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# Creators’ Commenting Behavior and Linking Strategies in the French YouTube Environment

Kurt Kusterer, Sylvain Mignot, Annick Vignes

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## To cite this version:

Kurt Kusterer, Sylvain Mignot, Annick Vignes. Creators’ Commenting Behavior and Linking Strategies in the French YouTube Environment. 2025. hal-05287186

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https://hal.inrae.fr/hal-05287186v1

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# Creators' Commenting Behavior and Linking Strategies in the French YouTube Environment.

Kurt Maxwell Kusterer<fnref n="1" /> Sylvain Mignot<fnref n="2" /> Annick Vignes<fnref n="3" />

**Abstract.** From an empirical analysis of a rich dataset describing the French YouTube environment, this article attempts to understand the determinants of revenue on the YouTube platform. While the revenues depend on the level of popularity of Youtubers, our focus lies in discerning the factors driving popularity. Specifically, this research focuses on understanding to what degree commenting videos on YouTube can serve as a strategic tool for the commentators. We first distinguish between different commenting strategies, depending on whether the commentator is a YouTuber or an ordinary viewer. Subsequently, we employ an Exponential Random Graph Model (ERGM) to assess the factors influencing the likelihood of commenting on a video. Our findings underscore the significance of co-commenting networks on the YouTube platform, suggesting that cooperation may emerge as a prevailing strategy on YouTube.

**Keywords:** Network analysis, Social media, YouTube, Modeling

## 1 Acknowledgments

We wish to thank the ANR APY (Projet-ANR-20-CE38-0011), the participants of the LISIS "journée des doctorants", the participants to the CAMS-EHESS seminar "systèmes complexes pour les sciences sociales" and the participants of the "Bridging Theoretical and Empirical Approaches to the Study of Social Dynamics" 2024 conference.

## 2 Introduction

YouTube, established in 2005, has evolved into the world's premier video hosting platform, experiencing consistent growth since its inception (Burgess & Green, 2018). The YouTube platform earned approximately 200 billion USD in ad revenue in 2018 (Lund, Zukerfeld, Lund, & Zukerfeld, 2020). The year prior to this, YouTube published a press release stating that "the number of channels

<footnote n="1">¹ Université Gustave Eiffel; 5 boulevard Descartes, Champs-sur-Marne.
kurtkusterer23@gmail.com</footnote>

<footnote n="2">² Université Catholique de Lille; 60 Bd Vauban, Lille.
Sylvain.mignot@univ-catholille.fr,</footnote>

<footnote n="3">³ LISIS:Laboratoire,Interdisciplinaire,Sciences,Innovations,Sociétés, 5, boulevard Descartes; Champs-sur-Marne. annick.vignes@inrae.fr</footnote>
earning six figures per year on YouTube is up to 50% y/y" (Bärtl, 2018). Many
see YouTube as a lucrative career path due to its potential for revenue gen-
eration, driving a significant number of individuals to create online video con-
tent (Burgess & Green, 2009). Within its vast user base, two distinct categories
of users emerge: viewers, who consume and engage with videos by watching,
liking, and commenting, and YouTubers, who not only upload videos but also
engage as viewers themselves (de Bérail & Bungener, 2022). (Rochet & Tirole,
2003) and (Caillaud & Jullien, 2003) emphasize how in platform economies such
as YouTube, the growth of users (i.e. an increase in viewers) on one side of the
platform leads to a corresponding increase in creators (i.e. increase in YouTu-
bers) on the other side. This phenomenon creates an increase in the network
externalities associated with the the platform, enabling the YouTube platform
to generate the immense profits that it does. For YouTube channels on the plat-
form, their profit is strongly related to their popularity (Figueiredo, Benevenuto,
& Almeida, 2011). Thus, understanding how a YouTuber becomes popular is
fundamental to the interpreting their income potential on the platform (Chat-
zopoulou, Sheng, & Faloutsos, 2010). A key question that emerges, is how to
increase the popularity of content on YouTube. Our study aims to understand
how the strategic utility of video commenting can serve as a tool for YouTubers,
in promoting their content. Hence, we formulate the primary research question
in our paper:

- *Is commenting used as a strategic tool within the French YouTube environment?*

In what follows, we study the strategic use of comments for improving the
popularity and engagement rate<fnref n="4" /> of a YouTuber. Based off the work of (Schultes,
Dorner, & Lehner, 2013) and (Siersdorfer, Chelaru, Nejdl, & San Pedro, 2010)
whom explain, how the leveraging of comments enables YouTubers to build their
communities.

