# Doing data science with platforms crumbs: an investigation into fakes views
on YouTube

Maria Castaldo  Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-lab,
F-38000 Grenoble, France  Corrisponding Author<EMAIL_ADDRESS>Paolo Frasca  Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-lab,
F-38000 Grenoble, France  Tommaso Venturini  CIS, CNRS, 59 Rue Pouchet, 75017
Paris, France  University of Geneva, Switzerland  Floriana Gargiulo  Gemass,
CNRS, 59 Rue Pouchet, 75017 Paris, France

###### Abstract

This paper contributes to the ongoing discussions on the scholarly access to
social media data, discussing a case where this access is barred despite its
value for understanding and countering online disinformation and despite the
absence of privacy or copyright issues. Our study concerns YouTube’s
engagement metrics and, more specifically, the way in which the platform
removes "fake views" (i.e., views considered as artificial or illegitimate by
the platform). Working with one and a half year of data extracted from a
thousand French YouTube channels, we show the massive extent of this
phenomenon, which concerns the large majority of the channels and more than
half the videos in our corpus. Our analysis indicates that most fakes news are
corrected relatively late in the life of the videos and that the final view
counts of the videos are not independent from the fake views they received. We
discuss the potential harm that delays in corrections could produce in content
diffusion: by inflating views counts, illegitimate views could make a video
appear more popular than it is and unwarrantedly encourage its human and
algorithmic recommendation. Unfortunately, we cannot offer a definitive
assessment of this phenomenon, because YouTube provides no information on fake
views in its API or interface. This paper is, therefore, also a call for
greater transparency by YouTube and other online platforms about information
that can have crucial implications for the quality of online public debate.

##  1 Introduction

### Fake views, real trends

"We want to make sure that videos are viewed by actual humans and not computer
programs"[25]. As stated in its official web pages, YouTube has a strong view
count policy and has not been afraid of enforcing it. In December 2012, the
platform deleted 2 billion views from the channels of record companies such as
Universal and Sony [22] [26] [2] [19]. Over the years, countless youtubers
have suffered sudden and drastic cuts to their views (and many have complained
about it, often through YouTube videos). According to YouTube’s policies [24]
[35] [25], these interventions aim at preserving a “meaningful human
interaction on the platform” and to oppose “anything that artificially
increases the number of views, likes, comments or other metric either through
the use of automatic systems or by serving up videos to unsuspecting viewers”
[24].

Despite the media interest in the phenomenon [27] [38], not much research has
been carried out on the implementation of this policy. The general lack of
studies on the subject is partly motivated by the fact that since 2016 YouTube
has largely restricted access to its data through the API, making the
researchers’ task more difficult. Up to our knowledge, the only previous work
concerning views count corrections is that of Marciel et al.[32] in 2016. This
paper studies the phenomenon of views corrections in relation to video
monetization, to identify possible frauds, drawing on research carried out on
ads frauds in other social media [14] [34]. In their work, Marciel et al.
created some sample YouTube channels and inflated their views though bots.
Strikingly, they found that “YouTube monetizes (almost) all the fake views”
generated by the authors, while it “detects them more accurately when videos
are not monetized”.

Although we consider this investigation into the correlation between
monetization and views correction a first useful step toward understanding
YouTube’s policy, we believe that some other pressing questions should be
addressed by the scientific community. For instance, can fake views have an
impact on the success of a video and be used to manipulate YouTube’s attention
cycle? It is well known that, on social media, future visibility is highly
dependent on past popularity, as trending contents tend to be favored by human
influencers [42] and recommendation algorithms [23], both of which are highly
sensitive to trendiness metrics [49]. In YouTube in particular, the
recommendation engine represents the most important source of views [56] and,
as admitted by its developers, “in addition to the first-order effect of
simply recommending new videos that users want to watch, [has] a critical
secondary phenomenon of bootstrapping and propagating viral content” [18].
Quite deliberately, YouTube’s algorithm creates a positive feedback that skews
visibility according to a rich-get-richer dynamic [5] [36] [46]. As
acknowledged by YouTube engineers: “models trained using data generated from
the current system will be biased, causing a feedback loop effect. How to
effectively and efficiently learn to reduce such biases is an open question”
[55].

