6th International Conference on Computational Social Science IC2 S2July 17-20, 2020 75 Amherst St. Cambridge, MA USA

The Effects of Disaggregated Factors of YouTube
Recommendations in Diversity
Pedro Ramaciotti Morales† , Bilel Benbouzid‡ , Emma Gauthier‡ , Alexis Perrier‡† médialab, Sciences Po‡ LISIS UMR 9003 CNRS, UPEM, Université Gustave Eiffel

Keywords: YouTube, algorithmic recommendations, diversity of recommendations, agent-basedmodels, consequences of recommender systems.
We are in the midsts of an active debate about the effects that algorithms have on the digital publicspace. As they filter, select, and influence what comes to our attention, the effects of algorithms arepolitical, social, cultural, as well of economic. This debate has also come to encompass important phenomena such as radicalization, and the so-called echo chambers and filter bubbles. Most interactions
between users and with content take place in a few platforms that account for a wealth of scientificworks. YouTube, an important content provider (the second most popular site as of 2019), has been
comparatively less investigated.
In this study we set out to identify how different relations between YouTube channels may affectthe diversity of content watched by users. We propose agent-based simulations to explore the consequences of privileging some of these relations (generated by explicit human interactions or computed
algorithmically) in recommendations. We consider a set of YouTube channels from a single country–in our case France– to better account for its representativeness within an informational ecosystem.
This set includes the channels of political parties and personalities, news outlets, youtubers, independent media, companies, and social movements that have the most subscribers, reaching nearly 1400
channels. We then proceed to establish relations between channels by consulting YouTube’s API andby web-crawling during Jan-May 2019. Four relations are considered: two of them (human) depending
on explicit user actions, and two of them (algorithmic) computed algorithmically by YouTube. These
relations are represented in four networks of channels: a) Featured network (human), in which channel A is linked to channel B if the administrator of channel A decided to include channel B in thefeatured section of channel A, b) CoCommented network (human), in which channel A is linked
to channel B if there is a significant number of users commenting videos of both channels, c) Recommended network (algorithmic), in which channel A is linked to a channel B if a video from B
was recommended in the lateral bar in a video from channel A, d) Related network (algorithmic), in
which channel A is linked to a channel B if it appears in the related section in channel B (this amounts
to channel recommendation and was discontinued in May 2019). In order to have a classification overwhich to measure diversity of video consumption in simulations, these channels were then classified
manually into one of 12 categories: political comment, complotism, militant groups, investigative
journalism, entertainment, grassroots movements, traditional media, local media, theme-specialized
media, conferences/interviews, public relations, and political vulgarization.
To study the effects of following hypothetical recommendations computed with each one of thesefour networks, we propose an agent-based simulation [2]. On each network we consider random
1

6th International Conference on Computational Social Science IC2 S2July 17-20, 2020 75 Amherst St. Cambridge, MA USA

walks of different lengths (to simulate different user engagement levels), and for each length we perform 10.000 simulations, storing the categories of channels visited during the walks. Then, for eachnetwork, and each length of walk we compute the diversity of categories visited, using Perplexity [3] (ameasure of uncertainty of visited channel category, closely related to Shannon entropy) to account for
both the number of categories and their apportionment. Crucially, we distinguish collective diversity(computed with the categories visited by all 10.000 agents) from the individual diversities (computedfor each agent) to separate the system-wide effects from those on individual watching experience.
One possible organization of the factors that influence actual recommendations in YouTube canbe organized in two groups: users’ characteristics (personalized, or user-centric) and videos’ characteristics (video-centric, or cold-start) [1]. Our agent-based simulations provide insights about thelatter. Simulations using Featured and CoCommented networks show what the effects would beof weighting more of the traces of human actions underlying these networks in the computation of
the video-centric part of recommendations. In comparison, simulations obtained using the Related
network use only algorithmic similarity computed by YouTube, and Recommended network serves
as baseline representing the effects that actual recommendations have on diversity.
In this work, we analyze the effects that these disaggregated factors have to show that including
more traces of human actions in cold-start recommendations could –depending on the mixing of userand video-centric factors– improve the diversity of contents watched by a collective of people, but
not necessarily the diversity experienced by individuals. We also suggest that both collective andindividual diversities resulting from actual recommendations are higher that those that would result
from recommending based purely in YouTube’s similarity. In our study we also address the algorithmic
visibility of the different categories of channels that result from giving more weight to factors here
identified. Finally, we discuss how network clusterizations compare between these four networks
when contrasted with our expert categorization to comment on the relation between the so-called filter
bubbles and contents offered by channels.

References[1] P. Covington, J. Adams, and E. Sargin. Deep neural networks for youtube recommendations. InProceedings of the 10th ACM conference on recommender systems, 2016.
[2] A. Dash, A. Mukherjee, and S. Ghosh. A network-centric framework for auditing recommendation
systems. In 2019-IEEE Conference on Computer Communications. IEEE, 2019.
[3] P. Ramaciotti Morales, L. Tabourier, S. Ung, and C. Prieur. Role of the website structure in thediversity of browsing behaviors. In Proceedings of the 30th ACM Conference on Hypertext andSocial Media, 2019.

2Perplexity (num. of categories)

6th International Conference on Computational Social Science IC2 S2July 17-20, 2020 75 Amherst St. Cambridge, MA USA

1010a) Featured network(human)10b) CoCommented network(human)

88

6

64

42

20 0 10 20 30 40 50 60 70 80

0 0 10 20 30 40 50 60 70 80c) Recommended network(algorithmic)d) Related network(algorithmic)

Length of random walk

Length of random walk10

Collective diversity
Median individual diversity
Ind. div. (0.25-075 quantiles)
Ind. div. (0.1-0.9 quantiles)
Ind. div. (min-max values)

886644220 0 10 20 30 40 50 60 70 80

0 0 10 20 30 40 50 60 70 80

Length of random walk

Length of random walk

Figure 1: Collective and individual category diversity (measured with Perplexity) of simulated agents(10.000 per length of browsing) randomly browsing YouTube channels following one of four relations
between channels encoded in four networks. Some relations lead to faster loss of collective diversity asusers adopt more recommendations. Weighting more human interaction factors in recommendations
can improve collective diversity but not necessarily individual diversity.

3