By analyzing a data set built from comments, videos and channels<fnref n="5" /> we high-
light examples of the strategic use of comments. We show how commenting can
be used as a form of promotion, for both current content and future content. We
conceptualize YouTube as a bilateral network, where connections exist between
videos and their respective commentators. Using the network to identify the
determinants influencing the establishment of a link, through the use of a Ex-
ponential Random Graph Model (ERGM) (Gruzd & Hodson, 2021). This leads
to the formulation of a secondary research question:

- *Can the ERGM (Exponential random graph model) help to predict actor behavior?*

We note that our approach to studying the comments diverges from tradi-
tional Natural Language Processing (NLP) investigations of comments, which

<footnote n="4">⁴ Engagement rate describes the level of activity associated with a video or channel.</footnote>

<footnote n="5">⁵ For the period of 2021-2022.</footnote>
typically employ text mining techniques to scrutinize comment content (see (Teng, Khong, Sharif, Ahmed, et al., 2020) for a recent review of this literature).

We expect to see that comments serve as an important tool for a YouTuber, directly impacting the popularity of content through their placement. Posting comments represent a more active form of participation, than the ratings of videos (likes, dislikes, comment ratings) (Cha, Kwak, Rodriguez, Ahn, & Moon, 2009) hence the strategic utility of the engagement metric<fnref n="6" />. (Rihl & Wegener, 2019) explains that comments are used as method for strengthening a YouTubers fan base. We aim is to show that this phenomenon is achieved through the strategic use of comments.

We have laid out the paper into the following sections. **Related work**, **Data**, **Selecting the monetized content**, **Data Analysis**, **Network analysis** and **Conclusion**.

# 3 Related work

Recent literature around YouTube, has been seen to follow the developments of the professional trends on the platform (Törhönen, Giertz, Weiger, & Hamari, 2021). YouTube once held the reputation for being a youthful alternative to mainstream media, however since its purchase by Google, the platform has evolved into a site dominated by commercialized and professional video content (Morreale, 2014). This new ecology has created a space for content creation which has unimaginable scale and scope, promoting creativity and culture while at the same time proving to be turbulent and precarious for creators (Cunningham, Craig, & Silver, 2016).

The YouTube platform has had a significant impact on global media, with nearly two billion monthly users. YouTube is a central player in the media ecosystem however YouTube's monetization strategies, particularly since the introduction of the YPP in 2007, has changed the motivations for content creation on the platform (Coromina, Matamoros-Fernández, & Rieder, 2020). The program was introduced shortly after Google acquired YouTube (9 October, 2006), Google being fully intent on monetizing its popularity (Andrejevic, 2009). This changed the nature of content creation on the platform, from what originated as user-generated-content (UGC) into professionally generated content (PGC) (Kim, 2012). This program, which organizes the sharing of revenue between a select group of creators, has drastically increased the competition for attention and legitimacy on the platform (Caplan & Gillespie, 2020).

While there is an ever increasing number of viewers and creators on the platform, visibility and popularity remain highly skewed, resulting in the majority of attention being captured by a small segment of creators (Rieder, Coromina, & Matamoros-Fernández, 2020). This gives rise to an environment in which content creators compete for attention (Poutanen, Kovalainen, & Rouvinen, 2019),

<footnote n="6">⁶ These metrics provide a measure of how many times a video has been interacted with.</footnote>
uploading video content as a means of generating revenue (Duffy, Pinch, Sannon, & Sawey, 2021; Van Dijck, Poell, & De Waal, 2018).

Understanding the factors that impact popularity dynamics of social media can provide valuable insights to content creators (Figueiredo et al., 2011). Popularity differences arise not only because of video content but there also other "content-agnostic" factors (Borghol, Ardon, Carlsson, Eager, & Mahanti, 2012). Factors such as subscriber base and information dissemination also have an important effect on video popularity on YouTube (Irshad, Anand, & Bisht, 2019).

