NHS twitter league table

23Aug10

tweetlevel

The NHS have certainly noticed the potential of social media and are proactively using this channel to engage with their community. I’ve been following this conversation for a while (under the tag #nhssm).

Anyway, it didn’t take much for me to notice a friendly bit of rivalry between the different trusts to see who was doing best using the TweetLevel scoring system. Without further ado, you’ll find below the current league table – if a name isn’t included that should be then let me know.

Account Influence Popularity Engagement Trust
1 royalmarsden 57.2 46.2 57.5 40.3
2 TheChristie 54.2 44.4 56.3 38.4
3 NNUH 54 47.5 62.6 36.1
4 NHSPeterborough 42.7 44.9 52 29.1
5 nhssm 42.7 35.1 60 33.4
6 NHSSalford 42.2 42.8 52.9 25.4
7 nhsconfed 42.2 45.5 32.6 25.3
8 NHSHounslow 41.6 41.1 38.2 26.8
9 NHSBradford 40.5 40.9 42.7 28.4
10 allofusinmind 38.8 37.3 35.9 23.1
11 NHSSouthWest 37.8 43.6 32.6 23.2
12 NHSCumbria 37.3 41 34.3 20.7
13 OBMH 36.5 42.6 30.7 20.5
14 NHSBEN 35.2 43 28.2 18.5
15 NHSPlymouth 35.2 44.6 33.5 19.9
16 nhslincolnshire 34.5 45.8 27.4 19
17 nhslambeth 34.5 42.4 32.2 19
18 NHS_Havering 33.3 43.8 38.4 18.5
19 StGeorgesTrust 32.9 35.1 37.5 17.9
20 heartofengland 32.8 31.8 40.8 18.3
21 nhsdoncaster 32.5 43.1 31.9 17.9
22 NHSSurrey 32.4 33.4 40.6 16.5
23 nhsbarnsley 32.1 43.4 34.9 18.1
24 NHS_Southampton 32.1 42.6 24.3 18.1
25 NHS_Lothian 31 39.2 27 17.1
26 NHSLondonSHA 30.7 47.4 25.2 19.1
27 NHSMiltonKeynes 30.6 42.2 27.7 17.2
28 NHSNorthants 30 43.1 25.8 17.2
29 NHSLCR 29.4 37.1 5.5 16
30 NHS_Sheffield 29.2 40 23.3 18.3
31 NHS_Enfield 29.1 42.4 22.3 17.3
32 SALFORDSELFCARE 29 36.4 25.4 15.3
33 NHSRotherham 29 40.9 31.5 17.4
34 NHSHaringey 28.7 42 22.7 18.6
35 NHSYorks_Humber 28.3 41.5 24.6 16.8
36 NHSNorfolk 28 43.2 1.1 17.5
37 NHSWebteam 27.4 32.2 31 14.5
38 Buckshosp 27.3 40.9 20.5 16
39 NHSLeicester 27.3 43.6 3.7 17
40 cotcpd 27.1 28.8 34.1 14.2
41 NHSWakefield 26.8 37.9 21.1 17.1
42 NHSNorthEast 26.5 41.3 24.4 16.5
43 NHSManchester 26.2 46.6 18 17.9
44 NHSDevon 26 37.1 26.3 15.2
45 camdenprovider 25.8 43.1 24.2 16.2
46 WestMidHospital 25.7 34.5 22.2 14.1
47 NHSWandsworth 25.6 40.9 4.4 15.8
48 NHSNorthofTyne 25.6 42.3 3.7 16.2
49 NHSGreenwich 25.3 38.6 6.6 14.9
50 Bartsandlondon 25.1 39 5.1 15.2
51 NHSNottsCounty 25 40.4 3.7 15.6
52 NHSOxfordshire 24.2 38.5 4.8 14.8
53 SheffieldHosp 24.1 40.8 16.8 16.1
54 nhscalderdale 23.9 33.2 24.3 13.6
55 NHSWCheshire 23.4 32.7 23 13.5
56 NHSIslington 23.1 37.5 19 14.5
57 NHSSuffolk 22.9 36.7 4 14.1
58 LewishamHealth 22.9 36.4 27.4 14
59 NHSCentralLancs 22.7 32.7 20.2 13
60 NHSBarnet 22.5 39.4 16.5 14.8
61 NorthBristolNHS 22.2 29.8 18.6 12.6
62 nhsfife 22 32 20.9 12.9
63 NHSnews 22 28.5 23.5 11.8
64 SWLondonNHS 21.9 29 39.6 12.2
65 NHSBrightonHove 21.8 42.1 19.4 15.3
66 NHSWestKent 21.7 26.1 44 11.5
67 NHSCambs 21.5 37.1 18.7 13.7
68 NHSLanarkshire 21.4 33.3 4 13
69 nhswestessex 21.4 42.4 22.9 15.9
70 TraffordHealth 21.2 37.6 2.2 14.1
71 NHS_Southwark 20.8 30 19.8 12.4
72 NHSIC 20.5 39.7 19 14.1
73 ESHospitalsNHS 20.3 38.5 0 14.4
74 NHSGlos 20.3 34.9 17.7 13.2
75 NHSSTOKE 20.1 36.1 3.7 13.2
76 NHSCIOS 20.1 40.5 2.2 14.5
77 PHNT_NHS 20 37.2 5.1 13.2
78 NHSLiverpoolCH 19.4 39.2 5.9 13.3
79 WHHNHS 19.2 38 23 13.8
80 NHS_Warrington 18.9 34.7 16.4 12.6
81 NHSBwD 18.8 37.8 20 13
82 NHSLuton 18.8 39.1 31.8 14.1
83 NHS_Hillingdon 18.5 39.3 3.7 13.6
84 swastFT 17.8 31.2 17.7 11.5
85 nhsleeds 17 25.5 21.3 10.8
86 BromleyNHS 16.9 41.5 23.2 14
87 NHSCITYHACKNEY 16.7 35.2 18.6 12.3
88 MKCHS 14.9 32.7 7 10.5
89 NHSKingston 14.5 20.9 4 7.7
90 LiverpoolPCT 12 23 4.4 7.7
91 imperial_nhs 11.6 36 0 11.3
92 NHSDorset 9.3 27 2.2 8.2
93 NHSMK 8.4 23 3.7 6.6

