Algorithm exposed

Investigating Youtube personalization with yTREX

Team Members

Claudio Agosti, Davide Beraldo, Stefano Calzati, Zoé Charpentier, SeongIn Choi, Luca Del Fabbro, Anja Groten, Concetta La Mattina, Salvatore Romano, Giovanni Rossetti, Laura Swietlicki, Jeroen de Vos.


Team Members 1

Contents 1

Summary of Key Findings 2

1. Introduction 2

2. Initial Data Sets 2

3. Research Questions 2

4. Methodology 2

5. Findings 2

6. Discussion 2

7. Conclusion 2

8. References 2

Summary of Key Findings

This research concerns looking more structurally into the personalisation process in-action to be able to question its mechanisms. Secondly, we check if the front-end findings correspondence to with what the official API returns and our experimental collection method.

  1. We started by comparing the related suggestion for all the students in the class and they were different. We attribute the reason to the fact that they were using their everyday browser, and the evidence collected was personalized for their past activities.
  2. All the class installed then a new browser (Brave) compatible with Chrome. With this, we were sure to be able to compare all our activities without inhereting any of our past online activities. When we did that, we could collect, visualize and reproduce graphically the difference between the perception of a profiled user and how YouTube treat someone anonymous (and with no past behaviour).

In our research setup, we kept comparing browsers in different conditions: logged in our personal YouTube account vs clean-browser, checking a video before and after a specific Google query, changing our charset, and watching the same video. In other words, we tried to control variables used by YouTube personalization algorithm in order to infer their weight in our individualized experiences.

We found significant changes generated by the time watched per video and different strategies to suggest videos on the basis of the topic of the video watched. We compared the public information about a famous music video with our collection of data and the overlap is just partial.

The most interesting findings should drive further research on the topic with a bigger database. The first thing we are going to analyze in greater depth is the qualitative and quantitative difference between the recommendations witch follow a political or an a-political content.

Moreover, in the future we have the ambition to implement more specific features to the yTREX tool which allow recording different test group in a specific folder to make it easier to control the variables in Youtube research.

1. Introduction

YouTube provides an API to search for data on the platform, and a superficial explanation of the mechanisms that are used to personalize the user’s experience. This resource is widely known by social media analyst and our goal was to test a new approach.

The platform has been the centre of a couple of popular scandals in recent years. Youtubes recommendations algorithms has been accused of driving users into the “rabbit hole” of extreme content[1]. Recently YouTube tried to give more apparent control on the personalization procedures by introducing the possibility to block some undesirable channels.

Youtube took steps to empower users with more control[2], but this is not a systemic solution. Becca Lewis, an affiliate researcher at Data & Society and the author of a recent study about far-right content on YouTube, said to Wired[3] “These changes seem positive at first glance, but they ultimately still put the burden of responsibility on the user, not on the platform.”

The digital giant also decided to reduce the visibility of so-called borderline content without taking the responsibility to take them off the site. Moreover, the automatic way of defining which content is dangerous could be questioned; it is not a public procedure but rather an algorithm black box (Lapowsky). We only have some information about the deep learning technique used to create the “Up next” list by the platform from a paper written by some of the main Google developers in 2016 (Covington, Adams and Sargin). On the other side, we have various research groups trying to shed light on the company, such as, and we already have an idea of the different thematic groups actually present.

In general, we can say that algorithms are governing and curating personal content and news feed, on the contrary in traditional media content curation was done by human curators. One of the consequences that we can observe is that there is no restriction on the reinforcement of bad habits: if you like conspiracy videos, you will have more and more of them. That has happen because the system wasn’t designed to give you something good, “ it’s built to get you addicted to” it (Maack).

In this project, we used a browser extension developed as part of ALEX project. The tool is accessible to, also referred to as ytTREX. This approach allows us to record what YouTube offers to their consumers, and to study how the experiences become individualized by personalization algorithm.

2. Initial Data Sets

We built our dataset during the summer school, by taking the same sequence of steps collectively and in a synchronized way. We were coordinating 10-12 people per time by opening the same video in the same moment. Using the API implemented in ytTREX , we were able to download all the participants' observations.

We made one small experiment for each variable manipulated. That means that for every trial we have maximum 10 individual observation. Our dataset is not big enough to make inferences that we can generalize, but it was enough to compare how our observation makes our profiles diverge from an initial condition of relative anonymity to a secondary condition where personal activities are artificially made to let Google study our profiles.

3. Research Questions

On this basis, we formulated the first questions related to the research context.

How does the Youtube recommendation algorithm personalize its output?

Which variables play a role in the personalization process?

How can we empirically understand this personalization?

To what extent does informal the front-end analysis overlap with formal API back-end output?

In addition to those preliminary question, we were looking for different ways to represent graphically the personalization to be able to raise the awareness of the people regarding this issue.

In this spirit, we isolated different variables involved in the process, and we manipulated them in the minimum basic level, to show the big changes that follow the single act of giving bits of information to the platform. Which variable is playing a prominent role in the personalization process between language settings, the politicization of the content and user’s interaction with the platform? Which one is the most dangerous for its social and political implications?

