The latest in our efforts to quantify the unquantifiable and reduce you to a mere number was launched yesterday. Yet again we brutalise complex human concepts like Trust, Popularity, Influence and Engagement with our over-simplistic (but pretty bloody clever) algorithms. With the Social Media Index (SMI) we did it to try measure ‘influence’ across a variety of social media platforms and now we have had a bit of a think about Twitter. Well, I say “we” but really I mean the fiendishly smart Jonny Bentwood.

Twitter is, as you know, the platform de-jour, but how do you begin to sort through it to find out who is important to you or your client? How can you use it better, particularly if it’s having an increasing role in how you manage relationships? To help us do this across all our Edelman teams and at scale (and dynamically as the twitterverse is developing and changing so rapidly) we have built a tool which we have been using for a while and which from today is open for use by anyone.

It’s called Tweetlevel. It looks at Twitter users four ways:

1. Popularity (how many people follow you – the easy one)
2. Influence (is what you say interesting and how many people listen to it)
3. Engaged (are you actively participating within your community – vs just broadcasting)
4. Trusted (do people believe what you say)

It does all this based on this formula (there’s always a formula or an algorithm or something):

The full methodology is at the bottom of this post. Crudely, though, this is how we come to those different weightings:

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 individual’s 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 and individuals posts are re-tweeted. Other attributes were included in the final score but were given a lower weighting.

Weighted for Trust

The best measure of trust is whether an in individual is will to ‘trust’ what someone else has said sufficiently that they are also prepared to have what they tweeted associated with them. The key metric in this instance looks at combination of retweets and references (shown through ‘via’. Other attributes were included in the final score but were given a lower weighting.

As ever, these are big value judgements and I am sure our labelling of them will raise the usual concerns. It is, we stress, a fairly blunt tool, but it does begin to help you think about twitterers in a way that we have found useful.

The proof of the pudding is always in the eating they say and so here I have taken Valeria Maltoni’s list of 100 PR people worth following on Twitter and put it through Tweetlevel. You can of course put any group or universe of people you follow through it and come up with your own rankings. We routinely do this now across all sorts of sectors and industries for our clients. Valeria listed her 100 in alphabetical order, but I have listed them below in Tweetlevel ranking order. Yes, yes it is list bait, but it is interesting to see where people fall and how they score so differently across the four criteria.

Have a go yourself and as ever let us know what you think.

