Core users in the Twitter network
Posted: Mon Dec 23, 2024 9:00 am
We are sharing on this occasion the result of the update of our “Ranking of the most influential Peruvian users on Twitter” which is obtained thanks to our influence score “ Q-Score ”. In addition to the ranking update, we present an additional ranking that shows the “most central” Peruvians in the Twitter network that has been built on the concept of centrality.
"More central" users: users who interconnect the network
The concept of “centrality in a network” refers to the relative importance of one of the components of the network with respect to the others. Let us imagine, for example, two users, each with a very large network of contacts, but between these two networks there is only one person who interconnects them. This person is a “central” person for the network. If this person were to leave the network, then the network would be cut off from communication.
Centrality
Mentions between users generate a complex network
Therefore, the value of a user's centrality does not depend on the minnesota b2b list itself, but rather on how this user is located in the network, what links they maintain and the value of those links, always from the perspective of the network itself.
Among the metrics widely used to measure the centrality of an element ("or node") within a network, there is the eigenvector centrality model, which implies that those nodes (in this case Twitter users) that obtain a higher centrality value are those users who are connected to many other users who are also well connected.
The "central nodes" are then the ideal candidates for disseminating information and ensuring that the appropriate channels exist (which in this case are the relationships between users) so that the information can reach the largest proportion of the network.
Study: Peruvian users more central on Twitter
TECHNICAL SHEET
Universe: To conduct the study, we began by selecting the 5,000 most influential users according to the "Q-Score" they received. Additionally, the 5 users that each of them mentions the most were included.
Data collection period: December 23-27, 2013. The best-known companies, organizations, and institutions were eliminated from the map in order to focus on individuals.
Data considered: Last 100 tweets from each of the 5,000 users (500,000 tweets analyzed).
Once the universe is built, we use a tool to identify communities within the main network, each community is given a different color making the total network look like this:
Twitter Neighborhood in Peru
Interactive network: you can visit it by clicking here (wait 10 seconds)
Once the network and its communities have been built, we move on to identify the Top 50 users who have a high degree of eigenvector centrality, who are very important for the network but are not necessarily the most influential. To do this, we eliminate from this list those who are already represented in the list of top influencers.
Rankings
We then proceed to present 2 rankings that we hope will be useful to identify both the most influential users, as well as the "central" users who in this case can be understood as the "Influencers within the influencers."
"More central" users: users who interconnect the network
The concept of “centrality in a network” refers to the relative importance of one of the components of the network with respect to the others. Let us imagine, for example, two users, each with a very large network of contacts, but between these two networks there is only one person who interconnects them. This person is a “central” person for the network. If this person were to leave the network, then the network would be cut off from communication.
Centrality
Mentions between users generate a complex network
Therefore, the value of a user's centrality does not depend on the minnesota b2b list itself, but rather on how this user is located in the network, what links they maintain and the value of those links, always from the perspective of the network itself.
Among the metrics widely used to measure the centrality of an element ("or node") within a network, there is the eigenvector centrality model, which implies that those nodes (in this case Twitter users) that obtain a higher centrality value are those users who are connected to many other users who are also well connected.
The "central nodes" are then the ideal candidates for disseminating information and ensuring that the appropriate channels exist (which in this case are the relationships between users) so that the information can reach the largest proportion of the network.
Study: Peruvian users more central on Twitter
TECHNICAL SHEET
Universe: To conduct the study, we began by selecting the 5,000 most influential users according to the "Q-Score" they received. Additionally, the 5 users that each of them mentions the most were included.
Data collection period: December 23-27, 2013. The best-known companies, organizations, and institutions were eliminated from the map in order to focus on individuals.
Data considered: Last 100 tweets from each of the 5,000 users (500,000 tweets analyzed).
Once the universe is built, we use a tool to identify communities within the main network, each community is given a different color making the total network look like this:
Twitter Neighborhood in Peru
Interactive network: you can visit it by clicking here (wait 10 seconds)
Once the network and its communities have been built, we move on to identify the Top 50 users who have a high degree of eigenvector centrality, who are very important for the network but are not necessarily the most influential. To do this, we eliminate from this list those who are already represented in the list of top influencers.
Rankings
We then proceed to present 2 rankings that we hope will be useful to identify both the most influential users, as well as the "central" users who in this case can be understood as the "Influencers within the influencers."