How people use Twitter – 10 distinct usage groups

In this post we will look at another example of cluster analysis performed on Twitter. The analysis was performed on 17000 Twitter users with the goal of extracting distinct groups of usage which essentially shows us the different types of Usage behavior of Twitter users. The following parameters were taken under consideration :

• Number of Followers

• Number of Links posted per 20 Tweets (not during RT)

• Number of Updates

• Elapsed Days

The following table shows the results :

Note that each cluster has a specific number from 1 to 10. Clusters are listed according to their size, which means that cluster “10” is the largest usage group, while cluster “5” being the smallest.

Let’s see what the table tells us, starting with the first line: Cluster 10, is the largest (=more frequent) type of usage behavior. Users of that group have an average number of followers; have been using Twitter for relatively many days (elapsedDays=high); have a high number of updates; while the number of links they provide per 20 tweets is average – say around 3 links.

Now consider – highlighted – Cluster 8, which we will call The Information providers: Notice that even though this group of users have relatively few…

In this post we will look at another example of cluster analysis performed on Twitter. The analysis was performed on 17000 Twitter users with the goal of extracting distinct groups of usage which essentially shows us the different types of Usage behavior of Twitter users. The following parameters were taken under consideration :

• Number of Followers

• Number of Links posted per 20 Tweets (not during RT)

• Number of Updates

• Elapsed Days

The following table shows the results :

Note that each cluster has a specific number from 1 to 10. Clusters are listed according to their size, which means that cluster “10” is the largest usage group, while cluster “5” being the smallest.

Let’s see what the table tells us, starting with the first line: Cluster 10, is the largest (=more frequent) type of usage behavior. Users of that group have an average number of followers; have been using Twitter for relatively many days (elapsedDays=high); have a high number of updates; while the number of links they provide per 20 tweets is average – say around 3 links.

Now consider – highlighted – Cluster 8, which we will call The Information providers: Notice that even though this group of users have relatively few elapsed days and average number of updates, they achieve a High number of followers. The reason is that these users provide a large number of links per 20 Tweets ( Note that this confirms findings during a previous analysis).

See also Cluster 3: Even though this group of users has been on Twitter for many days and also has a high number of updates, it appears that it pays a price for not providing links.

Recall that the “#OfLinks” parameter counts only these links that are NOT part of a Retweet. This tells us that users that are able to find original content and provide it to the community tend to gain more followers.

This analysis was given with the aim of providing a simple example and should not be considered as a detailed analysis since few parameters have been taken into account. Cluster Analysis on Twitter data (which include things that people like doing, professions, interests, marital status, mention of products or opinions to name a few) can – potentially – give us excellent insights on different aspects of user behavior.