Name Influence Popularity Engagement Trust


1


ginidietrich


80.6

59.2

87.2

72.8


2


steverubel


74.9

67.6

60.6

69.1


3


arikhanson


71.3

55

77.3

57.5


4


shonali


70.8

55.3

79.5

57.8


5


zoeyjordan


70.7

52.6

72.7

58.7


6


BethHarte


70.1

61.5

76.3

64.6


7


kamichat


68.4

55.3

72.1

56.6


8


briansolis


68

69.6

52.5

75.8


9


prblog


67.3

61.7

58.7

56.6


10


shel


66.3

57.2

70.8

55.4


11


laermer


66

59.4

44.9

61.9


12


CubanaLAF


64.7

53.3

73.2

56.9


13


PRsarahevans


64.4

68.6

57.6

66.1


14


rachelakay


64.1

57.2

71.2

50.6


15


skydiver


63.5

72.2

40.8

68.3


16


ikepigott


63.4

54.5

67

51.9


17


TDefren


63.3

61.9

58.6

57.5


18


jasonkintzler


63.3

59.6

63.4

52.4


19


DougH


62.8

64.7

62.5

54.5


20


trevoryoung


62.6

54.4

58.4

49


21


mikeschaffer


62.5

48.8

61.9

50.4


22


dbreakenridge


62.2

55.3

64.4

50.2


23


elizabethsosnow


61.5

53.8

56.7

52.1


24


kmatthews


61.5

52.3

67.3

49.2


25


GeoffLiving


61.3

58

64.9

53.3


26


Steveology


61

67.9

50.3

62.5


27


dmscott


60.7

68.1

57

61.3


28


dmullen


60.7

54.8

70

45.5


29


davefleet


60

57.8

66.6

57.8


30


BarbaraNixon


59.4

57

61.2

57.2


31


jspepper


58.9

59.4

72.6

47.4


32


jpostman


58.5

58.9

53.3

45.3


33


martinwaxman


58.4

50.6

58.5

43.7


34


jangles


58.1

56.4

55.3

56.5


35


leehopkins


58

52.9

61.2

37.1


36


alanweinkrantz


57.7

54.1

56.2

42.4


37


DoctorJones


57.5

52.4

69

46.9


38


MikeLizun


57.5

55

49.3

50.1


39


davidparmet


56.9

53.5

72.1

39.9


40


charshaff


56.8

49.8

60.1

41.9


41


ryananderson


56.7

51.4

61.5

43.9


42


paullyoung


56.5

53.6

54.2

42.9


43


wiredprworks


56.5

56.8

58

45.3


44


cbasturea


56.4

50.3

60.1

40.5


45


kdpaine


55.6

57.5

68.4

52.5


46


DannyBrown


55.6

63.3

73.3

53.2


47


RichBecker


55.2

50

63.6

45.7


48


brittanymohr


54.8

49.3

57.1

40.2


49


drewb


54.7

54.8

51.2

54.8


50


princess_misia


54

50.4

53.1

44.5


51


PRwise


53.6

57.1

39.7

55.8


52


KarenRussell


53.2

51

67.9

47.1


53


DebInDenver


52.6

48.6

64.7

39.7


54


johncass


52.2

55.5

61.5

45.8


55


thornley


52.2

55.8

49.6

48.9


56


BillSledzik


51.7

47.9

64.4

36.2


57


benrmatthews


51.7

51.4

51.9

47.9


58


stevemullen


51.6

50.7

53.5

38.1


59


PeterHimler


51.5

50.3

49.1

42.4


60


CathyBrowne


51.2

53.8

72.4

28.9


61


jbell99


51

53.2

47.9

37


62


DavidBrain


50.9

51.2

44.2

47.3


63


cherissef


50.4

45.7

48.3

37.2


64


khartline


50.1

55.1

57.8

33.8


65


leeodden


49.5

64.4

55.7

31.2


66


wadds


49.4

50

57.8

42.1


67


perfectporridge


49.4

53.4

43.2

36.2


68


AdamSinger


49.3

53.7

42.4

44


69


CatrionaPollard


49.1

50

44.9

43.1


70


jackmonson


49

48.1

51.1

33


71


ThePRDoc


48.5

46.2

43.5

43.8


72


LindsayLebresco


48.2

49.6

60

31.6


73


domw


48.1

49.4

56.4

39.7


74


jedhallam


47.6

50.3

54.7

38


75


simoncollister


46.6

50

58.3

37.7


76


sherrilynne


46.1

49.1

46.9

37


77


missusP


45.6

63.9

53.9

43.4


78


KellyeCrane


45.5

55.9

51.3

46.7


79


LuAnnGlowacz


45.3

44.2

50.4

33.8


80


LukeArmour


44.8

49.4

49.5

28.4


81


tpemurphy


44.1

47.1

47.6

36.4


82


ShaneKinkennon


43.9

45.1

46.2

32.8


83


andismit


43.9

47.8

53.5

32.8


84


RTorossian5wpr


43.7

47.4

27.8

45.7


85


stuartbruce


42.7

52

46.1

34.7


86


Paul_Stallard


42.6

48

44.8

37.1


87


GRIPCOMMPR


42.4

46.6

50.6

25.9


88


KyleFlaherty


42.2

51.5

49.8

40


89


SarahWurrey


41.6

50.6

47.1

24.5


90


hyku


41.4

54

32.7

23.1


91


bmcmichael


40.2

48.4

49.5

23.6


92


ealbrycht


39.6

49.7

45.5

20.2


93


mmanuel


38.6

49.7

44.6

19.4


94


csalomonlee


38.4

47

51.2

26.2


95


gdugardier


37.2

44.2

32.1

24.8


96


mpwatson


35.7

45.9

45

21.4


97


robskinner


29.4

42.5

32.7

18.6


98


davidreich


27.3

42.2

43.6

16.8


99


samoakley


23.9

45.5

39.3

17

Full Methodology (or, ‘the science bit’)

TL TwitterLevel Rg Range assigned to score
Fo Number of followers Fg Number users following
Up Number of updates @U Number of name pointing
Rt Number of retweets Ta Twitalyzer score
TaN:S Twitalyzer noise to signal ratio Ti Twinfluence score
Tg Twittergrader score Ii Involvement index score
Vi Velocity index score w Weight assigned to each attribute
Z Standardised score p Popularity
e Engagement i Influence
t Trust

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 an individual’s 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 individual’s 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 and individuals posts are re-tweeted. Other attributes were included in the final score but were given a lower weighting.

Weighted for trust – the best measure of trust is whether an in individual is will to ‘trust’ what someone else has said sufficiently that they are also prepared to have what they tweeted associated with them. The key metric in this instance are a combination of retweets and number of followers. 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 these use ‘popularity’ as its main criteria. Edelman have used all these lists (such as The Times, Celebrity Tweet and Mashable together with other ‘interesting’ names and used its algorithm to establish their importance.

See, told you it was clever!