It's no surprise that Twitter has reached its tipping point when the number of techies and marketing-folk are easily outnumbered by celebrities using the platform. No longer do I have to start a conversation explaining what Twitter is, suffice to say - everyone knows.

It's common knowledge that the UK's own laureate Stephen Fry is a great advocate of this medium but who would have realised that Philip Schofield was more influential than Downing Street.

When I published the Social Media Index with David Brain that attempted to rank an individuals presence across all platforms, we received a huge amount of feedback. A great deal of this focussed on the fact that what we were listing was popularity and not what is more important which is influence or engagement. The resulting white paper on distributed influence tried to remedy this by analysing measurement in far greater detail.

Understanding that this is also going to 'ruffle a few feathers', I have been helping my colleagues at our consumer arm, JCPR, to apply the principles of SMI to the world of Twitter. The JCPR Twitter Index has been created to list individuals based upon their influence (and not like other tables, their popularity).

Influence can be measured on Twitter using the following formula:

(A full explanation of this is shown in the methodology at the bottom of this post).

What I find truly interesting is how an individuals ranking is completely changed dependent upon whether we are looking at popularity or influence.

For example: Al Gore is rated as the 23rd in the world based upon the number of followers he has. Honestly though, how influential is he? For a man who has only tweeted 29 times (and not within the pat 6 months) - I would say...not at all.

If I was to spend my money trying to get a person to endorse a company I would instead focus on the person who was far more engaged and who other people found interesting. Jonathan Ross in this case would be a great example.

I have listed three tables below, the first of these is the list of top twitters weighted by popularity, the second is by influence and the third by engagement

Top 20 Twitters Weighted by Popularity, Influence and Engagement

Even though this is a bit of fun, there is a serious side behind it. We are always trying to help our clients understand the influence that certain people have over particular sections of society. Behind all the glitz and glamour that goes with fame, it's important to understand where real influence lies, which is often very different to mere popularity. The JCPR Twitter Index helps us define that within the hugely dynamic social media space.

Of course, this index has initially been used to score celebs but its methodology can easily be used to look at other segments (from analysts, musicians, politician and brands). I hope to be publishing the analyst version of this very soon.

Methodology

Following - Twitter lists the number of people each user follows. The tendency for most celebrities is to only follow a few individuals - the more people that someone follows, there is an increased likelihood of them actively participating in conversations with the community instead of simply broadcasting to it. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm.

Followers - Twitter lists the number of followers each user has. Like subscribing to a feed, this is a clear indication of 'popularity' as it requires someone to actively request participation. Follower ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 50) that was used as part of the algorithm.

Updates - How often does someone update what they are doing. This number is purely objective as it scores someone highly no matter what the content of their post (i.e. how relevant is it). Nevertheless it is assumed that if someone posts frequently but has poor content then their 'followers' will decrease. Update ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm.

Name Pointing - e.g. @name - How many people engage in conversation with a celebrity or point to their name. The clearest way to establish this is to run a search on the number of people who reference @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 30) - again this was then used as part of the algorithm.

Retweets - Has a tweet caused sufficient interest that it is worth re-submitting by others? Despite a great deal of 'noise' (i.e. posts that are not relevant or interesting), when someone sees something that is of high interest, their post can be re-tweeted. The clearest way to establish this is to run a search on the number of people who reference RT @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 50) - again this was then used as part of the algorithm.

Twitalyzer - "This is a unique (and online) tool to evaluate the activity of any Twitter user and report on relative influence, signal-to-noise ratio, generosity, velocity, clout, and other useful measures of success in social media." This 3rd party tool is a useful method to combine automated metrics dependent upon criteria within posts and publicly available numbers. Where tools such as this are available, we incorporate them into the algorithm to achieve a more confident score. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twitalyzer noise to signal ratio - Signal-to-noise ratio is a measure of the tendency for people to pass information, as opposed to anecdote. Signal can be references to other people (defined by the use of "@" followed by text), links to URLs you can visit (defined by the use of "http://" followed by text), hashtags you can explore and participate with (defined by the use of "#" followed by text), retweets of other people, passing along information (defined by the use of "rt", "r/t/", "retweet" or "via"). If you take the sum of these four elements and divide that by the number of updates published, you get the "signal to noise" ratio. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twinfluence Rank - Twinfluence is an automated 3rd party tool that uses APIs to measure influence. For example: "Imagine Twitterer1, who has 10,000 followers - most of which are bots and inactives with no followers of their own. Now imagine Twitterer2, who only has 10 followers - but each of them has 5,000 followers. Who has the most real "influence?" Twitterer2, of course." As with Twitalyzer, this index uses 3rd party tools to add greater confidence in the overall Twitter score. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twitter Grader - Twitter Grader is the final automated tool to add greater confidence to the final index. This site creates a score by evaluating a twitter profile. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Involvement Index - As the only personal subjective measure in the algorithm,opinion points were assigned to each celebrity. People who scored highest in this category had frequent, relevant, high-quality content that actively involved the twitter community (asking questions, posting links or commenting on discussions) and did not purely consist of broadcasting. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Velocity Index - As more people engage on Twitter, it may become harder to keep activity going. The velocity index measures changes on a regular basis and assigns a score based on increased or decreased participation. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Weighting - Each specific variable listed above was given a standard score out of 10. Using a weighting scale I varied the importance of the each metric to establish a individuals total score.

Weighted for Popularity - the key variable is the number of people someone has following them. There are many online tools that show this such as Twitterholic.

Weighted for Engagement - the key variables are an individuals participation with the Twitter community (as measured by the Involvement Index), with additional emphasis on the frequency of people name pointing an individual (via @username), the numbers of followers and the signal to noise ratio. Other attributes were included in the final score but were given a lower weighting.

Weighted for Influence - the key variables in this instance is a combination of the number and authority of someone's followers together with the frequency of people name pointing an individual (via @username) and the how many times an individuals posts are re-tweeted. Other attributes were included in the final score but were given a lower weighting.

Criteria for inclusion - There are many lists of top celebrities on Twitter - every one of the these use 'popularity' as its main criteria. Edelman have used all these lists (such as The Times, Celebrity Tweet and Mashable together with the top 100 from Twitterholic and used its algorithm to establish their influence.

As with previous measurement posts I have published, I welcome the community to comment on it and provide your feedback.