The importance of other meta-level features such as video-title, tags and thumbnail form an important part of the digital media content, while interactions between creator and viewers is known to add to video popularity as well (Hoiles, Aprem, & Krishnamurthy, 2017). These social interactions between creator and viewers are represented by engagement metrics such as likes, views and comments (MacDonald, 2021). These metrics contribute towards the subscribers counts and the watch time of content for creators, these two particular channel metrics also ensure YPP eligibility (1000 subscribers and 4000 hours viewed). Creators, however, remain at a disadvantage when attempting to achieve YouTube Partner Program (YPP) eligibility, primarily due to the lack of transparency surrounding the YouTube algorithm. This opacity makes it difficult for creators to ensure their content appears in the "Suggested Videos" section for viewers, thereby hindering their ability to secure the necessary subscribers and watch time required for YPP eligibility (Kopf, 2020). Recent research on YouTube has aimed to improve the understanding of the YPP by focusing on various metrics that YouTubers rely on to achieve popularity.

According to (Halim, Hussain, & Ali, 2022), the French YouTube context is gauged by three predominant metrics; positive comments, duration of content and title of content. (Schultes et al., 2013) and (Siersdorfer et al., 2010) analyze how the use of comments, can impact video perception, community building, and dissemination of information. Comments, thus act as an important tool in the strategic arsenal of a YouTuber.

## 3.1 Data Sets

Our data covers the French YouTube environment for the period of 2021 July 1st to 2022 July 30th. The data set is divided up into three, a set of channels, a set of videos for the channels and a set of comments for the videos. We combine these data sets in order to build one final data set.

**Channels** The channels data set holds aggregated channel information for 36 973 channels in the French YouTube environment. Table 1 shows the descriptive statistics of engagement metrics<fnref n="7" /> for the channels in our sample.

<footnote n="7">⁷ Note: These metrics are sum totals of the metrics since the inception of the channel.</footnote>
<table><caption>**Table 1.** Descriptive statistics for channels data set</caption><thead><tr><th>Measure</th><th>Subscribers</th><th>Commentators</th><th>Video views</th><th>Likes</th><th>Dislikes</th></tr></thead><tbody><tr><td>Mean</td><td>217 068</td><td>1550</td><td>48 779 417</td><td>1 232 501</td><td>40 472</td></tr><tr><td>Median</td><td>33 100</td><td>244</td><td>3 105 480</td><td>74 159</td><td>2713</td></tr><tr><td>Kurtosis</td><td>152</td><td>6</td><td>424</td><td>868</td><td>426</td></tr><tr><td>Skewness</td><td>9</td><td>2</td><td>16</td><td>23</td><td>17</td></tr></tbody></table>

**Videos** The video data set consists of 391 588 videos, each video belongs to a channel within the channel data set and contains the information for the video within the period 2021 July 1st to 2022 July 30th.

<table><caption>**Table 2.** Descriptive statistics for videos data set</caption><thead><tr><th>Measure</th><th>Duration (in seconds)</th><th>Views</th><th>Likes</th><th>Dislikes</th><th>Comments</th></tr></thead><tbody><tr><td>Mean</td><td>1729</td><td>65 669</td><td>3 418</td><td>66</td><td>199</td></tr><tr><td>Median</td><td>729</td><td>7 781</td><td>332</td><td>9</td><td>34</td></tr><tr><td>Kurtosis</td><td>11 414</td><td>9</td><td>847</td><td>229 372</td><td>8 186</td></tr><tr><td>Skewness</td><td>48</td><td>79</td><td>22</td><td>437</td><td>62</td></tr></tbody></table>

**Comments** The comments data consist of 12 299 310 comments and consists of the following features: commentator name, commentator id, video id, id of the channel posting the comment and upload time.

<table><caption>**Table 3.** Descriptive statistics for comments data set</caption><thead><tr><th>Measure</th><th>per commentator</th><th>per category</th><th>per video</th></tr></thead><tbody><tr><td>Mean</td><td>3</td><td>762 829</td><td>149</td></tr><tr><td>Median</td><td>1</td><td>431 565</td><td>44</td></tr><tr><td>Kurtosis</td><td>9817</td><td>3</td><td>321</td></tr><tr><td>Skewness</td><td>46</td><td>2</td><td>13</td></tr></tbody></table>

# 4 Selecting the monetized content

Our study concentrates on channels that generate revenue on the YouTube platform. To achieve this, we will filter out all channels that do not meet the eligibility criteria for the YouTube Partner Program (YPP) by considering the conditions required for qualification. Channels must meet specific criteria to qualify for the YPP, including amassing a minimum of 4000 hours of viewing time within the
past 12 months and maintaining at least 1000 subscribers. In addition, since
2017, YouTube has required channels to have accumulated a minimum of 10,000
lifetime views before being allowed to apply for the program (Popper, 2017). By
focusing on channels that meet these criteria, we aim to gain insights into the
factors that contribute to revenue generation on the platform.

