Twinfluence

Influence of a Twitter account does not solely depends on the number of people who are following the Twitter account. It also depends on the size of the network of the followers of Twitter account. Twinfluence applies the concept of social capital to propose a set of new influence measures for Twitter users (from the about page).

Reach: Reach is the number of followers a Twitterer has (first-order followers), plus all of their followers (second-order followers). Reach is a measurement of potential audience and listeners, a best estimate of the number of people that a given Twitterer could quickly get a message to.

Velocity: Velocity merely averages the number of first- and second-order followers attracted per day since the Twitterer first established their account. The larger the number is, the faster that Twitterer has accumulated their influence. Of course, this number could jump significantly with the addition of a few high-profile followers.

Social Capital: Tthe average first-order network of a Twitterer's followers. A high value indicates that most of that Twitterer's followers have a lot of followers themselves.

Centralization: This is a measure of how much a Twitterer's influence (reach) is invested in a small number of followers. Centralization scores range from 0% (completely decentralized) to a theoretical 100% (completely dependent on one Twitterer).

Efficiency: The more people you follow, the more time you have to spend reading and filtering tweets. Efficiency is a measure of how many people a Twitterer had to follow in order to build up their reach, as a percentage.

A few examples to illustrate these new influence measures.

BarackObama, the most followed Twitter user


SteveRubel, a digital marketer working at Edelman Digital


TwitFacts, the Twitter user I created for this blog



These new influence measures are interesting. They have however also their limitations. Influence should be measured for persons. Barack Obama uses various channels to illustrate his influence : speaches, debates, personal encounters, his website, his Twitter account, … On a lower scale everyone is active on different channels. Steve Rubel writes on his blog, he writes columns, he speaks at various events, he works for various clients as a digital marketer at Edelman Digital. On addition to that, he has a stream of Twitter messages. Influence can be measured for each of these channels : number of blog posts linking to Steve Rubel's blog, number of readers and comments on his columns, number of speaking engagements, number of Edelman clients wanting to work with him, ... All these measures are only capable of capturing a tiny aspect of someone's influence. Influence should ideally be measured for a person, not for a specific channel. Twinfluence is however an interesting approach for a specific domain for which no influence measures were available.

Twinfluence uses a clever way to deal with the Twitter API. All requests to the Twitter API are performed with the Twitter user credentials supplied by the user. After several queries, there is a big risk that a user will receive this error message :