Collaborations on YouTube: From Unsupervised Detection to the Impact on Video
and Channel Popularity

Christian Koch, Moritz Lode, Denny Stohr, Amr Rizk, Ralf Steinmetz

Multimedia Communications Lab (KOM), Technische Universität Darmstadt, Germany

E-Mail: {Christian.Koch | Denny.Stohr | Amr.Rizk |<EMAIL_ADDRESS>
## Abstract

YouTube is one of the most popular platforms for streaming of user-generated
video. Nowadays, professional YouTubers are organized in so called multi-
channel networks (MCNs). These networks offer services such as brand deals,
equipment, and strategic advice in exchange for a share of the YouTubers’
revenue. A major strategy to gain more subscribers and, hence, revenue is
collaborating with other YouTubers. Yet, collaborations on YouTube have not
been studied in a detailed quantitative manner. This paper aims to close this
gap with the following contributions. First, we collect a YouTube dataset
covering video statistics over three months for 7,942 channels. Second, we
design a framework for collaboration detection given a previously unknown
number of persons featuring in YouTube videos. We denote this framework for
the analysis of collaborations in YouTube videos using a Deep Neural Network
(DNN) based approach as CATANA. Third, we analyze about 2.4 years of video
content and use CATANA to answer research questions providing guidance for
YouTubers and MCNs for efficient collaboration strategies. Thereby, we focus
on _(i)_ collaboration frequency and partner selectivity, _(ii)_ the influence
of MCNs on channel collaborations, _(iii)_ collaborating channel types, and
_(iv)_ the impact of collaborations on video and channel popularity. Our
results show that collaborations are in many cases significantly beneficial in
terms of viewers and newly attracted subscribers for both collaborating
channels, showing often more than 100% popularity growth compared with non-
collaboration videos.

##  1 Introduction & Problem Statement

Collaborations are a key strategy to increase the dissemination and, hence,
popularity of user-generated videos on YouTube. Here, YouTuber _A_ , i.e., a
content creator, introduces a collaborating YouTuber _B_ to his viewers
seeking to attract their interest to the content of YouTuber _B_. The
rationale here is that given the viewers interests, they are more likely to
watch the introduced YouTuber’s videos and eventually become subscribers [3,
4, 19]. Collaborations often occur in a reciprocal manner on both of the
collaborating channels to attract each other’s viewers and, thereby, increase
both YouTuber’s revenue. Video monetization can be activated through
participating in the YouTube Partner Program (YPP) [7]. Thereby, YouTubers can
configure their videos allowing YouTube to insert short ad clips before and
within their videos. In return, YouTube pays a share of the resulting revenues
out to the YouTubers. With more than 10k people that were reported in 2016,
the annual growth of the number of YouTubers earning six figures per year
amounts to $\sim 50\%$ [20].

In contrast to works describing collaborations, e.g., within scientific
communities or online social networks, there are only few studies on the
detection of collaborations in user-generated videos and a lack of
quantitative analysis of their desired impact on popularity. Note that Video-
on-Demand (VoD) platforms such as Netflix are not exposed to user-generated
content (UGC) which is more scattered, diverse, and generated at a much higher
rate. While general metadata of UGC videos is available, no explicit
information on collaborations exists. Although mentions of collaborating
YouTubers in the video title or in the description are in general possible,
they often miss collaboration-related information and are, hence, not a
reliable source of information.

Nowadays, popular YouTubers often belong to a multi-channel network (MCN),
e.g., BroadbandTV, Studio71, and Maker Studios, which offer equipment, brand
deals, and strategic advice to increase the YouTubers’ popularity in exchange
for a share of their revenue [7]. Essentially, such strategic advice comprises
collaboration policies that describe the most beneficial collaborations with
diverse YouTubers.

Until now, a public documentation on the efficiency of collaborations on
YouTube does not exist. A quantitative analysis of different types of
collaborations, e.g., between YouTubers of different popularity or content
categories has the potential to guide YouTubers to popularity- and, hence,
revenue-maximizing behavior. To this end, this article addresses the
challenging task of identifying and analyzing content creator collaborations
on large-scale UGC video platforms taking the example of YouTube. Our analysis
focuses on _(i)_ collaboration frequency and partner selectivity, _(ii)_ the
influence of MCNs on channel collaborations, _(iii)_ collaborating channel
types, and _(iv)_ the impact of collaborations on video and channel
popularity. In the following sections we address these focus points in a
framework for collaboration detection and analysis denoted CATANA. For the
design of CATANA, we argue that the analysis of content creator collaborations
on large-scale UGC video platforms faces three key challenges:

  * •

YouTube-suitable Face Detection: The YouTubers’ faces constitute the basis for
identifying collaborations. Hence, we require their inference and storage in
an appropriate representation. Changing lights, face expressions, and camera
perspectives add to the difficulty of deriving these representations.

  * •

YouTuber Identification: In contrast to most face recognition tasks, in our
scenario, an unknown number of people appearing in user-generated videos needs
to be identified based on the detected faces. Therefore, an accurate
association of face samples to people for a large set of YouTubers needs to be
obtained. Additionally, we need to identify which person is the owner of the
YouTube channel and which persons are potential guests.

  * •

Collaboration Representation: The co-occurrence of YouTubers in a video
indicates collaboration. However, people appearing sporadically, e.g.,
passersby do not carry strong evidence of collaborations. Filtering outliers
and storing inferred collaborations in an appropriate form is essential for
further analysis.

The remainder of this article is structured as follows. Section 2 provides an
overview of used benchmarking datasets, face recognition approaches, and
existing works on YouTube collaborations. In Section 3, we motivate and
explain CATANA’s major building blocks. Section 4 presents our data
acquisition and selection process. In Section 5, we evaluate and discuss our
results answering relevant research questions. We conclude our article in
Section 6 and discuss future work.

##  2 Related Work

The related work provides, first, an overview of existing datasets that are
useful for benchmarking face recognition methods on YouTube. Second, an
overview of recent face recognition methods is given. Third, existing works
addressing YouTube collaboration analysis are presented and discussed.

###  2.1 YouTube Face Recognition Datasets

In this section we give a brief overview of the two face datasets used for
optimizing CATANA. Both datasets are frequently used in face recognition
studies of the last years, as well as in related work.