4. Methodology


We tried different settings to be able to compare the variation on related videos. Our aim was to test all the main variables implicated in the personalization of content. We made it into a carefully synchronized team effort, to tried and test one variable at the time. The illustration above showcases our three principal levels of investigation:

1) Language through general setting and Google trackers (French and Korean).

2) The relevance of politicization: political vs a-political videos and Fox vs CNN.

3) Interaction with the platform: the time watched per video.

We started from the difference between a clean browser and our personal profile logged in. Then, we manipulated the information about the language changing the YouTube setting of “country” and “language”. In another attempt, we searched for 2 topics on Google and then we looked up the first three pages. We were divided into a French and Korean query language groups. After this experimental manipulation, the two groups watched the same video to control if the algorithm uses the Google tracker information to create the “Up next” list.

After the manipulation of the general settings, we moved to the type of content: we took a series of steps to see the differences between a political and an a-political video, watched with clean and logged browser. We also tested the diversity of related video network generated for two different users: the first one with just a Fox News video on its chronology, and the second with just a CNN video.

In the last part of the project, we modify the time of viewing the same political video. To do this we replicated the experiment of Fox-CNN, but this time we diversified two test condition by watching for 20 second or 2 minutes the same clip. We don’t know how exactly Youtube records this data, and for that reason we kept the interaction as basic as possible, avoiding any mouse movements. After 20 seconds or 2 minutes, we just closed the whole browser at the same time.

5. Findings

EXP 1: CLEAN vs PERSONAL browser. Can we graphically describe the YouTube personalization?


In the first image, we can see 10 different users (black spots) using a clean browser without personal information. The related video (azur spots) show us that the users are sharing almost the totality of the suggested video s.


In the second image we can see the same video watched by the same numbers of users, but with personal account logged in, therefore with all our personal data on it.

The commonly suggested videos are clearly less compared in the non-personalized results above. Note that the number of videos unique to one user is much more - personalization on practice.


To quantify the difference, we first derived the proportion and mean of videos shared on a clean browser and a personal browser. When we visualize the proportions, we can see that the number of videos users have in common is larger in a clean browser compared to the commonly recommended videos in a personal browser. When the personal browser is used, the long-tail of single observations is longer. Subsequently, we conducted a statistical test to see whether the difference of mean and variance of the proportion of shared recommendation is significant between the two groups. The results showed that the mean and variance between the groups differed significantly at the p-value of 0.001.

EXP 2: Recommended “for you” videos, POLITICAL vs A-POLITICAL. Does YouTube provide the same numbers of recommended “for you” contents on political and a-political issue?


The visualization above portrays the differences between the 'related videos' (in blue) and the videos 'recommended for you' (in yellow) while watching a-political content represented by a “cutest cat compilation” video. In the first trial (left) we used a clean browser, in the second, we used our personal YouTube account.

The percentage of personalized recommendations grows when the platform has more data on us. In this case, we have some explicitly “for you” in the clean browser, but they are a minority.


In this image, we can see the same test of before, but with a video clearly containing political content about Brexit with the title “Philip Hammond - do deal Brexit”. Here the differences between the clean and the personal browser set up are much more evident.

In this case, we have no “for you” videos at all. It seems that Youtube doesn’t want to suggest anything on this sensitive issue, to be sure to do not make mistakes.

When we move to our personal accounts to see the same video, almost half of the contents are recommended: “for you”. This observation does suggest that recommendations to politicized content are more heavily personalized than the recommendations to a-political content, but this claim is made on these two comparative observations only and unquestionably needs more research.

As said, to establish the reliability of this test we should have done a much bigger dataset. We can’t exclude that our personal profiles have more information about our political orientation. This might be why the numbers of the recommended videos increased. In the same way, we can not be sure that, with different trails on different videos, we won’t have at least some suggestions “for you” also in the clean browser, as happened on the first attempt.

EXP 3: API vs ytTREX. Can we observe differences between the list of related videos provided by YouTube and the one obtained with our direct experience?


In the third experiment we tried to compare the related videos list provided by API on a very popular video (‘Gangnam Style’) with the related videos that we can observe using ytTREX, differentiating again between personal and clean browser.

It seems that the list published by the platform overlaps with just a subset of common videos with a list that we can see directly. Can we put sufficient trust in data provides by Youtube? With a larger set of videos and with larger numbers of related video collected, we can try to answer this question, using ytTREX.

EXP 4: 20 seconds vs 2 minutes watch. Testing the influence of 'interactions' on personalisation.


We observed the different levels of personalization generated by watching the same video for either 20 seconds or 2 minutes. In this trial we divided ourselves into two different groups, the first one has seen a Fox News video and the other group saw one clip from the CNN channel. After this manipulation, we have seen the same political video about Brexit and we compared the related videos results.

