Structural Differences in Hashtag Communities: Highly Interconnected or Not?

In several recent posts, I’ve shown a variety of network diagrams based on who’s following whom in various twitter hashtag community networks. In this post, I thought I show a couple more, demonstrating the power of the visual approach for getting a quick feel for the structure of a particular community.

First up, here’s the inner friends graph for the #cam23 hashtag, which is used predominantly by a handful of Cambridge Unibiversity librarians who opted in to their local 23 things programme:

(Node size is proportional to the number of incoming friends links; colour is proportional to the number of outgoing links.)

So what do we see? Pretty much everyone in this network is following a large number of other folk in the network, and is being followed by a large number. The network is highly interconnected. Messages don’t necessarily need tagging in order to ensure that the message gets distributed across the network because most folk are connected most other folk.

(It’s never that simple of course. The likelihood of someone seeing a message from a particular person in their network is a function of, amongst other things, the number of people they follow, the frequency at which those people post, and so on.)

Now let’s look at a hashtag around a different sort of event – the Isle of Wight #Bestival. Here’s a sample from that hashtag community:

In this case, we see lots of small blue dots, disconnected from other folk in the network. A couple of nodes are well connected, such as @ventnorblog, the Isle of Wight’s hyperlocal news site. Generally, if we wanted to broadcast a message to the #bestival hashtag community, the only way we could hope to would be by tagging a message appropriately and hoping they had a search running on that tag.

If we run the Gephi connected components tool, we can group nodes that are comnected to each other. In the image below, the large blue circle is the “collapsed” network centred around ventnorblog and redfunnel (the table of the left shows that the majority of twitterers sampled fall into this group). Another, smaller network, shown in exploded form, has also been identified:

Now let’s go back to the cam23 community, and consider the sociability of everyone in the community. In the following image, node size is prooportional to the total number of friends, and colour is proportional to the total number of followers:

So red nodes have a large number of followers/wide broadcast reach outside the hashtag community, and large nodes show that the node has a wide hinterland, and receives messages from a large number of folk outside the hashtag community.

If we now plot “1-total_friends” as the node size and in-degree/incoming links as the colour (incoming links are links from followers), we can get an indication of the extent to which the tweets an individual sees are dominated by tweets from the hashtag community (size) – that is, large size means the person’s network is dominated by folk in the hashtag network – and the extent to which the a person’s tweets reach out into the hashtag network (colour; more red means that person’s tweets are seen by more of the hashtag community).

Small red nodes mean that a person has wide reach into the hashtag community, but that they follow a lot of other people, so hashtagged tweets may be drowned out. Large red nodes show that a person’s friend network is dominated by the hashtag community and that they are widely followed within it.

(Note that originally there were a couple of nodes that looked like “rogue” nodes, so I sized all nodes with zero incoming links to zero size; alternatively I could have filtered the graph to only show nodes with at least one incoming link.)

PS in addition to explicitly opt-in communities, such as hashtag networks, it strikes me that we could also start considering the structure of incidental/passive inclusion topic networks by searching for folk who are using particular key terms, rather than searching over hashtags?)