so this is only
 directed from the community over here towards the alt right and then they call
 a user infected specifically if they comment on one or two alt right videos
 they're lightly infected if they comment on three to five they're mildly infected
 and if they comment on more they're severely infected so as you can see
 users starting from the alt light or from the IDW or from both they will
 become in some will become infected over time namely and I postulate we simply
 look at the since that the tendencies between the groups are similar we'll
 simply look at the light infections here so they say okay after you know in 2018
 about 8 to 10 percent of the users become infected in these groups you see
 here here about the same trajectories whereas it so whereas in the control
 group it's less here though honestly I don't think it's that much less right I
 think that again I think there's a normal diffusion process here they do
 this similarly with the with the other ones and to me like to them this makes
 total sense like oh yeah users that start in these communities they migrate
 to get infected by the alt right they go towards the alt right because you can
 find it so easily and to me this simply looks like a normal diffusion process
 here's what you need if you want and by the way the control group isn't that
 much different here's what you need if you want to show that there is a
 pipeline in this direction you need this exact same graph in the other direction
 and you need to show that people that started in the alt right do not go back
 in the same fashion towards the alt light or the IDW and they do especially
 not go to the control group you need to show this basically between each pair of
 these and you need to show that the direction of infection is only in a
 single direction namely towards radicalization otherwise you're just
 looking at a normal diffusion process between differently distance and
 differently sized groups so they go on to analyze and they say well how much
 basically how much of the alt right audience makes is made up by people that
 have been radicalized that have been infected so that this infection is kind
 of their proxy for what they call a radicalization and if you become
 infected then basically you're not part of the alt right or something even though
 you might have you might have commented something negative actually the might
 engage with their ideas and call them their crap but in any case you're now
 infected and they ask themselves how much of the alt right audience has
 are of these infected so basically how much of the alt right audience have our
 people that in the past have been not alt writers have been exclusively
 commenting on alt light or IDW videos and they find that for example for alt
 light 23% of the alt right audience are former alt lighters and have our former
 alt lighters that have now made one comment on an alt right video so that
 their claim is well there is a sizable portion of the alt right that at the
 beginning wasn't alt right that basically became infected and therefore
 that that kind of shows this radicalization pipeline that the alt
 right audience is mainly consistent of people that have not been alt right
 previously but have become so and to me again this is simply a function of the
 size of these communities right if if you think of this again and you start
 randomly somewhere on YouTube let's let's make this assumption people start
 randomly somewhere on YouTube what's the probability that you're going to start
 in the alt right very small right so what's the the kind of natural let's say
 the natural size of alt right before users go and migrate is very tiny right
 so not many users are going to be what you would consult originally alt writers
 whatever their their first comment basically what this thing measures is
 where is your first comment and are any of your subsequent comments alt right if
 your first comment is not in the alt right then you become a potential
 candidate for infection and if any comment is on the alt right then you're
 infected so what's the probability that your first comment is not alt right well
 you're gonna land somewhere on YouTube YouTube is huge the alt right is very
 small thus that probability is extremely small and then you let you simply let
 people diffuse let them diffuse let them diffuse some will end up in the alt
 right and since the alt right is so small to begin with actually most people
 that will comment at some point on an alt right video will will have their
 first comment from somewhere outside the alt right videos simply simply a
 numbers game right simply the alt right is so small that this is virtually
 guaranteed so what they find here is again simply an evidence of a regular
 diffusion process between these differently sized groups and the claims
 they make from this are just over the top again that their comparison to
 the control group if you if you look at the numbers they're actually not that
 different from this from the IDW numbers there they're different than the alt
 light here substantially different but again that simply a function of distance
 in my opinion in these in these clusters lastly they look at the YouTube
 recommender system and they say okay if we look at these videos and the channels
 and we look at on these videos what other videos are recommended and what
 other channels are recommended so if you have like a video on YouTube you have
 the video here and here you have like recommended videos similarly when you
 have a channel right you have a channel this is a person yeah I'm this person
 the person can have first of all they can have featured channels where they
 say look these are channels that I find cool I go check them out and then they
 also have recommended channels that are kind of given by YouTube as
 recommendations so here YouTube controls basically everything here the creator
 controls part and the YouTube controls dollar part so they look to both first
 of all the channels channels recommend recommendations so these are both
 sections here and they look at if you start on a alt light video how likely if
 you do a random walk are you to end up in the alt right or in the intellectual
 dark web or control group after one step two steps three steps four steps so that
 the big line is the random Walker and actually the dashed line is the distance
 if you were to target Lee go into the direction of such a video like what's
 the minimum number of clicks you need and you can see here the the if you
 start at alt light after one or two steps the random Walker is kind of a 2%
 chance to end up at an alt right video and about a 25% chance here of ending up
 in a intellectual dark web video and about a 50% chance of ending up again at
 an alt light video the scales here really different so it's very difficult
 to judge how it compares to the control group which is kind of at zero here but
 to me again this is a reflection of the size of these communities and I think
 it's a bit you know we are to to then claim oh these are reachable basically so
 2% chance of landing on an alt right video um I'm not sure but again if you
 compare if you start from the control group there's almost no chance you'll
 end up in a alt right video so I guess the comparison is is okay if you compare
 to control group if you start look at videos however again if you start at alt
 light after one step you are approximately 25% likely to be in an IDW
 video you're a bit over 50% likely to stay in an alt light video however
 compare this to channels you're almost super unlikely to end at a control
 channel if you start at an alt light channel but in video recommendations
 you're actually also about 25% chance of ending in a control group video where
 as look at the scale here you're only about 0.03% likely to end up in an alt
 right video and also here so here even look at this if you start an IDW video
 the chance that you're going to end up in a control again super high much
 higher than an alt light video whereas with the channel recommendations this
 was completely turned around so we see the alt right completely loses when it
 comes to video recommendations and mainly the control group gains compared
 to the channel recommendations I think here's what I think I think this is due
 to this section here this section here where the creators have power and also
 this section here YouTube recommending I think they're putting a lot of work
 into the video recommendations I think they're putting not that much work into
 these recommendations and by work I mean actually manually intervening and
 deciding what's kind of good videos and bad videos and the the control group
 they're probably there's probably big advertisement money in that so they
 might be pushed up a bit in the video recommendations since most people are
 going by video recommendations I've actually never used the channel
 recommendations feature and the channel recommendations first of all the
 creator has power over part of it and then also YouTube may not put as much
 work into these related channels so both have in the effect that I would say that
 that the data here first of all it doesn't doesn't convince me of a
 radicalization pipeline it simply convinces me that some communities are
 larger smaller and closer together but second of all that this down here if you
 forget about the alt-right for a moment yeah they're irrelevant this down here
 actually compared to up here shows maybe a bit of evidence of an algorithmic
 promotion of these mainstream media channels compared to how the communities
 are actually clustering which I think this this up here might be a much more
 accurate picture so you know that it's just kind of a funky thing in the data
 yeah that alt-right is irrelevant to this part because they're they're just
 too small so this is this is kind of my take on this they didn't give
 recommendations and is this a pipeline and so on and I don't think so you've
 now heard my idea and you've heard their idea decide for yourself but I think
 it's a good example of how if you are convinced of an underlying mechanism
 you're going to collect evidence in support of that mechanism and if you
 catch yourself doing that really really think isn't there an easier explanation
 for this all right that was it for me have fun