##### Labeled Faces in the Wild (LFW)

LFW is a publicly available face dataset introduced in [8] and released as an
effort to spur face recognition research. It has been referenced in more than
50 papers related to face recognition [10]. LFW’s provides a large set of face
images in a large range of variations. This includes variation in pose,
lighting, expression, background, ethnicity, and age [9]. The dataset consists
of 13,244 images of 5,749 people with images in 250x250 pixel JPEG format.
These face images were collected from the web and contain celebrities,
politicians, athletes, and other public figures. For training, validation, and
testing, mutually exclusive splits are available suited for usage in a 10-fold
cross validation. Training and testing data are, thereby, presented as either
matching or mismatching face pairs. Hence, the proposed evaluation experiments
aim at the problem of pair matching, deciding whether the images are of the
same person. In total 6,000 pairs are provided, divided into 10 splits with
each 300 match and 300 mismatch pairs.

##### YouTube Faces (YTF)

YTF is a dataset of face videos introduced in [18] and designed for studying
the problem of face recognition in videos. The dataset contains 3,425 videos
of 1,595 different people appearing in YouTube videos. These people are a
subset of the 5,749 people from the LFW dataset and for every person an
average of 2.15 videos are available. The video duration ranges between 48 and
6,070 frames and all video frames are stored individually as JPEG images.
Structure and design of YTF is heavily inspired by LFW. Similarly, matching
and mismatching pair are provided, for usage in a 10-fold cross validation
pair-matching evaluation. In comparison to the LFW evaluation, pairs consist
of videos, allowing to decide if the pair of videos is subject of the same
person or not. Overall, 5,000 pairs are provided, divided into 10 splits, each
containing 250 match and 250 mismatched pairs.

###  2.2 Face Recognition

Two kinds of fundamentally different face recognition approaches exist. The
first kind that is widely-spread relies on fixed mathematical models and
structures, e.g., Eigenfaces[15] and Local Binary Patterns Histograms (LBPH)
[1]. While these classical approaches have shown solid performance, the second
kind of techniques, using deep neural networks (DNNs) is outperforming
classical approaches and beginning to replace them. As they are clearly
superior in performance to classical approaches and even compared with humans
[10], we focus only on DNN-based approaches.

Parkhi et al. [11] present a deep learning-based face recognition method
called VGG-Face. The authors further propose how a very large-scale face
dataset can be assembled by a combination of automation and human in the loop.
Focusing on celebrities for the dataset acquisition, they extract a small
number of images using the Google Image Search and the actors’ names. Then,
these images are audited through human annotators. In the next step, the
actors are queried again through image search, this time extracting a bigger
amount of images. Instead of human annotation, a linear Support Vector Machine
(SVM) is pre-trained with the small number of images from the first step and
leveraged to classify the newly acquired images. Thereby, a dataset of 2,622
identities and 2 million images is assembled. Furthermore, a CNN
(Convolutional Neural Network) based face recognition method is proposed,
which is trained on the acquired dataset using the _triplet-loss function_.
The proposed method is then evaluated on the LFW and YTF datasets and compared
against other state-of-the-art methods, resulting in an accuracy of 98.95% for
LFW and 97.3% for YTF. We conclude that image acquisition through online image
search can be a promising method to acquire training data. In the context of
this work, channel names could be queried, resulting in face images of
YouTubers. However, this would still require a human interaction for a
potentially large number of YouTubers and passersby in videos.

Schroff et al. [13] present a DNN model named FaceNet. They introduce the
_triplet loss_ for training, i.e., a loss function designed to result in face
representations, clustered based on similarity. Triplet loss thereby works
with triplets of matching / non-matching face images and embeds these into a
128-dimensional Euclidean space. Thereby, the loss function ensures that an
anchor image of a specific person is closer to all other positive images of
the same person, than it is to any negative image from any other person. Thus,
the network results in a 128-dimensional face representation in a space where
distances directly correspond to face similarity. Consequently, classification
and clustering are now straightforward by using standard Euclidean distance
metrics. Hence, accuracies of 99.63% on the LFW dataset and 95.1% on YTF were
measured. Even though similar in architecture than the previous DNN [11], a
significantly larger private dataset of 200M images was used for training.

Amos et al. [2] present OpenFace111http://cmusatyalab.github.io/openface
[Accessed: ], a CNN-based face detection and recognition framework using only
openly available training datasets for training. OpenFace builds up on the
FaceNet [13] architecture, combining it with computer vision and machine
learning techniques, such as State Vector Machine (SVM) for classification.
For training the CNN model, public face datasets were used, which licenses
allow usage and publication of the resulting models. Hence, no training is
necessary for using the OpenFace framework. OpenFace is thereby trained with
only 500,000 images from combining the two largest labeled face recognition
datasets for research, CASIA-WebFace222http://www.cbsr.ia.ac.cn/english/CASIA-
WebFace-Database.html [Accessed: ] and
FaceScrub333http://vintage.winklerbros.net/facescrub.html [Accessed: ].
Overall, OpenFace achieves an accuracy of 92.92% on LFW.

A related framework to OpenFace is
Facenet444https://github.com/davidsandberg/facenet [Accessed: ], as both are
based on FaceNet [13] and its proposed DNN. Even though Facenet shares almost
the same name as FaceNet [13], its developer have no connection to the
authors. In comparison to OpenFace, Facenet additionally implements multiple
additional ideas from different papers to tune accuracy. Amongst others, a
different loss function is used for training, the face representation is
1792-dimensional instead of 128-dimensional and, furthermore, a different face
detection technique is used [21]. Facenet implements the _center loss_
function [16], which is developed to improve the discriminative power of the
learned features, minimizing the intra-class variation while keeping the
classes separable. To do so, it learns a center for each feature class and
penalizes distances between features and their center, resulting in high
accuracy. Facenet is trained using the MS-Celeb-1M face dataset [6], whose
license allows model reproducibility. Facenet is evaluated on the LFW dataset,
showing an accuracy of 99.2%.

We conclude that DNN-based approaches show high accuracy on relevant datasets
such as LFW and YTF. As Facenet evidently outperforms the other reviewed
approaches, we will deploy it as a key component for CATANA’s face recognition
capabilities.

###  2.3 Collaborations on YouTube

Mattias Holmbom [7] conducted a study on five YouTube channels, including
interviews with their content creators. He found that it is currently more
difficult than ever for new content creators to establish a YouTube channel,
as already a large number of channels exist. Concerning collaborations on
YouTube, three of the five YouTubers associate their popularity directly or
indirectly with collaborations featuring other channels. Furthermore, MCNs
were suggested as tool to get help in finding other YouTubers addressing
similar topics for collaboration.

In Brendan Gahan’s article "How to be successful on YouTube: The 3 steps" [3],
the third proposed step for new content creators is collaboration with other
YouTubers, which already have an established subscriber base as an essential
step to expand a channel’s audience. YouTube’s official Creator
Academy555YouTube website offering free online courses helping users creating
better videos and improve channel performance. also suggests collaboration as
a powerful way to reach new users [19]. This is also confirmed from MCNs’
side, as the MCN "Channel Frederator Network" recommends to facilitate and
instigate collaborations between network members [4]. We conclude that
analyzing channels registered in the same network can significantly improve
the likelihood detecting collaborations. This is important for our work to
control the comparisons of videos with and without collaborations.