The YouTube partner program is tiered, channels are sorted according to the number subscribers they have. We consider YouTubers whom achieved 'silver status,' thus they have over 100,000 subscribers, this signifies a kind of professional status on the platform.

The sharing of ad-revenue has served as motivator for creators to produce
more and better quality content (?). YouTube's transition from a UGC (user
generated content) space to a PGC (professional generated content) space has
been fueled by the YPP.

**4.1 The distribution of income for the sample**

In figure 1 we show the distribution of earnings for our sample within the French YouTube environment.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Distribution of earnings in € (X-axis : x. 10⁶)</figcaption></figure>

In Figure 1, we observe a highly skewed distribution towards the lower end, where the majority of channels are located with lower earnings. Conversely, there is a very small population of channels at the higher end of the distribution. This distribution of earnings follows a similar pattern to the previously measured metrics of, views, likes, and comments. This indicates the importance of engagement metrics to a channel's earning potential on the YouTube platform.
## 5 Building the data set

Our study focuses on how the attachment of comments to a video can serve as a promotional tool for the channel. We build a data set of comments, containing information for the video and for the channel being commented.

We combine datasets by linking video IDs to their respective channel, keeping only channels that meet the eligibility criteria for the YPP. Subsequently, we integrate this combined set of Videos/Channels with our collection of comments. This yields a comprehensive dataset of comments made to videos. Table 4, shows the features within the data set.

<table><caption>Table 4. Features of the aggregated data set</caption><thead><tr><td>Feature</td><td>Description</td></tr></thead><tbody><tr><td>Commentator name</td><td>Name of the channel/commentator posting the comment</td></tr><tr><td>Commentator id</td><td>The id of commentator posting the comment</td></tr><tr><td>YouTube channel id</td><td>The id of the channel being commented</td></tr><tr><td>Channel name</td><td>The name of the channel being commented</td></tr><tr><td>Subscribers</td><td>The number of subscribers for the channel being commented</td></tr><tr><td>Video Id</td><td>The id of the video being commented</td></tr><tr><td>Duration</td><td>The duration of the videos being commented</td></tr><tr><td>Views</td><td>The number of views for the video being commented</td></tr><tr><td>Count of comments to channel</td><td>The number of comments posted to the channel over the period of the study</td></tr><tr><td>Count of comments to video</td><td>The number of comments for the video over the period of the study</td></tr><tr><td>Category</td><td>The category selected by the channel</td></tr><tr><td>Ratio</td><td>The ratio of likes to dislikes.</td></tr><tr><td>Self promotion</td><td>A comment made by the creator to their own video.</td></tr><tr><td>Cross Promotion</td><td>A comment made by a known YouTube on the content of another creator.</td></tr></tbody></table>

We have created the features of self promotion and cross promotion comment, we explain these in a later section of the paper.
# 6 Data analysis

## 6.1 Analyzing commenting

To gain a deeper understanding of commenting on the platform, we view the distribution of comments per video. In order to do this, we test the fit of the data to two distinct distributional shapes. Firstly, we present the distribution of comments per video on a log-log scale. Figure 2 illustrates the distribution of comments across videos.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Log-log distribution of comments per a video</figcaption></figure>

We can observe a roughly straight gradient on the log-log plot, this can indicate the existence of a power law distribution. However, this condition solely is not sufficient to fit a power-law distribution and further test will need to be done (Clauset, Shalizi, & Newman, 2009).

Thus, we test the fit of the data against alternative distributions, specifically the power law vs exponential, truncated power law, and log-normal distributions. We compare these using a goodness-of-fit measure for each distribution, which can be considered individually or in comparison to the fit of other distributions. The resulting *R* statistic is the log-likelihood ratio between the two candidate distributions. This value will be positive if the data is more likely to follow the first distribution, and negative if the data is more likely to follow the second distribution (Alstott, Bullmore, & Plenz, 2014).

Table 5 presents a comparison of a power law to other forms of heavy-tailed distributions. In the first instance, the exponential distribution presents a slightly stronger fit than that of the power law. In the second instance, the truncated power law is a stronger fit, and lastly, the log-normal distribution provides the strongest fit. Given these results, we proceed to test the goodness of fit for a log-normal distribution for comments on videos.