As we can see in the graphs, watching Fox and CNN for 20 seconds create just three common suggestions specific for CNN watchers (the yellow nodes) and no one for the other group (blue spots). In fact, we can see a lot of videos suggested to both groups (grey spots) and some suggestions just or the single user (azur spots).

In the second trial, extending the watching time till 2 minutes, we can see eleven common suggestion to at least two CNN users, and six for Fox.


The difference between the two groups of related contents is significantly different, and we tested it using the ANOVA in R. Before running the test, we re-coded the sources according to their political views. Then we calculated the proportion of conservative videos shared, and the mean and variance. As seen in the bar graph, the means of the proportion of conservative videos shared in the Fox group is higher than the CNN group. The ANOVA test revealed that the difference in mean and variance between the two groups was significant at the p value of 0.001. That means that the algorithm suggests more conservative videos to the Fox users. The suggestions don’t seem to be related as much to the argument in the clip, but the more with the political orientation of the channel.

6. Discussion

O ur aim was to test the ytTREX tool because we were the first group to use it for research proposal. That’s why we wanted to have a trial for as many variables as possible to be able to create the “clean browser” methodology, indispensable for further research.

We have shown how different variables affect the personalization of contents. Graphic representation of the differences created by a really small modification of the information related to our “clean browser”, creates quite big differences in the related videos.

Some authors said that YouTube usually increases the visibility of inflammatory contents, and they focus their attention on the problem of the propagation of this kind of content also to the mainstream watchers (Rieder, Matamoros-Fernàndez, and Coromina). This research has been an attempt to provide empirical data to make claims about the personalisation mechanism present on Youtube. Opposed to more popular claims regarding the inherent polarisation of personalisation, this research and yTREX allow to start exploring and systematically scale the claims made.

We need to empower more systematic public inquiry into the effect of the algorithm across different topics. We have seen significant differences in the various research setups, in particular between political and a-political videos, but we need to go deeper in the analysis to have a more complete view.

To do this we need to be careful in the use of Yt API because it might not be reliable (Exp3), even if harvesting could be more time consuming, is the best way to make independent research.

Contrary to expectations, our minimal manipulation of the language settings through the visualization of 3 different pages on Google did not give us visible results. Changing the general Youtube language settings, we did notice some differences, but not really relevant. Some of the videos had translated titles, but the videos suggested were similar. Probably the company uses this general feature just to make the interface more accessible, and not to personalize the contents.

Even if the language does not seem to be relevant for the type of the videos suggested, the interactions with the platform are surely used to personalize user’s experiences, but we need to deeply understand how. The political perspective of a video watched is important to generate the suggestions “for you”, and this is the variable with more social implications to deepen.

In future research, we would like to further investigate the exact role of Google tracker’s to help inform Youtube's personalization algorithm.

7. Conclusion

We are aware that there are some challenges in our way. Now that we have a clean browser methodology, and we tested the tool in a research context, we need to focus on some analysis based on a bigger database and a deeper and more specific issue.

Surely, the most interesting difference is between political and a-political videos. The differences in personalization can be not relevant if the topic is “funny cat” for example. On the contrary, when we talk about politics, conspiracy, fake news, and similar, this supposedly neutral algorithm becomes part of the political life of our society.

We already know, by the admission of the developers [Convington], that the time watched per play is one of the most relevant factors used to create the suggested list. Actually, it is one of the best ways to understand the appreciation for a video and then to figure out if a suggestion has been effective or not. It’s important to discover how exactly YouTube records this data to be able to consider this variable in future studies. It’s possible that there are other types of interactions recorded by the platform like mouse dynamics, but we need further research to ascertain it with certainty.

8. References

Algorithms Exposed,


Covington, Paul, Jay Adams, and Emre Sargin “Deep Neural Networks for YouTube Recommendations”. Proceedings of the 10th ACM conference on recommender systems. ACM, 2016,

Lapowsky, Issie “YouTube Will Crack Down on Toxic Videos, But It Won't Be Easy.” Wired, 25 January 2019,

Maack, Már Másson “‘YouTube recommendations are toxic,’ says dev who worked on the algorithm.” The Next Web, June 2019,

Matsakis, Louise “YouTube Is Giving You More Control Over Video Recommendations ”Wired, 26 June 2019,

Rieder, Bernhard, Ariadna Matamoros-Fernández, and Òscar Coromina “From Ranking Algorithms to ‘Ranking Cultures’: Investigating the Modulation of Visibility in YouTube Search Results”. Convergence, vol. 24, no. 1, Feb. 2018, pp. 50–68, doi:10.1177/1354856517736982.

Other resources:

Rose “The Making of a YouTube Radical.” The New York Times, 8 June 2019,

Tufekci, Zeynep “Algorithms Won’t Fix What’s Wrong With YouTube.” The New York Times, 14 June 2019,

Tufekci, Zeynep “YouTube, the Great Radicalizer.” The New York Times, 10 March 2018,





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Topic revision: r3 - 12 Aug 2019, JedeVo
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