Bertram Gugel created a visualization of a share of the YouTube collaboration
graph [5]. The visualized graph provides insights into (i) the size of the
respective YouTube channels, measured by subscriptions, (ii) whether the
association is uni- or bidirectional, as well as, (iii) to which MCN the
channels belong. However, only the _Featured Channel List_ of a YouTube
channel is used to determine a collaboration. On the one hand, this deliberate
association between YouTube channels does not necessarily indicate that
channels collaborate. On the other hand, there exists also collaborations with
not featured channels. Furthermore, this work does not provide a thorough
analysis of individual videos and the effect of collaboration on their
popularity. Hence, we conclude that to the best of our knowledge no
comprehensive analysis on YouTube collaboration and resulting effects on
popularity exists so far. We set this as the goal of this article.

##  3 System Design

In the related work, we have seen that it is common practice to recommend
YouTubers to collaborate to increase their audience and, hence, revenue.
However, there is a lack of precise and detailed analysis of the impact of
YouTube collaborations on video and channel popularity. With CATANA, we aim to
close this gap. In the following, we motivate CATANA’s major building blocks,
depicted in Figure 1. As our focus is on video and channel popularity, we need
the _Metadata Crawler_ which collects popularity time series, i.e.,
information of view counts in the case of videos and of the subscriber counts
in the case of channels. These time series are stored in the _Database
Storage_. Before analyzing the videos, the _Video Downloader_ acquires the
video data. In a next step, the contained faces of the videos are detected and
a dense representation is computed and stored in the _Database Storage_ ,
together with the video ID allowing to associate the contained faces with the
videos’ popularity time series. Then, the _Clustering_ module determines face-
person associations by associating similar face representations with the same
person. The most representative face representations per person are stored in
the _Database Storage_. The _Collaboration Detection_ module compares face
representations of different videos, thereby allowing to find persons
appearing in different videos, as these videos contain the same face
representations, i.e., the same persons. In a last step, the collaborations
detected are stored in form of a bidirectional graph used in the _Analysis &
Evaluation_ module. In the following, we discuss the key processes and design
choices of CATANA in detail.

Figure 1: CATANA’s system architecture.

###  3.1 Face Detection and Recognition

We evaluated six recent approaches (ref. Section 2.2) to choose an appropriate
face recognition method. Eigenfaces[15] uses principal component analysis
(PCA) to transform a high-dimensional face image to a lower-dimensional
representation, while Local Binary Patterns Histograms (LBPH) [1] are
histogram-based. In contrast to these traditional well-known methods, we also
evaluate recent DNN-based approaches: Facenet, Openface, FaceNet, and VGG. We
compare the performance of these approaches with two benchmark datasets: LFW
and YTF (ref. Section 2.2). Both datasets provide pairs of matching and non-
matching faces useful to assess face recognition methods. The results are
presented in Table 1, depicting the accuracy of the pairwise match/mismatch
between subject pairs in a 10-fold cross-validation. As Facenet performs best
on both datasets, and even outperforms humans, we choose it for CATANA’s face
detection and face recognition functionalities.

Table 1: Face recognition performance comparison with stDev, non-public are
grayed out.

| LFW | YTF
---|---|---
Eigenfaces |  0.6002 $\pm$ 0.00791 | -
LBPH |  0.6782 $\pm$ 0.6300 | -
Facenet |  0.9930 $\pm$ 0.0042 |  0.9980 $\pm$ 0.00134
Openface |  0.9292 $\pm$ 0.01343 |  0.9971 $\pm$ 0.00264
Human |  0.97531 | -
FaceNet | 0.9963 $\pm$ 0.0009 | 0.9512 $\pm$ 0.39
VGG | 0.9913 | 0.9740

  * 1

[9]

  * 2

[2]

  * 3

[12]

  * 4

this article

### Frame Extraction and Selection

Typically, face recognition approaches are designed for images, not for video.
Due to the large number of frames per second with usually small changes, it is
not efficient to process every frame. Instead, we use a duration-based frame
extraction rate $f(n,r)$ taking the number of frames $n$ in the video and its
frame rate $r$ as inputs to reduce the number of images to process. We extract
evenly spaced frames with a usual rate of 10 frames per minute. For short
video, we adapt the rate to extract at least $f_{\text{min}}=600$ frames which
shows a good performance. For long videos, the number of frames extracted is
limited for storage reasons to $f_{\text{max}}$=8,000. One might argue that
the frame extraction approach used is quite simple and could benefit, e.g.,
from shot boundary detection or face tracking. Therefore, we additionally
tested a sophisticated approach, that does not analyze individual frames but
face tracks, i.e., the face is detected once and followed through the
subsequent frames. Therefore, we used Pyannote-
Video666https://github.com/pyannote/pyannote-video [Accessed: ] as a framework
to consider a face tracking approach as well in our design. This framework is
based on the face recognition framework Openface [2] and implements shot
boundary detection, face track extraction, and clustering via hierarchical
agglomerative clustering.

###  3.2 YouTuber Identification

In the previous section, we discussed how a set of face images per video is
obtained using Facenet. In a following step, we use clustering to create an
association between images and people. In the case of face tracks, these are
already grouped per individual. However, due to shot boundaries, i.e., scene
or camera switches, multiple face tracks per individual may exist.

In a next step, we evaluate five different clustering approaches: Pyannote-
video in two configurations, DBSCAN, HDBSCAN, and agglomerative clustering
(AGG). Pyannote-video can be configured with different frame extraction and
face detection rates. Therefore, two settings are chosen: _C1_ : Every frame
is used for face tracking and 1 frame per second is used for face recognition.
_C2_ : Only 1 frame per second is extracted as well as used for face
recognition. To assess the performance, we evaluate metrics for error rate,
number of not clustered images, number of clusters found, and the clustering
runtime.

