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!

With grateful thanks as ever to Hugh MacLeod.

PR Week’s front page story on the CIPR finances is a little disturbing. The statement on the CIPR web-site is not much more reassuring. I have been critical in the past of some of the stances the organisation has taken, but it is one of the two bodies that is supposed to represent our industry and does a lot of good in terms of basic training. According to PR Week it is scheduled to make a loss of £700,000 this year of which £500,000 is a one-off charge related to moving headquarters. The problem for the CIPR is that many of its members will be very good managers of small businesses and will be looking at this development with some surprise and little sympathy. Having a good view of revenues and keeping costs in line are the first two financial skills of anyone in the PR agency world. I bet most agency owner CIPR members can tell you to within pennies what their revenues will be for the next few months and probably sign all the cheques that go out of the business themselves. And these people will be asking:

* What scale of revenue drop could see the organisation plunge into a £200,000 trading loss (netting out the one-off move costs from what sounds like
an extraordinary sequence of events with their landlord)?
* How frequent and how accurate is the revenue forecasting? “At no point did we forsee a loss of that magnitude”, said Kevin Taylor. Why not?
* How quickly did the organisation go into cost cutting and containment mode?
* How many people (surely the highest cost with PRCA) have had pay cuts and how many have already been made redundant?
* When will the CIPR return to profit and if this is not within the next couple of months, please see above point. “We would hope that next year we
would return to profit”, said Kevin Taylor. Hope! How many CIPR members running offices of multinationals would dare tell their bosses they “hoped”
to be in profit next year?

If this seems a harsh post it is because this industry’s ability to manage itself well is a necessary basis for its credibility. We have to be able to run our own businesses to be taken seriously by our clients and colleagues if we want a place at the table where they manage theirs. And we have done so much better this recession than last. The PR companies in the listed advertising agency groups are often the sparkling financial performers (look at exceptionally well-run Weber Shandwick in the dreadful mess that is IPG). The UK listed firms Chime and Huntsworth have produced good results in the downturn and we all benefit when their experienced and eloquent business leaders talk about how they manage for growth and for profit on a grown-up financial stage. This news seems a throwback to the eighties when the industry had much less business acumen and agencies would pop up and then go bust hurting clients, employees and business partners.

I know the CIPR is not in business to make a profit, but it has to be run in a business-like manner to be taken seriously. My advice to the CIPR is get advice from your members who run their own PR businesses, because you need to fix this problem fast for the sake of all of our reputations and “hoping” to be in profit next year is not exactly a stretch target.

Jere Sullivan from David Brain on Vimeo.

Dylan said it best, “Times, they are a changing”. That is surely the case when you look at what is happening in the world of public affairs. We just completed the Capital Staffer Index, a Trans-Atlantic study (Berlin, DC, Brussels, Paris and London) of senior congressional and parliamentarian staffers on their use of digital media to research and shape policy and communicate with constituents. We identified some interesting findings.

Blogging has gone mainstream. The novelty of having a blog – no matter who you are – has become background noise. However, with the continued free fall of newspapers and news outlets around the world a great opportunity has arose for a host of serious, well informed and ethical online outlets to are provide content and aggregate news. Our research showed that nearly 40% of staffers are using blogs to monitor policy-related news and opinions on policy issues. And nearly half view blogs as an effective means to communicate with constituents.

An “about face” in PA. The most effective way to communicate with staffers and their members is still face to face meetings (96%). Nothing earth shattering there. But, face time has taken on a new meaning with staffers – not just the entry level college grads, but senior policy making staff – who are turning to Facebook. 60% are using it for personal connections, but nearly 30% are using it to connect with professional colleagues, one in five are using it to communicate with constituents and one in ten to research policy issues.

All politics, and digital media, is local. When asked which sites staffers turn to first thing in the morning they revealed it is primarily their local mainstream online outlets such as BBC in London. Other online sources such as dedicated political blogs and their respective government policy sites also emerged as points of interest. The one exception to the rule was Google/Google News which was the only site that was accessed across all markets.

An opportunity to fill the Digital Advocacy Gap. The findings demonstrated that there is a real appetite for digital media as a research and engagement tool. It is no longer a question of if I should use digital as part of the PA mix, but rather how I should use it.

Original article at McKinsey Quarterly

Now as a firm we bang on about the need for public engagement on issues of import, but in the real world and face-to-face it can sometimes be quite a charged thing. Politicians campaigning door-to-door and CEOs doing town hall meetings on shop floors at difficult times know this very well. Most of us do not. And despite the less than perfect exchange of views that this incident represented, the value of public and person-to-person debate should not be forgotten in this era of digital conversation. It is one thing to unleash a volley via keyboard, another entirely to do it to someone’s face with a crowd around you. I for one will be tuned in with huge interest next week, and I agree with the leader column in today’s Times newspaper that we need to choose carefully the people who will take Nick Griffin on. They certainly need to have more presence than I did this morning and preferably they will be wise and experienced debaters. Westminster has many faults and the media is full again this morning of MPs shooting themselves in the other foot over paying back fraudulently claimed expenses. Here is a chance for us to see a few well chosen men and women earn their corn on the big stage. The brightest and the best from each party need to hone the arguments of freedom and tolerance and demonstrate the value of public debate and show us all what the BNP really stands for. And in the course of doing that, they might remind us what they really stand for.

Hat tip Martin Thomas

Korea has many, many things and now it has Crowd Surfing in Korean. I expect they will declare a national holiday. I have no idea where it’s available so if anyone comes across it please let me know.

Hat tip Stephen Davies.