This is where fake views come into play. Indeed, if the correction of
illegitimate views happens too late, these views have the potential to weight
in the cycle of trendiness [12] and unfairly propel their targets. If YouTube
fake views correction is significantly slower than its recommendation
dynamics, then artificially promoted videos risk to be favored by human and
algorithmic recommendations, and thus reach larger audiences and collect extra
real views. If, before being deleted, fake views are able to trigger a cascade
effect that increases the visibility of some content, then they may be used to
manipulate online debate. Not unlike social bots [21] and paid commentators
[28], fake views could give the false impression that some content is highly
popular and endorsed by many, thus distorting public debate and ultimately
endangering democratic processes [54] [39] [33] [4] [31] [3] [41] [30].

### Approach and tentative results

While much research has been dedicated to identifying social bots [40] [7]
[44] [48] and analyzing artificially produced contents [50] [20] [52] [8] [51]
[16] [17] [45] [29], no attention has been devoted to the role played by fake
views in content diffusion. This work addresses this open and urgent question
by analysing YouTube data at an unprecedented temporal granularity. As
detailed below, we collected two datasets from more than a thousand French
‘politics and news’ channels. The first dataset is a 17 month collection of
the hourly views counts of over 270.000 videos. The second dataset collects
the views evolution of a thousand videos, but does so every five minutes.
These datasets and their combined analysis allow us to examine, for the first
time, the timing of YouTube fake views corrections and to rise concern about
their consequences.

In summary, our analysis has lead to three main findings. Our first finding
concerns the remarkable size of the phenomenon, since we identify fake views
corrections in almost all the channels of our larger corpus and more than a
half of the videos. Our second finding is that the rhythms of fake views
corrections are inconsistent with those of view production, thereby suggesting
that significant delays could occur between the generation of fake views and
their correction. Our third finding is the existence of a correlation between
fake and real views: videos with more fake views also collect a larger number
of legitimate views.

###  1.1 Disclaimer

Despite our best efforts, our analysis cannot provide a definitive proof of
the influence of fake views, because YouTube offers very little temporal
information about fake and real views. At any given moment, we can retrieve
the total number of views collected until then by a video, but not the history
of their evolution – forcing us to collect the views count at regular
intervals. Even more frustrating, through its interface or API, YouTube offer
no information about fake views removal. This information, it is worth
pointing out, does not raise any particular security or privacy issue – if the
real views count is revealed, why not the fake views one? The fake views count
would provide a crucial cue for fact-checkers, journalists and scholars to
identify possible shady operations around (although not necessarily by) a
video or a channel, and yet YouTube decides to hide this information, which it
could easily make available. As many of its likes, the platform share its data
only to the extent that they help its business model. Like it or not, however,
online platforms have become more than commercial players and the influence
they have acquired on public debate should bind them to greater transparency.
We wish we could relegate our methodological troubles to a footnote and focus
on the results. Unfortunately, YouTube does not make this possible. This paper
thus is also a way to share with the research community a few of workarounds
we developed to study attention cycles despite YouTube’s opacity and in
particular a method to reconstruct fake views count through frequent
monitoring and machine learning techniques.

##  2 Results

Our results are mainly based on the analysis of a data set which records hour
by hour and for 17 months the number of the views collected by 270.133 videos
published by 1.064 French channels dealing with news and politics. The
limitations imposed by YouTube API made it impossible to collect data more
frequently (or to monitor videos more than a week after their publication).
The hourly frequency, of course, leads to a significant loss of information.
As no direct information is provided about views corrections, we can only
estimate them by observing the hours with a negative delta in the views count,
i.e. the hours in which a video loses more views than it gains. In the hours
in which the number of new views exceeds the number of removed views, the
platform’s interventions become invisible. For this reason, we did not draw
our conclusions from the direct analysis of this dataset: instead, we have
developed a machine learning method to discover these hidden correction. This
method is described in details in the Data and Methods section.