Figure 2 depicts the evaluation results of the approaches based on the
clustering error and number of not clustered face images. In Figure 3, we show
the evaluation results based on the number of identified clusters and the
clustering runtime. We can see that Pyannote-Video C1 has a high number of
erroneously clustered face images. Furthermore, clustering run-time is
multiple times higher for C1 than the video duration, and also multiple times
higher than required by the other approaches. The second configuration C2
performs better than C1, but still worse than the other approaches regarding
clustering errors and clustering runtime. Comparing the two similar approaches
DBSCAN and HDBSCAN a difference in noise handling, measured by the number of
not clustered face images can be noticed. DBSCAN identifies more images as
noise than HDBSCAN and all other approaches. The number of found clusters fits
for both density-based approaches: DBSCAN and HDBSCAN, being close to the
optimal number. The major advantage of both density-based approaches, i.e.,
DBSCAN and HDBSCAN over the other approaches is that no estimation of the
number of cluster must be given. Finally, we conclude that both DBSCAN and
HDBSCAN perform best amongst the evaluated techniques. The face tracking
approach, while promising in theory, did not perform well. As the number of
not clustered face images suggests, DBSCAN handles noise more conservative
than HDBSCAN, resulting in possible useful images being discarded. A
significant advantage of HDBSCAN over DBSCAN, besides handling varying cluster
density, is that it returns probabilities describing the strength of
membership for every face image. This information can be leveraged to further
filter outliers and improve the clustering results. _For these reasons we
decided to use HDBSCAN for clustering the face images._

While evaluating the performance measures above, we noticed non-compliant
behavior of HDBSCAN for class one videos, especially in which no other
(external) actor appeared. HDBSCAN thereby fails to create a single cluster
and labels all data as noise. This observed effect is due to the hierarchical
approach and it could not be improved through parameter modification. As
DBSCAN performs well on a single cluster case, we use DBSCAN as a fallback
solution, coming into effect when HDBSCAN fails to detect a single cluster.

Figure 2: (Left) Number of clustering errors. (Right) Number of not clustered
face images. Figure 3: (Left) Number of clusters found compared to reference
(optimal). (Right) clustering run-time comparison.

An alternative approach to clustering is classification. However, this
approach was discarded, as it does not fit our requirement that the face
recognition technique should be able to distinguish between individuals
without prior training data. For the sake of completeness, we will give a
brief overview of this alternative approach. Through classification, it is
possible to assign detected faces to known individuals. The problem with this
approach in our setting is that training data is needed for classification. As
we have channel information, which is analyzed beforehand, we could acquire
training images through, for example, Google image search. The drawback with
this approach is that the number of content creators per channel is unknown.
Thus, the automatic retrieval of correct training data for multiple persons
through image search is not viable. Furthermore, appearing individuals that
are not related to any analyzed channel would not be recognized. For these
reasons, the previously described clustering approach is utilized,
incorporating both, HDBSCAN and DBSCAN.

###  3.3 Collaboration Detection

We define a collaboration as the co-occurrence of a YouTuber from a different
channel in a YouTuber’s video, e.g., in a video showing both YouTubers or
playing a (potentially) prerecorded clip of the featured YouTuber. To identify
collaborations, we build connections between videos using the previously
derived face clusters. Single videos may have multiple face clusters, i.e., at
least one per detected individual. Therefore, we compute a similarity matrix
between face clusters using the Euclidean distance as a similarity measure. In
a next step, the similarity matrix is taken as an input for HDBSCAN clustering
which groups all face clusters of individual persons based on the similarity
measure. This gives us face cluster-wise connections showing that the same
person appeared in all connected videos. This information allows the
investigation of collaborations between different channels.

Although we can now connect the appearing individuals, we do not know yet
which of the persons is a content creator, i.e., owner of the channel, or an
external actor appearing. To determine the content creator of a channel, we
can select the face cluster with the highest number of appearances on the
channel. However, this approach would be unable to assign multiple content
creators to a single channel. As do not want to restrict this method to single
content creator channels and we assume that some portion of the available
channels already have multiple content creator, we use the following approach.
To decide if an individual is a content creator, we leverage the number of
appearances per individual and channel. In detail, a person may have appeared
in different channels, we assign the person as a content creator for the
channel with the highest number of appearances. Thereby, a channel with one or
multiple content creators can be correctly detected.

Figure 4: Sample graph of PewDiePie’s YouTube channel and 1-hop
neighbors888Channel images taken from https://www.youtube.com/{PewDiePie,
chadwildclay, channel/UCEYLdM2bdhmw-TS3c0TjFNw, KickThePj, TheBigManTyrone,
MrMccruddenmichael, user/LandonProduction, HerrNewstime, iwantmylauren,
iOTrendz, channel/UCIq3bpW-MaAzj4Y2G9ezPhA, channel/UCBINYCmwE29fBXCpUI8DgTA,
channel/UCm3GpkVRonpt2BHrt3GhwjQ, comicbookresources, JoeCroninSHOW,
channel/UCZApDB9BW7ZjNcPb3Wu7rRg, imsannachanel, TokyoAtomic, CutiePieMarzia}

#### Collaboration Graph

Inspired by [5, 17], the collaborations between channels are modeled as a
graph with channels as nodes and collaborations as directed edges connecting
the nodes. The edge direction describes that a content creator of the origin
channel appeared, i.e., collaborated, in one or more videos of the destination
channel. Footnote 8 depicts an example graph showing one the world’s most
popular YouTubers: PewDiePie. The edge label denotes how often collaborations
between the two channels were observed. We use a graph to visualize and model
the collaborations as we can apply different graph algorithms to analyze the
underlying channel relations.

Table 2: Overview of the available crawler tools, ∗are outdated | Scrapy | YTCrawl | YOUStatAnalyzer | HarVis | ytdata∗ | TubeKit∗
---|---|---|---|---|---|---
Customizable | Highly | limited | limited | limited | limited | limited
Documentation | Extensive | sparse | sparse | sparse | sparse | sparse
Storage | Customizable | File | MongoDB | SQL | SQLite | MySQL
API usage | Yes | No | Yes | Yes | Yes | Yes
Language | Python | Python | Python | Java | Python | PHP

##  4 YouTube Statistics Data Acquisition

In the following, we briefly describe how we crawl the required information
from YouTube and specify how we select appropriate YouTube channels for
crawling our dataset.

###  4.1 YouTube Data Crawling

To assess the effect of YouTuber collaborations, we need to acquire channel
metadata such as the subscriber count and video view counts. Additionally, we
also want to acquire the video and channel metadata, such as video titles,
video and channel descriptions, and the channels’ featured channel list. The
crawled data is not filtered by a specific video topic but through a seed set
of YouTube channels and their uploaded videos. Under those requirements most
of the existing tools such as YTCrawl999https://github.com/yuhonglin/YTCrawl
[Accessed: ],
YOUStatAnalyzer101010https://github.com/mattiazeni/youstatanalyzer [Accessed:
], or HarVis111111https://github.com/DrUzair/HarVis [Accessed: ] cannot be
leveraged for our purpose without significant code changes. Customizing the
frameworks requires a solid documentation, which most of the tools do not
provide or only in sparse form. To this end, we decide to develop a YouTube
data crawling tool based on the Scrapy121212https://scrapy.org [Accessed: ]
framework which is highly customizable and provides extensive documentation,
that allows to implement all requirements of our envisioned crawler in
reasonable time and with a comparable small effort. Scrapy is widely used and
has a big open source community. Table 2 shows a qualitative comparison of the
considered YouTube data crawling tools, including Scrappy.

