Twitch, a live streaming platform that’s been active for over a decade since their start back in 2011. A media platform that lasts that long doesn’t do it without a dedicated community. Twitch has changed much since it’s inception and gained huge popularity during the 2020 pandemic. Twitch’s diversity is communities has always been present in the platform as it’s built on common interest. What I intend to do is take a look at those communities in Twitch, identify some central actor, and see how dense the network is.
This data includes links between steamers and variables such as viewer amount and whether the streamer was partnered. This data was collected by B. Rozemberczki, C. Allen and R. Sarkar. It was collected in May of 2018.
g = graph_from_data_frame(d = twitchlinks, directed = F, vertices = twitchnodes)
set.seed(505)
plot(g, vertex.size = 3, edge.color = NA,vertex.label="",
vertex.label=NA, , main = "English Speaking Twitch 2018")
lou <- cluster_louvain(g)
set.seed(505)
plot(lou,g, vertex.label="", main = "Louvain Clustering",vertex.size = 3, edge.color = NA,vertex.label="",
sub = sprintf("Modularity: %.4f", modularity(lou)))
## Connectivity and Major Actors
max_degree(g)
## [1] 720
diameter(g)
## [1] 10
While the graphics aren’t an enormous help due to their output, there are some things we are able to tell through other means. As you can see under the Louvain clustering graphic, the modularity of the graph was .44. With this we are able to tell that their is relatively decent community definition within Twitch’s English speaking group during 2018. We are able to look at other values like the total degree of 720. In the context of Twitch, it makes sense as links are defines as follows between streamers and Twitch being a content website where collaborations will happen.Finally, the Twitch network has a diameter of 10. This means there is great indication of inter connectivity within the greater Twitch community. Overall, this shows that there are defined communities within the Twitch ecosystem.