In the Data and Methods section, we first give a quantitative estimation of
the amount of information lost due to the hourly frequency of collection, in
comparison with a 5-minute frequency. Then, we propose two methods to
reconstruct the number of YouTube corrections (a Benchmark Method, based on an
heuristic procedure, and the Reconstruction model, a machine learning
algorithm) and we compare their performance. The Reconstruction model,
allowing to better reduce the information loss, is finally applied on all the
original time series to reconstruct the number of views removed by YouTube but
not directly visible in the raw data set. In the rest of this section, we
present the results obtained from the analysis of the data reconstructed in
this way.

Figure 1: A: 5 sample videos and their hourly evolution of views. B: The 20
most viewed channels in our dataset and their fraction of corrections over
real views. C: The most corrected channels in terms of fraction of corrections
over real views. D: Lorenz curve of the distribution of views and corrections
among different videos E: Percentage of videos concerned by the policy.

###  2.1 Scale of the phenomenon

The removal of false views is evident when the series of hourly views has
negative entries: some examples are shown in Figure 1A. In fact, we have found
out that fake views corrections are extremely common: we detected corrections
for almost all monitored channels (90% of them) and for 61% of the videos in
our corpus. Such a large scope underscores the importance of better
understanding how these corrections are made. In fact, corrections in our
corpus amount to about 22.5 millions. Although they represent, on average, a
seemingly modest 0.5 percent of the total views, their number remains
impressive and, more importantly, their distribution is very uneven. If we
look at the Lorenz curve (Figure 1D) of the distribution of corrections among
different videos, we can see that most of the corrections (more than 80%) are
concentrated on only 20% of the videos. In comparison, the concentration of
corrections appears to be stronger than that of legitimate views.

The heterogeneity of corrections is confirmed when we examine the most popular
and the most corrected videos. Figure 1B shows the 20 most popular channels in
our dataset and the percentage of actual views that corrections account for.
These very popular channels, which are mainly traditional media channels such
as TV stations, newspapers, and radio stations, still show marked differences
in their corrections (between 0.1 percent and 1.3 percent). In contrast, if we
look at the 20 channels with the highest fraction of corrections to actual
views (Figure 1C), we find channels with 40 percent corrections. These
channels, which are mostly platform-native youtubers, collect substantially
fewer views than the top channels (averaging about half a million views over
the collection period).

###  2.2 Correction rhythms

The view correction activities by YouTube have some interesting recurrences.
If we look at Figure 2B, we can see how corrections are distributed according
to the time of day. We can see, for example, that while the median number of
removed views hovers around a few dozen at most hours, it rises to more than
10,000 at 5 p.m. and hovers around 5,000 at 4 and 6 p.m. This observation is
confirmed if we study the number of videos corrected at each hour of the day
(Figure 2C). Again, while normally the median number of corrected videos is
close to zero, between 4 p.m. and 6 p.m. it is around 150 to 250. These
rhythms of correction are peculiar and completely different from the rhythms
at which views are made on the platform. In fact, if we look at Figure 2C, we
can see how the views are distributed at different times of the day: they
present a completely different behavior, with a minimum of hourly views around
7 a.m. and peaks of views in the evening or night hours (9 p.m. to 2 a.m.).

In summary, views are distributed quite evenly during the day, according to
circadian rhythms [13], while corrections are concentrated in specific time
slots. This fact suggests that most correction activities take place once a
day, every 24 hours. This rhythm not only is unrelated to the rhythms of views
production, but also seems rather slow given the fast pace at which content is
propagated on the platform and the speed at which suggestions from
recommendation systems are updated. For comparison, most of the videos in
YouTube’s "trending" section111https://www.youtube.com/feed/trending are less
than 24 hours old.222After collecting the top 20 videos in the trendy section
in 7 different days, we found out that only the 25% have been published since
more than a day. Therefore it is legitimate to ask whether such a frequency of
corrections is too low to prevent possible interference of non-legit views
with human and algorithmic recommendation.