Figure 5: Populate spider architecture, d is the user-defined maximum depth
for channel recursion.

Next, we describe the design of the Scrapy-based crawling architecture. As we
distinguish between continuous and static data, we implemented two spiders,
i.e., crawling modules in Scrapy. The first spider, which we denote as
_Populate Spider_ , crawls static data and populates a database with channel
and video entries, e.g., the video and the channel name. Figure 5 illustrates
the crawling process. The populate spider takes an initial set of channel IDs
for which it requests all static data and subsequently creates a database
entry for every crawled channel. Note that for fixed set of channels $\bm{d}$
is set to zero to hinder Scrapy from adding additional channels to the set.

In the next step, a second spider denoted _Daily Spider_ crawls continuous
data created by the channels and videos already known in a daily manner, i.e.,
view count and subscriber count. Here, each channel’s _Upload Playlist_ is
crawled, which contains the videos uploaded by a channel to identify newly
uploaded videos. Crawling the popularity statistics for all videos uploaded on
the given channels allows constructing time series of popularity statistics.
We record all interactions on the monitored channels such as view, comment,
and subscriber counts in the time span between 28.12.2016 and 28.03.2017 on a
daily basis.

Figure 6: Daily spider architecture.

###  4.2 Data Selection and Representation

The _Populate Spider_ needs an initial set of YouTube channel IDs. To create a
qualitative and large dataset of channels, we crawl three of the most popular
MCNs [14] as channels associated to a MCN have higher chances to collaborate
[4]. These MCNs are described in table 3. We used the website
SocialBlade131313https://socialblade.com/youtube/top/networks/most-subscribed
[Accessed: ] to derive the mapping of channels to MCN member lists. Here, we
take a random sample of $1.5\times 10^{3}$ channels for every MCN in which the
100 channels with most subscribers per MCN are included. In a next step, we
use the so called _Featured Channel List_ of the crawled channels, which
contains other channels defined by the channel owner to express an
acquaintanceship between his and other channels. This relationship is modeled
in a graph representation as described in section 3.3, containing roughly 44k
channels. Here, channels are represented by nodes, while the featured channel
list comprises unidirectional edges from one node to other nodes.

Table 3: Most popular YouTube MCNs worldwide, measured by number of subscribers. Name | Members | Subscribers (over 30 days) | Views (over 30 days)
---|---|---|---
BroadbandTV | 237,235 | 82,718,601 | 20,107,687,476
Studio71 | 13,194 | 18,535,751 | 5,402,265,290
Maker Studios | 9,460 | 14,011,532 | 4,613,091,2598

Finally, non-mutual edges between channels are removed, while mutual edges,
i.e., nodes having each other in their _Featured Channel List_ , are
considered likely to collaborate and, hence, are kept. We also excluded Gaming
videos as we observed a lack of face presence in these videos and,
furthermore, often high resolution game figures depicted on covers or within
the videos would have led to increases imprecision. From the remaining
subgraph, we extract the largest connected component, resulting in a graph
with roughly 8k nodes and about 10k edges indicating potential collaborations.
By applying CATANA to this subgraph, i.e., analyzing 2.4 years of video
uploaded on these channels for a period of three months, we identified 1,599
nodes and 1,728 edges representing actual collaborations. The edge sum, which
corresponds to the overall number of collaborations sums up to 3,925, see
Table 4.

Table 4: Collaborations observed in the three month’s time span. Collaborations | Duration | Mean | Median | 75-percentile | Max
---|---|---|---|---|---
3,925 | 3 months | 2.8 | 1.0 | 3.0 | 134

##  5 Evaluation

In this section, we formulate and answer research questions with respect to
the focus points of: _(i)_ collaboration frequency and partner selectivity
(Section 5.1), _(ii)_ the influence of multi-channel networks (MCNs) on
channel collaborations (Section 5.2), _(iii)_ collaborating channel types
(Section 5.3), and _(iv)_ the impact of collaborations on video and channel
popularity (Section 5.4).

###  5.1 How often do collaborations happen and reoccur with the same partner?

(a) Number of pair-wise collaborations

(b) Number of collaborations per channel

Figure 7: Histograms of detected collaborations per channel pairs (a) and per
channel (b)

Using the collaboration graphs, we can easily determine the overall number of
collaborations by summing up the edge weights between two YouTube channels.
Analyzing the edge weights allows us to examine the number of repeated
collaborations. Table 4 depicts the derived collaboration statistics where we
observe that the distribution is skewed to the right, indicating the presence
of a few channel pairs with a comparably high number of collaborations. We
confirm this by drawing the histogram of the collaboration counts in Figure
7a, showing the distribution of repeated collaborations between two YouTubers
who have collaborated at least one. We deduce that over a 3-months observation
period collaborations between two channels rarely happen more than once.

In a next step, we analyze the number of collaborations per channel instead of
distinct channel pairs. Therefore, we sum up the edge weights for every
channel node. Taking the perspective of a channel, we differentiate between
collaborations taking place in own videos (internal) and videos of other
channels (external). If a collaboration between YouTuber A and B occurs on
YouTuber A’s channel, it is considered as an internal collaboration by A and
as an external collaboration by B. This distinction helps us later to detail
the effects on both sides of a collaboration. Figure 7b shows the
corresponding distributions. We conclude that a small number of highly
collaborating channels denoted _central channels_ exists, with most of the
remaining channels having very few collaborations. Thus, central channels show
a high in-degree, demonstrating a key _influencer_ role on YouTube. These
YouTubers are especially valuable for product placements, advertisements, and,
hence, are especially valuable assets for their MCNs.

(a) Collaborations per uploaded videos

(b) Channel collaborations

Figure 8: Collaboration/Video ratio and separated in- and outgoing
collaborations.