In table 6, we show the measures for goodness of fit for the log normal distribution.
<table><caption>**Table 5.** Comparing distribution fits</caption><thead><tr><th>Power law</th><th>Exponential</th></tr></thead><tbody><tr><td>7.62</td><td>11.08</td></tr><tr><td>Power law</td><td>Truncated power law</td></tr><tr><td>-46.45</td><td>0</td></tr><tr><td>Power law</td><td>Log-normal distribution</td></tr><tr><td>-53.44</td><td>77.69</td></tr></tbody></table>

<table><caption>**Table 6.** Log normal distribution</caption><tr><td>Shape:</td><td>1.93</td></tr><tr><td>Location:</td><td>0.67</td></tr><tr><td>Scale:</td><td>35.46</td></tr></table>

These results indicate:

- Shape ($\sigma$ = 1.93): Higher standard deviation, indicating much greater variability and a longer tail.

- Location ($\mu$ = 0.67): Mean of the logarithm is higher, shifting the distribution to the right

- Scale ($\exp(1)$ = 35.46): The median is 35.46, indicating higher central values.

The results of the log-normal fit are corroborated by the descriptive statistics of the videos. The shape of the distribution, is characterized by a high standard deviation, indicating significant variability in the data. The mean, situated at the higher end of the values, and the median suggest that the central tendency of the distribution leans towards the upper scale.

## 6.2 Describing comments

In the subsequent section, we present the cumulative distribution function for comments on the platform. Initially, we display the distribution prior to transformation, followed by the distribution after applying a logarithmic transformation. The log transformation is utilized based on the preceding tests, which indicated that it provides the best fit for our data.

**Channel level:** *Figure 3 and figure 4* illustrate the distribution of comments per channel.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Distribution of comments per channel. **Fig. 4.** Distribution of comments per channel (log normal)</figcaption></figure>

Based on the distribution shown in *figure 5*, we observe that the majority of channels are situated at the lower end of the value scale. The graph exhibits a sharp gradient, highlighting a significant disparity in the distribution of comments across channels. *figure 6* presents the logarithmic distribution of this phenomenon, which more clearly indicates the steep yet consistent increase in comments towards the higher end of the scale.

We aim to explain this phenomenon by considering the factors influencing the likelihood of a comment between videos. Previous literature indicates that commenting is a form of community engagement. Therefore, the distribution suggests that channels near the top of the distribution are actively engaging with their communities. This engagement can benefit channels in two ways: greater engagement increases favor with the YouTube recommendation algorithm, and it fosters community growth through promotion, akin to traditional market expansion.
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Distribution of commentators per channel

**Fig. 6.** Distribution of commentators per channel (log normal)</figcaption></figure>

In *figure 5* and *figure 6*, we observe the distribution of commentators per community. These distributions exhibit a gradient similar to that of comments, albeit less pronounced. We include the distribution of commentators as an indicator of community size around each channel. Channels with a large number of commentators are more popular and possess greater mass appeal compared to those with smaller communities.

Video level: In *figure 7* and *figure 9*, we observe the distribution of comments per video. The differences in these distributions indicate a disparity between videos and channels in terms of comment collection. What might be assumed to be a co-linear relationship between video and channel, in fact is that not straightforward. The gradient of the distribution is significantly steeper than that of comments per channel, which can be attributed to the virality of video content as a contributing factor to increased commenting. We can attribute this difference in gradient to the impact of content virality over channel popularity.
<figure><img src="image_7.png" /><figcaption>**Fig. 8.** Distribution of comments per video (log normal)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 9.** Distribution commentators per video</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 10.** Distribution of commentators per video (log normal)</figcaption></figure>

<figure><img src="image_10.png" /></figure>

*Figure 9* and *10* illustrate the number of commentators per video. Videos exhibit a more rapid ascent to maturity compared to channels. Consequently, the distribution displays a steeper gradient and levels off sooner, as videos typically reach maturity within the first few days after upload.

## 6.3 Commentator strategy

It was necessary to divide our commentators into two distinct groups in order to produce a graph of co-commentators between videos. We define these two groups as follows:

- Commentators whom have only commented one video in the sample.
- Commentators whom have commented more than one video in the sample.

Approximately 16% of the commentators in our dataset comment only once. In order to build a network of co-commentators, we only consider the commentators who comment more than once.

Using the co-commentator network, we aim to show that a scale-free network is present within the comments network. According to (Almaas, Vázquez, & Barabási, 2007), scale-free network is a type of complex network in which the degree distribution follows a power law, meaning that a small number of nodes, known as hubs, have a very large number of connections while most nodes have only a few connections.