If instead of looking at the distribution by time of day we study how
corrections and views are distributed starting from publication, another
interesting result emerges. In Figure 2D, on the x-axis, we considered a time
starting from midnight prior to the publication of a video. For each hour
starting from midnight prior to publication, we summed the number of views,
corrections, and corrected videos (with the convention of considering these
quantities zero prior to publication). The expedient of measuring time from
the midnight before publication allows us to maintain the periodicity of the
corrections phenomenon, which would be lost if we summed the time series from
their time of publication. What can be seen in this graph (normalized to make
corrections and views comparable) is that views are much more concentrated in
the first few hours after a video is published, while corrections are more
spread out over the life of the videos. In fact, most views occur before 5
p.m. on the second day, the time when substantial corrections begin to occur.
This apparent delay in the corrections, together with their low frequency,
prompt us to investigate more deeply the relationship between corrections and
popularity.

Figure 2: A: Distribution of the number of corrected videos per hour of the
day. B: Distribution of corrections per hour of the day. C: Distribution of
views per hour of the day. D: Normalized number of corrected videos,
corrections and views since midnight before publication.

###  2.3 Late corrections and popularity

To gain a deeper insight into correction mechanisms and investigate possible
interference with the recommendation system, we should consider the timing of
corrections not only in absolute terms, as done in the previous section, but
also relative to when videos collect the most views. In particular, it is
crucial to ask whether corrections are made before or after a video reaches
its peak popularity. If corrections occur after this peak, one might suspect
that by inflating the number of views, illegitimate views could make a video
appear more popular and unfairly boost its human and algorithmic
recommendation.

Figure 2(left) presents the percentage of corrections made before videos reach
a certain percentage of real views. For each video, we calculated the
percentage of views collected at each hour. We then counted the number of fake
views that occur before a certain percentage of real views. As the graph
shows, most of the illegitimate views are corrected after the videos have
garnered most of the real views. On average, only about 10 percent of the
corrections are made before the videos have achieved 80 percent of the views.
Even more striking is the fact that as many as 54 percent of corrections are
made after the videos have stopped collecting real views, at the end of their
lives. It is therefore quite clear that most illegitimate views are removed
very late in the lives of most videos, well after their popularity has peaked
and begun to decline.

Figure 3: Left: Fraction of corrections occurring after different percentages
of real views. Right: Correlation between real and fake views per channel.

In order to understand whether these delays have an effect on video
popularity, we need to better investigate the relationship between fake views
and popularity. In Figure 3(right) we examine the relationship between the
number of fake views and the total number of legitimate views per channel. In
principle, these two quantities should be independent: in fact, if YouTube’s
correction policy were sufficiently fast and efficient, illegitimate views
should have no impact on real views and, at the same time, there is no
apparent reason why more popular videos should attract more fake views than
others. Yet, Figure 3(right) shows a strong linear correlation between the
logarithms of the two quantities. With a R-square equal to 0.819, the
logarithms of fake and real views are related by a linear regression with
intercept equal to 1.7704 and slope equal to 1.0574. The p-values associated
with these quantities, as the reader might expect by looking at the plot, are
very low, specifically less than 0.001. This linear correlation between the
logarithms of the two quantities results in the following relationship between
corrections ($c$) and real views ($v$):

$c=58.94\cdot v^{1.06}$

The relationship between these two quantities is hence slightly more than
linear, with a $95\%$ confidence interval on the exponent being within
$[1.026,\;1.089]$.

In view of this clear correlation, it becomes natural to wonder about a
possible causal relationship between fake and real views. In particular, we
would like to know whether fake views have an impact on popularity or not.
Unfortunately, with the information we have, we are unable to answer this
question. Although we have been able to reconstruct the time series of the
removal of fake views, we are unable to gather any information on the timing
of generation of these fake views. Without knowing when fake views are made,
we cannot in any way know whether they are recorded before or after the videos
become popular, and thus we cannot investigate the causal order of the two
phenomena. This information is not provided by the platform in any form and we
hence cannot investigate the causal relationship between fake views and
popularity.