One weakness of the former analysis is that it compares results based on the
absolute number of collaborations. Thus, we investigate the relative ratio of
a YouTuber’s collaborations compared to the overall number of the channel
uploads. Given the number of internal collaborations of a channel $k$,
denoting collaborations only occurring in its own videos, and the number of
videos of the channel $n$, we calculate the collaboration ratio as $k/n$.
Figure 8a shows the distribution of the collaboration ratio. Values around one
imply that in nearly every video of the channel, a collaboration is found.
This may indicate that certain content creators regularly work together, or
share a common channel while also operating separate channels alone. Values
larger than one can occur if multiple collaborations were detected in a single
video. In Figure 8b, we observe only a few outliers with a ratio above one,
while most of the channels have a ratio close to zero. This indicates that a
large portion of channels have only a single ingoing collaboration.
Summarizing our finding with respect to collaboration frequency we observe
that a single channel collaborates with other channels on average 2.8 times
($[2.5-3.15]$ at a 99% confidence level). If channels collaborate, we find in
our 3-month dataset that they repeat their collaboration on average 2.3 times
($[2.0-2.6]$ at a 99% confidence level). Overall, the distributions for
collaboration metrics are skewed as a consequence of a few highly influential
YouTubers.

###  5.2 How do multi-channel networks (MCNs) influence channel
collaborations?

To answer this question, we first analyze which collaborations take place
between MCNs. To this end, we first determine the respective MCN for each
channel using publicly available information141414https://socialblade.com/
[Accessed: ] to augment the channel information of our collaboration graph.
Figure 9 depicts the most collaborating MCNs in form of a MCN-collaboration
matrix. Overall, we found 405 MCN pairs collaborating with each other. Note
that the entry _None_ refers to channels for which we could not determine a
MCN association.

Figure 9: Absolute number of collaborations within and between MCNs. Figure
10: Internal and external MCN collaborations.

Examining the diagonal of the matrix in Figure 9 we observe a distinct trend
showing that most collaborations occur within MCNs, and thus between their
members. Further, we observe that significant collaborations between networks
are mainly confined to the three dominant networks, namely, BroadbandTV,
Studio71, and Maker Studios. We find many collaborations of unassociated
channels, i.e., with the label _None_ , with the three dominant networks,
which we attribute to the fact that they are the world’s three largest MCNs
and, hence, have YouTube channels associated which are popular and an
attractive target for collaboration. Furthermore, famous YouTubers are a
popular topic for other YouTuber’s that may show the popular YouTuber’s face
or video sequences. We deduce that belonging to a MCN strongly increases the
probability of a YouTuber to collaborate.

Figure 10 shows the percentage share of collaborations separated by MCN in-
and external collaborations. Here, we observe that the three largest MCNs,
i.e., BroadbandTV, Studio71, and Maker Studios as well as PranksNetwork have
much more internal collaborations compared with the smaller MCNs. Hence, their
YouTubers collaborate more in their own videos than on the videos of other
MCN’s YouTubers. Note that a large portion of the outgoing collaborations is
with channels that are not associated with a MCN. Channels not belonging to a
MCN show a preference to work with MCN-associated channels as more than 70
percent of their collaborations are outgoing. We conclude that an influence of
MCNs concerning YouTuber collaborations can be inferred. In summary, we find
that channels associated with a MCN collaborate more often with each other and
if collaborations occur outside the MCN, then they are usually with non-
associated channels and rarely with other MCNs’ YouTubers.

###  5.3 Which channel types collaborate?

Table 5: Popularity class definitions and their number of observed channels. Popularity Class | Subscriber Range | #Channels
---|---|---
0 | $[0,10^{3})$ | 813
1 | $[10^{3},10^{4})$ | 1,575
2 | $[10^{4},10^{5})$ | 2,569
3 | $[10^{5},10^{6})$ | 2,420
4 | $[10^{6},10^{7})$ | 544
5 | $[10^{7},5x10^{7})$ | 20
6 | $[5x10^{7},10^{8})$ | 1

In the following, we group YouTube channels with respect to popularity and
content category. First, we assign each channel to one out of seven popularity
classes, based on their subscriber count. Here, the chosen classes resemble
the classes used for the YouTube
awards151515https://www.youtube.com/yt/creators/rewards.html [Accessed: ],
which are awards shipped to the YouTubers when they exceed a certain number of
subscribers. Next, we analyze collaboration behavior regarding the YouTube
video category, a label which the YouTuber can select out of a set of given
categories during the video upload process.

##### YouTube Popularity Classes

We assign a channel’s popularity class with respect to the number of
subscribers as depicted in Table 5. In column _#Channels_ , the table shows
also the number of YouTube channels in our dataset which belong to the
corresponding popularity class. We can see that the major share of the
channels observed belong to popularity class 1, 2, or 3. Class 6 is an
exception, as it only contains a single channel, i.e., _PewDiePie_ , the most
successful YouTuber in terms of subscribers so far. Figure 11a depicts the
share of observed channel collaborations between popularity classes. Here,
channels belonging to a numerically higher class have more subscribers than
channels belonging to numerically smaller classes. The matrix entry
$a_{\text{t}\text{f}}$ of row $f$ and column $t$ denotes that the number of
YouTubers _from_ a channel of popularity class $f$ appear in videos belonging
to channels of popularity class $t$. We can see that most collaborations
happen within class 3 and neighboring classes 2 and 4, which we ascribe to two
factors. First, these channels have reached a popularity in the YouTube
environment that attracts collaborations. Second, these channels do not yet
belong to the most popular channels, i.e., categories 4, 5, and 6 but are
likely to try to increase their own popularity by attracting more viewers
through collaborations with other YouTubers.

(a) Popularity classes.

(b) Video categories.

Figure 11: Collaborations within & between popularity classes and YouTube
video categories (in %).

##### YouTube Categories

In Figure 11b we show the share of collaborations between YouTube categories.
Most collaborations are detected in, and between the _Entertainment_ and
_People & Blogs_ categories, which is reasonable as they prevalently contain
human presence and interaction. The same applies for categories like _Comedy_.
In the figure, we observe asymmetric relations, e.g., between _Comedy_ and
_Film & Animation_, that collaborate more in with _Entertainment_ channels
than within the same category. For collaborations within a category, i.e., the
diagonal of the heat map, we only notice a surge for _Entertainment_. We
observe that the most frequent collaborations occurred within the category
_Entertainment_ , which also shows the second most video uploads. Note that
_Entertainment_ is a rather generic term and can therefore, depending on the
YouTuber’s interpretation, also include comedy, film, and animation related
content.

###  5.4 How do collaborations impact video and channel popularity?

We investigate the two parts of this question separately, focusing, first, on
the observed effects on video popularity and, second, on channel popularity.
Therefore, we use popularity statistics of a 3-months period for roughly
$10^{5}$ videos, considering videos for which we have at least 12 daily
popularity measurements, i.e, videos being older than 12 days. We chose 12
days as we observed that most older videos do not receive much more views.
Using CATANA, we deduce a collaboration graph with roughly $10^{4}$ edges
indicating collaborations within the observed $8\times 10^{3}$ channels. Using
two sets for collaboration and non-collaboration videos, we analyze the
maximum values of view and subscriber counts of the 12-days time-span and
their gradient. The benefit of these gradients is that they are not biased by
absolute numbers but represent the relative popularity growth.