## 6.4 Commenting strategy

Our dataset reveals that YouTubers who produce monetized content frequently post comments on their own uploads, a tactic we associate with promotion. This tactic drives attachment on the platform, providing the basis for a scale-free network to emerge. The emergence of a scale-free network is characterized by the rate at which new nodes attach to previously existing nodes in an increasing function of the degree of the target node (Barabási & Albert, 1999), a mechanism known as preferential attachment (Redner, 2004).

We believe that the use of promotional comments drive preferential attachment and thus, drives the development of a scale-free network around the comments on YouTube.

Two forms of promotion have been observed on the platform:

- Self promotion - The act of commenting ones own content.

- Cross promotion - The act of commenting the content of another "YouTuber" or creator.

The work of (Schultes et al., 2013) explains that the strategic placement of comments can be used to generate greater engagement.

**Self-promotion:** Typically, pinning a question at the top of the comment section, which is often related to the newly published video or upcoming content. This approach invites engagement through the comments section, thereby activating the community around the content. Of the 3478 YouTubers in our sample, 2191 or 62% of YouTubers have used self promotion comments.

**Cross-promotion:** Comments such as "I also like this video by [channel name]" or "You should check out this other channel that makes similar content", these instances clearly represent cross promotion on the YouTube platform. This can
also be viewed as a form of co-operation on the platform. Of the 3478 YouTubers
in our sample, 1921 YouTubers or 55% of YouTubers have used cross promotion comments.

In a more practical sense YouTubers need to consider their visibility on
the platform and whether their content is being promoted by YouTube's rec-
ommendation algorithm. Greater engagement with their content (likes, views,
comments) is essential, as these metrics are direct indicators of content popular-
ity (Abdelkader, 2021)

# 7 Network analysis

## 7.1 Building the network

We now compute the co-commentator network from monetized video content and
this enables us to observe the relationships between videos and commentators.
The co-commentator network is a projection of a bipartite graph (video/ com-
mentators) into a homogeneous one (videos/ videos). In this projected graph,
videos are represented by nodes and the co-commentators by edges. Each edge
that connects a pair of nodes represents a commentator who has commented on
this pair of videos.

## 7.2 Network visualization

To facilitate the viewing of the graph, we select a subset of links and videos
based on their popularity. This decision is necessary because creating a network
visualization based on a data frame with ten million observations would result
in a potentially massive edge list. This would require significant computational
resources and would not be easily readable, making it an ineffective and un-
necessary use of resources. By using a subset of the most popular creators, we
can more easily view the communities related to the creators and examine their
commenting behavior.

In order to filter the data, we use an algorithm to extract the backbone of
the network. A backbone is a sparse and unweighted subgraph of a network
that contains only the most ‘important’ or ‘significant’ edges. A backbone can
be useful when the original network is too dense (Neal, 2022). In order to keep
only meaningful links and nodes, we use a disparity filter (Serrano, Boguná, &
Vespignani, 2009). It involves comparing edge weights to their expected values
under a null model that considers each node’s local neighborhood. The alpha pa-
rameter used for this visualization is 0.01. This parameter of the disparity filter
is used to control the level of significance required for an edge to be included in
the backbone. Specifically, the disparity filter calculates a p-value for each edge
in the network based on the null hypothesis that the edge weight is randomly
distributed. The p-value represents the probability of observing an edge weight
as extreme as the one observed, assuming the null hypothesis is true. The alpha
parameter is then used to determine a threshold p-value, below which edges are considered significant and included in the backbone.

The graph of the backbone contains 238 videos, 11 categories and 51 channels. All of which belong to 14 unique communities defined using the Louvain community detection algorithm, which optimizes the modularity used for measuring links inside a community compared to the between communities (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008).

<figure><img src="image_11.png" /><figcaption>**Fig. 11.** Communities of the backbone of the co-commentator network, each node color is a category</figcaption></figure>

We can observe that 57% of communities are characterized by a single channel, underscoring the significant role that channels play within the co-commentator network. This pattern suggests that commentators often follow creators or are directed by them to further content. Notably, these communities show limited differentiation in terms of categories, with only two communities encompassing
more than two categories. However, as the size of a community grows, there is a
greater differentiation of creators and categories, as exemplified by communities
1 and 13.

Furthermore, we observe the presence of a large interconnected component
at the center of the graph. Within this component, five distinct communities
are linked through cross-community connections, suggesting the presence of a
factor that transcends community boundaries. We hypothesize that this factor
is popularity, which motivates viewers to explore new content from other chan-
nels, either voluntarily or through the cross-promotional comments of popular
creators.