##  3 Data and Methods

###  3.1 Data

For our analysis, as described above, we used a large dataset with hourly
frequency, which we will refer to as the hourly dataset. We also collected a
smaller dataset with 5 minute frequency, which we refer to as the 5 minute
dataset: its purpose is to assess how much information was lost by collecting
the time series with hourly frequency and to test our methods to reconstruct
the lost information. The details of the two datasets are presented next.

#### Hourly dataset

Starting from January 2021, through a collaboration with the Qatar Computing
Research Institute (QCRI), we collected the time evolution of views of any
video published by a list of over a thousand French YouTube channels dealing
with news and politics. This dataset is particularly interesting as it cannot
be straightforwardly obtained through the official YouTube application
programming interface (API) [1]. Indeed for the latest several years the
YouTube API [1] has restricted the accessible engagement metrics of a video to
the current values only, without providing their temporal evolution any
longer: this restriction complicates temporal studies on development and
prediction of online attention and indeed studies on the temporal evolution of
engagement metrics have not been carried out since 2014 [46] [36].

The dataset covers 1064 popular French channels that we deemed influential in
the French public debate. These channels, with their description, are
available at [11]. The channels have been selected through a qualitative
analysis of the French YouTube, aiming to identify relevant actors that
diffuse political opinions through the platform. The selected channels belong
to the following categories: local and national media; influential YouTubers
discussing political topics; militant associations; politicians; political
parties; Yellow Vests groups; associations devoted to public causes; large
public or private institutions. YouTube provides no information about the
location from which videos are viewed, but since the channels of our corpus
focus on French public debate, we can assume most of their viewers to be based
in France.

For these channels, we recorded hour by hour the evolution of views of each
video published after the 1st of January 2021, for an entire week after
publication (170 hours). Between the 1st of January 2021 and the 10 of May
2022, we collected the views time series of 270.133 videos. The dataset is
available at [10] (resource IDs have been mapped to anonymize values
consistently along the dataset). The choice of collecting only one week of
views evolution is justified by noticing that news channels often collect the
majority of their views in few days after publication, presenting a strong
initial burst followed by a power-law decay [53]. Indeed our data confirm this
fast decrease of users engagement as only $3\%$ of views is obtained in the
last 24 hours. The choice to collect views every hour was dictated by the
constraints on YouTube’s API, which does not allow more than 10,000 requests
per day with the same access key. As a result, wanting to monitor the French
news and political sphere on YouTube, and hence a certain amount of videos
every day, we were forced to collect the data with this frequency.

#### 5-minutes dataset

To assess the amount of information lost by collecting data every hour, we
collected a smaller dataset with a 5 minutes frequency. This frequency has
been chosen so to minimize the information loss and is in practice the highest
useful frequency of data collection, since we have empirically observed that
view counts are updated no faster than every 5 minutes. This dataset contains
1012 videos posted between February 2, 2022 and February 16, 2022. The dataset
is available at [9]. Video identifiers have been anonymized consistently with
the hourly dataset.

###  3.2 Method to estimate corrections

To reduce the loss of information due to collecting data on an hourly basis,
we have devised a method to reconstruct the original corrections and
interventions. To remove any bias possibly caused by the hourly aggregation,
we need to infer the real corrections $c_{h}^{i}$ made by the platform at hour
$h$ on video $i$ from the observable quantity
$\tilde{v}_{h}^{i}=v_{h}^{i}-c_{h}^{i}$, where $v_{h}^{i}$ are the effective
non-observable views collected by video $i$ during hour $h$, and $c_{h}^{i}$
are the effective corrections made by the platform. In order to quantify the
performance of our reconstruction methods we use the corrections and
interventions visible in the 5-minute dataset as the best proxy for the real
corrections $c_{h}^{i}$. In general, we would like the estimated values
$\hat{c}_{h}^{i}$ to reduce the following errors:

  * •

the fraction of lost corrections, consisting of the fraction of real
corrections that are lost in the reconstructed series;

$\frac{\sum_{h,i:c_{h}^{i}>\hat{c}_{h}^{i}}(c_{h}^{i}-\hat{c}_{h}^{i})}{\sum_{h,i}(c_{h}^{i})}$