####  5.4.1 Video Popularity

First, we examine the maximum video view counts observed in the first 12 days.
Figure 12 shows the video view count distribution. By examining the left side
of the figure, we see in the box plot that the average view count is higher
for videos with a collaboration compared with non-collaborations. Note that
the median is stretched nearly doubled (from about 26k to 45k views) for the
cases of present collaborations. Although the median is only slightly higher
in case of a collaboration, the upper 50% of video views are more scattered
and show more views. Additionally, we plotted the average view counts for both
cases and their 95% confidence intervals on the right side of Figure 12 where
we see a large gap. The figure suggests that a significantly higher view count
can be expected if a video contains a collaboration.

Figure 12: Number of views after 12 days for collab. (green) and non-collab.
(blue) videos. Figure 13: View count growth factor _between_ collaboration and
non-collaborations videos. A positive value indicates the superiority of
collaborations.

The generally higher popularity of collaboration videos can be reasoned in the
generally higher popularity of collaborating channels. As we previously
examined, class 3 channels collaborate more often than channels of lower
popularity classes. If we therefore assume that more videos with
collaborations are uploaded by class 3 channels, the videos consequently tend
to a higher popularity compared for example with class 2 videos. To further
substantiate this effect, we evaluate the gradient and growth of the video
view counts. Hence, we calculate the average view growth factor between non-
collaboration and collaboration videos. For this, we use the average maximal
12 day view value for every channel, differentiated by collaboration and non-
collaboration. Next, the percentaged growth from the non-collaboration value
to the collaboration value is calculated channel-wise. Figure 13 displays
these results. As only channel with both collaboration and non-collaboration
data could be used for the percentaged growth calculation, samples of 1,116
channels are used. The difference in number of channels is due to channel
which only collaborate in external videos, and do not host collaboration in
their own. Statistics on the differences between the two video groups on the
video view growth are shown in table 6.

Table 6: View growth of collaborations compared to non-collaborations. Channels | Duration | Mean | Median | 75-percentile | Min | Max
---|---|---|---|---|---|---
1116 | 3 months | 34.32 | -6.73 | 32.47 | -99.70 | 6,376.28

In a next step, we investigate the distributions of these view count
differences depicted in Figure 13. Here, the 0.95 confidence interval is
between 19% and 51%, indicating that a significant growth of the views between
collaboration and non-collaboration videos can be expected.

Next, we compute the gradients between each pair of the 12 days of the videos’
view counts, resulting in 11 gradient values. Additionally, we calculate the
percentaged growth between these values. The percentaged growth of the first 6
days after a collaboration vs. non-collaboration view growth on the first day
is depicted by Figure 14. This figure shows the longer lasting temporal impact
of collaborations. Further, Figure 15 shows the box and bar plots of the
gradient values, i.e. the absolute view count increase, using a 0.99
confidence level.

Figure 14: Video view count growth for the first five days of a collaboration
vs. the non-collaboration view growth on the first day. Figure 15: Video view
count gradient.

####  5.4.2 Channel Popularity

Here, we cannot directly differentiate between collaboration and non-
collaboration channels as for videos, since one channel may contain both,
collaboration and non-collaboration videos. Therefore, we filter channels,
which uploaded videos with and without collaborations, resulting in 1,599
channels. The channel popularity is measured by the number of views of its
videos and the number of channel subscribers. We define a window of two days
after a collaboration, for which we will classify the subscriber counts as
belonging to a collaboration, the remaining statistics measured are classified
as belonging to non-collaboration. Thereby, we gathered 9,086 channel
subscriber measurements for collaborations, and 78,877 for non-collaborations.
Figure 16 shows the subscriber count differentiated between collaborations and
non-collaborations. From the figure we conclude that collaborations have a
slight positive effect on channel subscribers. Compared to the increase of
viewers, the increase of subscribers is only about one tenth of total users.
Though, the relative growth compared to non-collaborating video measures is
about 30% larger in terms of newly attracted subscribers compared with the
newly attracted viewers.

Figure 16: Channel subscriber gradient and percentaged growth.

In addition to the above evaluation of the two-day collaboration window, we
evaluate a 6-day window on a daily basis. Figure 17a shows the percentaged
subscriber growth over 6 days starting with day 0, which is the upload date of
the collaboration video. Figure 17b depicts the percentaged subscriber growth
for each day and on the left side and the respective overall channel view
growth on the right side. On both sides, also the growth of non-collaboration
videos after day 0 is shown to allow for a comparison. We note that the
highest subscriber growth can be observed for days 0 and 1 with the growth
slowly decreasing to approach the base line describing non-collaborations. In
addition to the channel subscriber growth, we apply the same evaluation
methodology for the overall channel views, an alternative measure of channel
popularity. Here, we expect a similar pattern as for the channel subscriber
counts but we observe a very different pattern, depicted in Figure 17b. For
day zero and one, the view growth is quite low and close to non-collaboration
videos. In contrast to that, for day 2, we observe a significant increase.
Despite the fact that more people watch collaboration videos on day two, we
found that users seem to be less engaged as for day 2 overall less
subscriptions are observed than for days 0 and 1.

(a) Channel subscriber count growth.

(b) Channel view count growth.

Figure 17: Effect of collaboration over the first six days. Figure 18:
Subscriber growth for collaborations between YouTube channel categories.
Figure 19: View count growth for collaborations between YouTube channel
categories. Figure 20: Subscriber growth for collaborations between channel
popularity classes. The x-axis denotes the depicted popularity class in the
constellation depicted in brackets, e.g., (1 in 3) means that a YouTuber of
popularity class 1 appeared in a video published on a popularity class 3
channel. Figure 21: View count growth for collaborations between YouTube
channel popularity classes.

###  5.5 Impact of Video Popularity Classes and Video Categories

##### YouTube Categories

Figure 18 and 19 show the impact of a collaboration of YouTubers belonging to
different video categories and popularity classes. We observe that view and
subscriber counts vary strongly amongst different channels. Hence, we compute
the relative popularity growth for videos with and without collaborations.
Here, we took only popularity measurements if a collaboration video was
uploaded and at the same day no other video was uploaded to guarantee that the
channel popularity measure is not impaired. In case different categories
collaborate, we show the effects for both categories separately. For all
collaborations taking place between YouTubers uploading mostly videos of the
same category, a significant increase of subscriber and view count is
observed. Here, the category _People & Blogs_ is an exception, as on average
more subscribers can be attracted by a collaboration but less video viewers.
This is still beneficial as the subscribers are potentially watching all
videos uploaded by the YouTubers in the future.

