# Who comments on whom? An analysis of behavior on the French YouTube, viewed through its network of commentators.

Annick Vignes, Kurt Maxwell Kusterer, Sylvain Mignot

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Annick Vignes, Kurt Maxwell Kusterer, Sylvain Mignot. Who comments on whom? An analysis of behavior on the French YouTube, viewed through its network of commentators. Bridging Theoretical and Empirical Approaches to the Study of Social Dynamics, https://iscpif.fr/bridgesd24/, Jun 2024, Paris, France. hal-05310531

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Submitted on 12 Oct 2025

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# Who comments on whom? An analysis of behavior on the French YouTube, viewed through its network of commentators.

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

## 1 Methodology and Research question

YouTube, established in 2005, has evolved into the world's premier video hosting platform, experiencing consistent growth since its inception (Burgess & Green 2018). Within its vast user base, two distinct categories of users emerge: viewers, who consume and engage with videos by watching, liking, and commenting, and content creators, who not only upload videos but also engage as viewers themselves (de Bérail & Bungener 2022). The economic objective of a YouTuber is to increase their popularity to be line with YouTube's economic program which rewards the most popular creators. A key question for a YouTuber is then how to increase their popularity through engagement. In what follows and in the line of Schultes et al. (2013) we formulate the hypothesis that commenting can be a strategic tool to augment a YouTubers popularity.

Previous research on the YouTube environment has given greater attention to the metrics of views, likes, and subscribers. However, the work of Schultes et al. (2013) indicates that users' video perceptions (Likes and Dislikes) are indeed influenced by the dispersion of what the authors define as valuable and inferior comments. Thus, we create the following research question:

- *Is commenting used as a strategic tool in the French YouTube environment? Can the ERGM model help us to predict preferential attachment through commenting?*

## 2 Network analysis

Working on a huge dataset describing one year of French YouTube, we select monetized channels based on metrics required to qualify for the YouTube partner program : Sum of content viewed greater than 4000 hours in a year and a minimum of 1000 subscribers (Rieder et al. 2020). We then create a network of co-commentators, using commentators with at least two comments from our filtered database.

### 2.1 Building a bipartite graph

We project the bipartite network onto a homogeneous graph, where videos are considered as nodes and the links are weighted to represent the number of mutual commentators between two videos. We only wish to visualize the nodes and links which are most central to the network, hence we extract the backbone of the graph. In order to do this we set a threshold on the weight of the edges (Neal 2014). The backbone of graph is represented by 554 nodes/videos and 22 907 edges/comments (tab 2).

The above statistics indicate a moderately dense network, with a relatively high degree of clustering. It also exhibits a degree of modularity and separation into weakly connected components. The shortest average path length indicates that commentators do not stray far from their communities, and hence we distinguish the formation of distinct communities within the network.

Communities are generated via statistical inference using a Bayesian stochastic block model. In total there are 219 communities detected. The largest communities are formed around popular creators, whom have an established following.

<footnote n="1">¹ UGE, kurtkusterer23@gmail.com</footnote>

<footnote n="2">² UCL, sylvain.mignot@univ-catholille.fr,</footnote>

<footnote n="3">³ LISIS, annick.vignes@inrae.fr</footnote>
<figure><img src="image_1.png" /><figcaption>Fig. 1: Graph Backbone</figcaption></figure>

<table><caption>Fig. 2: Graph statistics</caption><thead><tr><th>Network Statistics</th><th>Values</th></tr></thead><tbody><tr><td>Network statistic</td><td>Value</td></tr><tr><td>Average degree distribution</td><td>85</td></tr><tr><td>Average weighted distribution</td><td>1658</td></tr><tr><td>Network diameter</td><td>6</td></tr><tr><td>Average path length</td><td>2</td></tr><tr><td>Graph density</td><td>0.154</td></tr><tr><td>Modularity</td><td>0.492</td></tr><tr><td>Average clustering coefficient</td><td>0.643</td></tr><tr><td>Number of weakly connected components</td><td>5</td></tr></tbody></table>

## 3 ERGM model and main results

In this section we analyze the results of our Exponential Random Graph Model (ERGM), which we selected to evaluate the characteristics that increase the probability of commenting a video. The creation of a link is explained in terms of common video features. Our method is similar to that of (Gruzd & Hodson 2021), which have also ran network analysis in combination with an ERGM.

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: $g_1(G)$ represents Edges, $g_2(G)$ A match on Channel Id, $g_3(G)$ Absolute difference of views, $g_4(G)$ A match on category, $g_5(G)$ Covariance of likes/dislikes ratio, $g_6(G)$ the Covariance of count of cross promotion comments and $g_7(G)$ Node covariance of Self promotion.

## 4 Results

When running the ERGM model, we observe that commentators have a preference to comment the content of their favorite creators. There is a high probability that the commentator will comment further content of the same creator. Once more we observe this phenomenon of preferential attachment concerning the categories, commentators frequently comment content of the same category. Considering the "absolute difference of Views" variable, which indicates a high degree of isolation between communities based on their number of views. We can formulate the assumption that preferential attachment links creators of the same popularity. We are able to observe this assumption through the significant effect of "cross promotion". Creators commenting/promoting the videos of other creators whom reciprocate this action. We find that "Self promotion" also has a positive impact on the generation of a link between two videos. On YouTube commenting represents an economic tool, which is used augment engagement and improve the visibility of content, this in turn leads increased income potential for creators.
Bibliography

Burgess, J. & Green, J. (2018), *YouTube: Online video and participatory culture*, John Wiley & Sons.
de Bérail, P. & Bungener, C. (2022), ‘Favorite youtubers as a source of health information during quarantine: Viewers trust their favorite youtubers with health information’, *Social Network Analysis and Mining* **12**(1), 88.

Gruzd, A. & Hodson, J. (2021), ‘Making sweet music together: The affordances of networked media for building performance capital by youtube musicians’, *Social Media+ Society* **7**(2), 20563051211025511.

Neal, Z. (2014), ‘The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors’, *Social Networks* **39**, 84–97.

Rieder, B., Coromina, Ò. & Matamoros-Fernández, A. (2020), ‘Mapping youtube: A quantitative exploration of a platformed media system’, *First Monday* **25**(8), Article-number.

Schultes, P., Dorner, V. & Lehner, F. (2013), ‘Leave a comment! an in-depth analysis of user comments on youtube’.