  * •

the fraction the added corrections, consisting of the corrections mis-added by
the reconstruction methods, divided by the total real corrections;

$\frac{\sum_{h,i:\hat{c}_{h}^{i}>c_{h}^{i}}(\hat{c}_{h}^{i}-c_{h}^{i})}{\sum_{h,i}(c_{h}^{i})}$

  * •

the fraction of lost interventions, consisting of the fraction of
interventions no longer visible in reconstructed series;

$\frac{\sum_{h,i:c_{h}^{i}=0}\mymathbb{1}_{\hat{c}_{h}^{i}>0}}{\sum_{h,i}\mymathbb{1}_{c_{h}^{i}>0}}$

  * •

the fraction of added interventions, consisting of the number of mis-added
interventions by reconstruction methods, divided by the total number of real
interventions.

$\frac{\sum_{h,i:\hat{c}_{h}^{i}=0}\mymathbb{1}_{c_{h}^{i}>0}}{\sum_{h,i}\mymathbb{1}_{c_{h}^{i}>0}}$

When approximating the corrections with the negative views visible in the
hourly collection, i.e. by taking
$c_{h}^{i}=-\tilde{v}_{h}^{i}\mymathbb{1}_{\\{v_{h}^{i}>0\\}}$, we obtain the
errors shown in Table 1. As the table shows, the loss of information is far
from being negligible. We loose 66.31% of the corrections and 60% of the
interventions.

In the following we present two methods to reduce this information loss. The
first, which we will refer to as the benchmark method, is simpler and uses a
heuristic. The second method, which we will refer to as the reconstruction
method, uses an XGBoost classifier [15] to improve the benchmark method and
reduce information loss in the reconstructed series.

| Hourly Aggregation | Benchmark Method | Reconstruction Method
---|---|---|---
Lost Corrections | 66.31% | 50.27 % | 35.64%
Added Corrections | 0% | 1.63% | 4.93%
Lost Interventions | 60.00% | 60.00 % | 45.27%
Added Interventions | 0% | 0 % | 1.31%
Table 1: Validation of Reconstruction Method on the 5-minute-frequency
dataset. The table shows the loss of information with hourly aggregation and
with the estimates done with the proposed Reconstruction Method and the
Benchmark method.

### Benchmark Method

As a benchmark method we propose the following heuristic: in hours with
negative views, corrections are approximated by the number of negative views
increased by the expected views at that hour. To estimate the expected views
in a certain hour, we considered various quantities, such as the average and
minimum number of views over a time window around the given hour. The time
window considered are shown in Figure 4 and they should be intended
symmetrical, e.g. a window size of two hour means that both the two hours
preceding and following the hour of interest have been considered. As shown in
Figure 4 the errors considered in Table 1 are minimal when the minimum over a
one-hour time window is chosen as the approximation. Hence we approximate:

$\hat{c}_{h}^{i}=(-v_{h}^{i}+\min(v_{h+1}^{i},v_{h_{1}}^{i}))\mymathbb{1}_{\\{v_{h}^{i}>0\\}}$
(1)

This benchmark method reduces the lost corrections from 66.31% to 50.27% by
introducing only the 1.63% of added corrections.

Figure 4: Error introduced by the benchmark method, varying the time window.

### Reconstruction Method

The benchmark method adjusts negative views to estimate the platform
corrections, but it fails to detect correction events that have occurred when
the observed views $\hat{v}_{h}^{i}$ are nonnegative. Hence we developed a
method meant to detect anomalies in the views evolution and attribute them to
concealed corrections. The method consists of an XGBoost classifier that can
detect hours with unobserved corrections. Below we present how this classifier
was trained and we analyze its performance.

We constructed the train dataset as follows:

  * •

each row represents one hour of our time-series;

  * •

for each hour we extracted the evolution of views in the 12 hours before and
after that hour and added them as features;

  * •

we added as features the time of day (since corrections are roughly periodic
and occur mostly between 4 and 6 p.m.) and the number of hours elapsed since
publication.