##### YouTube Popularity Classes

In Figure 20 and 21 the impact of collaborations between channels of different
popularity classes is shown. In general, we observe a significant benefit of
collaborations. Note that lower popularity classes, especially class 1 and 2
YouTubers benefit significantly stronger than higher popularity classes. It
can be seen that for classes 3 and 4, the gain of a collaboration is
comparably low and often there is no significant difference. For class 4
YouTubers, no effect of collaborations can be observed. Hence, we deduce that
especially for YouTubers with less than $10^{5}$ subscribers, i.e., class 1 or
2, collaborations significantly increase the number of views.

Concluding our popularity evaluation for videos and channels under
collaborations, we state we observe that collaborations have positive effects
on video views and on engaging new subscribers. Especially for YouTubers of
low popularity classes such as class 1, a collaboration can add about 100%
additional views and new channel subscribers. Concerning the impact on video
views, we calculated a percentaged growth through collaborations with a mean
between 19% and 51% with 0.99 confidence. Also channels popularity is
significantly increased through collaborations, i.e., for both considered
metrics, namely channel subscribers and total channel views.

##  6 Conclusion and Future Work

In this article, we first designed and implemented a system for the
acquisition and analysis of collaboration data in user generated content at
the example of YouTube. We implemented a video-based face recognition system
named CATANA for which we examined and evaluated different face recognition
and clustering techniques. We applied CATANA to a collected dataset of videos
over a 3-month period where we extracted appearing content creators and
leveraged this information to detect collaborations between channels.

We observed that out of 7,492 channels in our dataset 1,599 collaborated, with
an average of 2.8 times per channel. Regarding the types of channels, which
collaborate we found that channels with a subscriber count between $10^{5}$ \-
$10^{6}$ collaborate the most and the _Entertainment_ YouTube category shows
most collaborations. Furthermore, we inferred that multi-channel networks
generally exhibit collaborations within the same network or channels, which
are not associated with any other, potentially competing, network. We analyzed
the acquired popularity statistics for both videos and channels and found
significant differences between collaboration and non-collaboration sets,
indicating a positive effect on the popularity that is measured by subscriber
and view counts. In this work we proposed a viable method for collaboration
detection in user generated content that is based on face recognition. A
potential limitation of the proposed system is the differentiation with
respect to the collaboration context, which can be mapped to different types
of face appearances, i.e. posters or content usage. In future work, using
voice recognition in addition to face recognition is likely to increase
detection rates as well as differentiate collaboration context.

## Reproducibility

To enable other researchers to use, reproduce, and extend our research, we
release our tool chain at https://github.com/christiannkoch/CATANA. This
contains: (i) the CATANA framework and evaluation scripts used here, (ii) the
3-months collaboration graph used in this paper, and (iii) an interactive web-
based visualization of the collaboration graph.

## Acknowledgements

This work has been funded in parts by the DFG as part of the Collaborative
Research Centre 1053 MAKI (C3, B4).

## References

  * [1] T. Ahonen, A. Hadid, and M. Pietikainen.  Face Description with Local Binary Patterns: Application to Face Recognition.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 2006.
  * [2] B. Amos, B. Ludwiczuk, and M. Satyanarayanan.  OpenFace: A general-purpose Face Recognition Library with Mobile Applications.  Technical report, CMU-CS-16-118, CMU School of Computer Science, 2016\.
  * [3] B. Gahan.  How to be successful on YouTube: The 3 steps.  http://thenextweb.com/insider/2015/03/04/the-3-steps-to-success-on-youtube/, 2015\.  [Accessed: ].
  * [4] M. Gielen.  Best Practices For A YouTube Multi-Channel Network 2.0.  http://www.tubefilter.com/2015/03/24/youtube-mcn-multi-channel-network/, 2015\.  [Accessed: ].
  * [5] B. Gugel.  YouTube Universum. Die Vernetzung der YouTuber visualisiert.  http://www.gugelproductions.de/blog/2015/youtube-universum-die-vernetzung-der-youtuber-visualisiert.html, 2015\.  [Accessed: ].
  * [6] Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao.  Ms-celeb-1m: A Dataset and Benchmark for large-scale Face Recognition.  In European Conference on Computer Vision, pages 87–102. Springer, 2016.
  * [7] M. Holmbom.  The YouTuber A Qualitative Study of Popular Content Creators.  Bachelor thesis, Umea University, 2015.
  * [8] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller.  Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments.  Technical report, University of Massachusetts, Amherst, 2007.
  * [9] E. Learned-Miller, G. B. Huang, A. R. Chowdhury, H. Li, and G. Hua.  Labeled Faces in the Wild Website.  http://vis-www.cs.umass.edu/lfw/.  [Accessed: ].
  * [10] E. Learned-Miller, G. B. Huang, A. RoyChowdhury, H. Li, and G. Hua.  Labeled Faces in the Wild: A Survey.  In Advances in Face Detection and Facial Image Analysis, pages 189–248. Springer, 2016.
  * [11] O. M. Parkhi, A. Vedaldi, and A. Zisserman.  Deep Face Recognition.  In Proceedings of the British Machine Vision Conference (BMVC), 2015\.
  * [12] D. Sandberg.  Facenet Project Repository.  https://github.com/davidsandberg/facenet.  [Accessed: ].
  * [13] F. Schroff, D. Kalenichenko, and J. Philbin.  FaceNet : A Unified Embedding for Face Recognition and Clustering.  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
  * [14] Social Blade LLC.  SocialBlade Network Statistics.  https://socialblade.com/youtube/top/networks.  [Accessed: ].
  * [15] M. Turk and A. Pentland.  Eigenfaces for recognition.  volume 3, pages 71–86. MIT Press, 1991.
  * [16] Y. Wen, K. Zhang, Z. Li, and Y. Qiao.  A Discriminative Feature Learning Approach for Deep Face Recognition.  2016\.
  * [17] C. Wilson, B. Boe, A. Sala, K. P. N. Puttaswamy, and B. Y. Zhao.  User Interactions in Social Networks and Their Implications.  In ACM European Conference on Computer Systems, EuroSys’09, 2009\.
  * [18] L. Wolf, T. Hassner, and I. Maoz.  Face Recognition in Unconstrained Videos with Matched Background Similarity.  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
  * [19] YouTube, LLC.  Youtube Creator Academy: Collaboration.  https://creatoracademy.youtube.com/page/lesson/collaboration.  [Accessed: ].
  * [20] YouTube, LLC.  YouTube Statistics.  https://www.youtube.com/yt/press/statistics.html.  [Accessed: ].
  * [21] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao.  MTCNN Face Detection.  https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html.  [Accessed: ].