Algorithm and Methodology tmp9CD

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. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.

Followers – Twitter lists the number of people that follow each user. Like subscribing to a feed, this is a clear indication of ‘popularity’ as it requires someone to actively request participation. Even though TweetLevel has a ranking of people based upon popularity, it is influence, engagement and trust that is more important. Due to the nature of logarithmic ranges, a change in the number of people that follow someone, such as from 500 – 1000, will give a far higher change in score than a move from 180K – 200K. 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. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user. Since the initial creation of TweetLevel, we have now been able to incorporate Twitter Lists into this part of the algorithm. Someone’s follower score will increase depending upon the number of times a user is included in a list, the number of people who follow that list and the authority of those people.

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 ratioSignal-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 RankTwinfluence 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 GraderTwitter 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 – The Involvement Index is unique Edelman IP that calculates a score based upon how an individual engages with their community. It is calculated by analysing the content of an individual posts. People who score highest in this category have 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. In the true spirit of ‘open sourcing’ this work, I welcome your comments, views and criticisms in how this approach can be as accurate as possible. Whereas I don’t believe for one moment that TweetLevel has found the holy grail of social media measurement, I think it is a good step forward and look forward to discussing this with you.       



4 Responses to “NHS twitter league table”

  1. Jony this is great. I ran a training course last year for health care professionals and introduced them to the concept of Twitter, they found it interesting but couldn’t work out the relevance. If I ever run the course again, the league table will feature. Really interesting stuff.

    Marc

  2. Really interesting. You seem to have left @OxfordRadcliffe off the list (maybe we should have put an NHS in there.)
    We should be in there at number 35 according to http://tweetlevel.edelman.com/user/OxfordRadcliffe
    Clearly we need to try a little harder…

  3. Hi Jonny,

    Thank you for putting this together. It is great to see the hard work people are putting into social media being recognised.

    If anyone is interested in how discussing social media and the NHS please join in the #nhssm community. Check out the posterous blog for more🙂

    Cheers,
    Alex

  4. 4 Emma

    NHS Choices and NHS Direct not included????


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