In order to do parameter tuning, we chose to optimize the F1 score, a metric
defined as follows:

$\text{F1}=\frac{2}{\frac{1}{\text{precision}}\times\frac{1}{\text{recall}}}$

where precision stands for the rate of true positives over all the samples
classified as positive, while recall stands for the rate of true positives
over all the really positive samples in the data. In this way we can limit the
number of false positives and false negatives introduced by our classifier. We
divided our dataset into a train set of 90075 observations and a test set of
24674 observations. We used the train set to perform a 5-fold cross validation
to choose the optimal values of maximum depth of the decision trees, the
learning rate and alpha parameters of the XGBoost classifier. The performances
in terms of F1 score associated with different combinations of values are
shown in Figure 5. The best parameters, able to grant an F1 score equal to
0.649, are a maximum depth equal to 25, a learning rate equal to 0.2 and alpha
equal to 1.

The classifier allows us to reconstruct which hours have had YouTube
interventions without these being visible in the hourly aggregation. Once we
identify the hours with interventions, we estimate the magnitude of the
corrections by formula (1). In this way, as shown in Table 1, we are able to
reduce the lost corrections from 66.31% to only 35.64%.

Figure 5: XGBoost parameter tuning. Performances in terms of F1 score
associated with different combinations of parameters’ values.

##  4 Conclusion

The analysis of our data reveals that fake views are widespread and that most
fake views are corrected relatively late in the life of YouTube videos. This
observation rises concerns because of the key role of early popularity in
determining overall visibility. Indeed, rich-get-richer dynamics have been
repeatedly observed in the evolution of views counts on the platform [6], and
the total number of previous views has been credited as the most important
predictor of future popularity [6] [47] [37]. Furthermore, rewarding trendy
contents with more visibility is a central feature of the recommendation
system, which –according to YouTube developers– aims at “bootstrapping and
propagating viral content” [18]. While encouraging diffusion of trending
videos, the recommendation system also constitutes the main source of views
for most YouTube videos [56] [57] and hence regulates the attention economy of
the platform. Its suggestions are updated relatively quickly: according to
Roth et al., for instance, two thirds of the suggestions are associated with a
given video for less than two days [43]. Such a fast refreshing of suggested
videos, along with the massive impact of recommendation system, warrants
concerns about delays in fake views correction. With a delay in correction,
videos has handsomely enough time to be recognized as viral and thus be pushed
to a wider audience than they would have achieved without illegitimate
boosting.

Our work has brought to light the vast amount of channels and videos concerned
by this phenomenon and its features in term of rhythms and frequency. We have
also highlighted the existence of a positive correlation between illegitimate
and real views. In the absence of first-hand data on fake views corrections,
we cannot rule out the possibility that it is fake views that boost the
popularity of some videos. Given the importance of the subject and the
potential harm from the malfunctioning of the correction policy, our findings
should –at the very least– encourage YouTube to include in its API the number
of corrected views for each videos, as well as their history. As we have
shown, this information is crucial to investigate the alarming possibility
that techniques of views inflation could be used to manipulate video
visibility by triggering viral dynamics sustained by manual and algorithmic
recommendation.

## Acknowledgments

A major acknowledgment goes to Yoan Dinkov and Preslav Nakov of the Qatar
Computing Research Institute (QCRI) for their help in collecting YouTube data.
A special thanks goes to Bilel Benbouzid for the precious discussions on the
French YouTube landscape.

## Declarations

  * •

Funding
This research has been supported by CNRS through the 80 PRIME MITI project
”Disorders of Online Media” (DOOM), by ANR through grant 19-P3IA-0003 and by
the European Union’s Horizon 2020 project SoBigData++ (grant agreements No.
871042).

  * •

Availability of data and materials.
Data is available on figshare at [11], [10], [9].

  * •

Code availability.
Code is available at github https://github.com/mariacastaldo/Fake_views.

  * •

Authors’ contributions.
M.C. conceived and performed the research, collected and analyzed the data,
discussed the results, wrote the manuscript. P.F., F.G., T.V. conceived the
research, discussed the results, wrote the manuscript. All authors have read
and approved the manuscript.

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