The plot above is an interactive network of the NC state senate’s Twitter follower network. Each node (dot) is a NC state senator. The color represents which the senator’s political party: red for Republican, blue for Democrat. The size of the node represents how many Twitter followers (in total, not just from other state senators). Therefore, the largest nodes are the senators who have the largest follower networks.
The table below shows the top 5 senators by the number of Twitter followers. Senator Jeff Jackson (Democrat - District 37) has the largest number of Twitter followers with 10,515 users. This explains why Senator Jackson’s node is the largest in the network. Senator Phil Berger (Republican - District 26) is 2nd with 8,951 followers. Therefore, one could argue that Senators Jackson and Berger have the widest network influence as measured by the number of followers.
| Senator (District) | Party | Followers | Posts | Friends | |:-------------------|:----------|----------:|------:|--------:| |Jeff Jackson (37) |Democratic | 10,515| 3,816| 1,452| |Phil Berger (26) |Republican | 8,951| 1,824| 260| |Andrew Brock (34) |Republican | 4,027| 4,833| 3,740| |Mike Woodard (22) |Democratic | 2,664| 1,113| 1,419| |Dan Blue (14) |Democratic | 2,615| 816| 486|
Follower, posts and friend counts are as of Dec 18, 2016.
Each edge (line) represents Twitter followship. Therefore, connections represent whether a given senator follows or is followed by his or her Senate colleagues. Senators with a lot of connections represent those who follow many of his or her colleagues while those senators with few edges follow few colleagues. Related, the position of each senator is a function of the number of connections. For example, Senator Jackson is in the center of the network because he follows nearly all of his Senate colleagues who are on Twitter.
On the other hand, senators who are on the periphery of the network are only connected to the senators they are relatively near. This helps explain why the network is clustered by party (color). On average, senators tend to follow members of their own party more often than members of the opposite party. Exceptions to this are senators that are positioned in the middle (e.g. Senator Jackson, Senator Rabon, Senator Ford, Senator Barefoot).
Feel free to click and observe qualities about the network. Generally, there are more advanced ways of characterizing the network through centrality measures which are measurements of network qualities. For example, the simplest centrality measure is the degree centrality, which simply measures the number of connections.
This network was a quick sketch to see what data I could pull and quick way of representing it. I could’ve gone in more detail (e.g. expanding the network or connecting it with text analysis of each senator’s tweets), but I wanted to keep this first post short.
In addition to the follower network data, I’ve also pulled the senators’ public posts on Facebook and Twitter. As I have time, I want to analyze what topics senators are discussing using topic modeling, a statistical-based family of algorithms for large collection of text documents that identifies topics, clusters of words that co-occur. Stay tuned.