The resulting graph allows for inspection of commenting strategies at an
inter-video level. Our aim is to understand if the existence of a self or cross
promotional comment increases the likelihood of further commentary between
videos. Thus, we seek to prove the strategic utility of comments in directing the
flow of popularity on the platform and the importance of the role of the YouTu-
ber in influencing their communities' viewing habits.

In the following section, we apply an exponential random graph model to the
entire sample to better understand the determinants of a link between videos.

7.3 ERGM model

We run the Exponential Random Graph Models (ERGM) to evaluate which
characteristics influence the probability of commenting a video. The creation of
a link will be explained in terms of common video features. Our method is similar
to that of (Gruzd & Hodson, 2021), in which they employ network analysis along
with an ERGM.

Exponential Random Graph Models are one of the most important families of
models proposed to statistically develop sound network analyses (Lusher, Kosk-
inen, & Robins, 2013; Wang, Robins, Pattison, & Lazega, 2013). By generating
thousands of random networks to match the observed network we are trying
to model the observed network. This approach allows researchers to statistically
test how likely some features are to appear in a given network Gruzd and Hodson
(2021).

The ERGM identifies the determinants of a link in the network furthering
our understanding of the strategic means for which commenting can be used.
Let G represent the observed network with vertices V and edges E. The Exponential Random Graph Model (ERGM) can be defined as:

$$P(G|\theta) = \frac{1}{Z(\theta)} \exp \left( \sum_{i=1}^{7} \theta_i \cdot g_i(G) \right)$$

where:

- $g_1(G)$ Edges

- $g_2(G)$ Match on Channel Id

- $g_3(G)$ Absolute difference of views

- $g_4(G)$ Match on category

- $g_5(G)$ Covariance of likes/dislikes ratio

- $g_6(G)$ Covariance of count of cross promotion comments

- $g_7(G)$ Covariance of count of self promotion comments

Below, we see the results of the ERGM model in table 7:

<table><caption>Table 7. ERGM model results</caption><thead><tr><th>variable</th><th>estimate</th><th>std error</th><th>Pr(&gt;|z|)</th></tr></thead><tbody><tr><td>edges</td><td>-2.015<sup>1</sup></td><td>3.423<sup>-3</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Same channel</td><td>7.338</td><td>3.315<sup>-3</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Same category</td><td>8.179<sup>-1</sup></td><td>6.590<sup>-4</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Absolute difference of nbr views</td><td>-2.327<sup>-1</sup></td><td>2.293<sup>-4</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Nbr views</td><td>6.997<sup>-1</sup></td><td>1.478<sup>-4</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Likes/dislikes ratio</td><td>1.575<sup>-3</sup></td><td>4.329<sup>-6</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Nbr of cross promotion links</td><td>2.150<sup>-1</sup></td><td>1.243<sup>-4</sup></td><td>&lt; 1<sup>-4</sup></td></tr><tr><td>Nbr of self promotion links</td><td>1.85<sup>-1</sup></td><td>3.826<sup>-4</sup></td><td>&lt; 1<sup>-4</sup></td></tr></tbody></table>

Our dyadic (attributes of pairs of nodes) explanatory variables brings us a lot of information concerning the commentating behavior of YouTube users. As expected our model reveals that a significant determinant prompting users to comment on the same two videos is their commonality in terms of originating from the same channel (matching channel). What we observe here is a community effect. viewers that feel engaged enough to comment video of a specific creator will have a higher probability of commenting other videos of the same creator.

We also observe a positive effect of belonging to the same category (matching category). Even while controlling for the channel effect, a given commentator will have a tendency to comment videos belonging to the same category. It seems that center of interest of commentators plays a significant role in their choice of videos.

The absolute difference of views is significant to the shape of the network, in that it indicates videos which have obtained a large numbers of views are more likely to be connected to videos of similar popularity. Commentators will tend
to comment videos with similar number of views

The other explanatory variables are node-level features.

The number of views have a significant positive effect. As numbers of views will be correlated with number of comments, we confirm that highly viewed videos will have a tendency to be of a higher degree (more links). In itself this result isn't surprising or interesting, but it allows us to control our results by removing the effect of the number of views from other estimators.

Our Likes/dislikes ratio shows that videos with a positive ratio will tend to be the nodes with a higher degree. It seems well received videos will be more commented, increasing the likelihood of attracting multi-video commentators.

Cross promotion comments are a significant feature in determining whether two videos will be linked or not. Comments posted by YouTubers on a video will increase the engagement rate of that video. The result holds true to observed behavior on the YouTube platform. Youtubers direct the attention of their followers as they see necessary via interactions within the comments.

Self promotion comments are significant and positive in the determining of a link. This action increase engagement with the content and enables the YouTuber to direct the viewer to further content they have created.

We see that a cross-promotion comment has a stronger influence than self promotion comment. Even while controlling by channel, category, and videos characteristics the commentating strategies still have a significant influence on the existence of co-comments between two videos. Thus, using comments as a mode of cooperation with other creators is a strategic tool for any YouTuber.

The results are in line with our expectations for the use of comments on the platform. Clearly showing that there is a strategic utility to the use of comments and answering the primary research question: "Is commenting used as a strategic tool within the French YouTube environment?". Further, we show that the ERGM model is an effective means for predicting actor behavior on the YouTube platform, thus answering the second research question "Can the ERGM (Exponential random graph model) help to predict actor behavior?".

## 8 Limitations

This study encounters several limitations rooted in computational, data, and platform-specific contexts that impact the overall findings and their generalizability. Firstly, the computational demands associated with constructing and analyzing large-scale networks are substantial. The sheer size and complexity of these networks require significant computational resources, which can constrain the depth and breadth of the analysis. Additionally, as networks grow larger, their interpretability diminishes. The intricate relationships within extensive net-
works can obscure meaningful insights, making it challenging to derive actionable conclusions. The complexity is further compounded when dealing with multilevel networks, where interactions across different levels can create intricate patterns that are difficult to interpret clearly.

In terms of data, the study is limited by a high proportion of missing values within the dataset, which can introduce bias and affect the validity of the results. The dataset, representing only a single year, also restricts the ability to evaluate the long-term effectiveness of the strategy under investigation. A more comprehensive understanding would require longitudinal data to capture trends and fluctuations over a longer period, thus providing a clearer picture of the strategy's efficacy across time.

Platform-specific issues further complicate the research. Accessing detailed YouTube data, particularly concerning earnings, proves difficult due to the platform's restrictive data-sharing practices. The lack of transparency regarding YouTube's policies and algorithms further hampers the reliability of the findings. With the platform's rules and algorithms subject to frequent changes, the consistency and comparability of the data can be affected, complicating the analysis and interpretation of results. These limitations highlight the need for careful consideration when interpreting the study's outcomes and suggest avenues for future research to address these challenges more comprehensively.

# 9 Conclusion

In conclusion, our analysis of dyadic explanatory variables reveals significant insights into the commentating behavior of YouTube users, within the French YouTube environment. The study highlights several key determinants that influence whether users comment on the same pairs of videos, providing valuable information on the factors that shape engagement patterns on the platform.

One of the most prominent findings is the community effect, evidenced by the strong influence of matching channels on commenting behavior. Users who engage with videos from a specific creator are more likely to comment on other videos from the same creator, suggesting a loyal audience base. Similarly, videos belonging to the same category also attract repeat commentators, underscoring the importance of shared interests in driving user interactions. These results emphasize the role of content type and creator affiliation in fostering user engagement.

More over we observe the behavior of the viewers whom are attracted to videos with similar popularity levels. There is a tendency for this popular type of content to draw the attention of the same commentators. This suggests that users are drawn to videos that resonate with a broad audience, reinforcing the visibility and reach of popular content. The positive correlation between the number of views and the degree of nodes further supports this, confirming that highly viewed videos naturally attract more comments and, consequently, more
interconnected commentating behavior.

Cross-promotion and self-promotion comments significantly influence the like-
lihood of two videos being linked by commentators. Cross-promotion, in par-
ticular, has a stronger impact, suggesting that collaborations and interactions
between different YouTubers are more effective in driving user engagement than
self-promotion efforts. This finding underscores the strategic importance of co-
operations among creators, as they can amplify visibility and foster a more in-
terconnected audience base.

Overall, our results demonstrate that, even after controlling for channel, cat-
egory, and video characteristics, commentating strategies have a significant im-
pact on the existence of co-commentators between videos. This strategic behavior
highlights the nuanced dynamics of user engagement on YouTube, emphasizing
the importance of community building, content categorization, and promotional
tactics in shaping the commentating network. These insights offer valuable im-
plications for content creators and platform strategies, providing a deeper un-
derstanding of how user interactions can be optimized to enhance engagement
and visibility on YouTube.
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