Top analyst tweeters (via TweetLevel)

22Feb10

Who are the analysts who use Twitter the most effectively?

Using SageCircle’s excellent database combined with a good number of my own I have used TweetLevel to understand who are the most important analysts on Twitter. These names are included on the following Twitter list – link

This unique tool compiles twitter data from over 30 sources and feeds the data through an algorithm to rank an individual according to four weightings:

1. Popularity (i.e. How many people follow you)
2. Influence (i.e. What you say is interesting, relevant and many people listen)
3. Engaged (i.e. You actively participate within your community)
4. Trusted (i.e. People believe what you say)

Of course, the explanation above is a simplified definition of a complex algorithm(the full methodology of this is shown at the bottom of this post).

How TweetLevel can be used?

I use this list by looking at a micro topic area (such as SaaS) and understand who out of the 950+ analysts on Twitter are the ones that truly use this as a conversation tool. I am normally left with ten names who I now know are critical to engage with. What counts is that if my analysts use Twitter as a medium to engage in conversations then I need to know who they are and take part in the discussion with them.

(Blatant self-promotion – if you want me to sort a list for you that is automatically updated for your specific area (that can be as broad or as specific as you want – from BI to Starbucks) then let me know.)

Ranking Criteria

The primary ranking metric is influence. However, it is interesting to see that when we analyse the same 950 names by influence we get a completely different top 10 list. Congrats to Michael Gartenberg for being the only one to hit both groups.

Findings

Once again a huge hat tip and pat on the back to Jeremiah Owyang who leads the way by a country mile. Everyone should take note how he uses Twitter to engage with his community and provide real value.

For the first time I have now seen a larger number of brands in the top five than individuals. eMarketer, Forrester and EConsultancy all fair extremely well. We should therefore recognise other people who punch well above their weight scoring exceptionally highly even though they are only in smaller firms. Kudos to James Governor (jumps from 5th to 3rd) and Michael Krigsman (new entry at 7th).

Even though the influence is the primary ranking metric, I also like to look at those analysts who are most engaged. To have a high level of influence is partially (but not massively) related to popularity. Engagement however is purely related to how someone engages with their community. My praise therefore goes out to Ron Shevlin who even with less than 1,000 followers is especially engaged with his community. This is where TweetLevel excels – normally people like Ron would be ignored on many influencer lists, but in this case it shows that his use of Twitter is exceptional for a particular niche area.

The other key finding from the engagement list is that everyone in the top 10 are individuals not brands. With the recent discussion in the analyst world regarding personal brand versus corporate brand, I will be interested to see whether we will see a move to more company-led tweeting.

Common complaints

Whenever these lists are published, there are several points that always get raised which I will address now…

1. This twitter account is not from an analyst. The argument as to whom is an analyst or a consultant is becoming largely moot. In my opinion if someone is independent and directly influences technology procurement then they are an analyst – I for one therefore see Vinnie Mirchandani as an analyst.  I know this will cause a huge amount of disagreement but as an outsider looking in this is the way I see the market. This is not to say that some analysts have different strengths over others, it is more a case that I think as an AR pro, I need to monitor the lot of you.
2. The twitter handle is written by multiple authors. Some twitter accounts have several analysts writing them whereas others do not. The merits of a single twitter account author is something that I personally favour as this allows me to understand the tone of author without having to understand the many personalities that are associated with it. Regardless, for this table, my view has not been to argue this but merely to present the data.
3. It is irrelevant showing all the tweeters as I am only interested in a specific topic– bingo, that is exactly right. My suggestion to all AR pros is to identify which of your analysts are on this and only look at those. If you would like to understand who is important for a particular micro-topic area, please let me konw.
4. Hey – you have forgotten to include this list. Please let me know the name and if I will include it as an edit.

Top 10 Analyst Tweeters (ranked by influence and engagement)

Rank	Ranked by Influence			Ranked by Engagement
1		jowyang	73.3		rshevlin	63.4
2		eMarketer	67.2		Gartenberg	63.1
3		monkchips	65		dvellante	62.3
4		forrester	62.9		NeilRaden	61.3
5		Econsultancy	62.5		dealarchitect	61
6		drnatalie	62.4		RayLucchesi	60.3
7		mkrigsman	61.7		dale_vile	59.7
8		rwang0	61.5		richi	59.7
9		Gartenberg	60.8		ekolsky	58.9
10		mfauscette	60.4		storageio	58.8

Top 500 Analyst Tweeters (ranked by Influence on TweetLevel)
These names are included on the following Twitter list – link

		Account	Influence	Popularity	Engagement	Trust
1		jowyang	73.3	71.3	58.2	77.6
2		eMarketer	67.2	63.8	41.8	79
3		monkchips	65	58.3	51.5	61
4		forrester	62.9	67.8	45.3	59.8
5		Econsultancy	62.5	63.4	41.8	68.3
6		drnatalie	62.4	56.8	46	56
7		mkrigsman	61.7	55.8	55.8	55.8
8		rwang0	61.5	56.4	48.5	55.5
9		Gartenberg	60.8	55.8	63.1	61.4
10		mfauscette	60.4	57.1	41.8	56.9
11		jesus_hoyos	59.6	55	45.8	54.7
12		augieray	58.6	53.6	53.8	50.6
13		ekolsky	57.9	47.4	58.9	52.1
14		merv	56.6	48.6	54.1	49.6
15		richi	55	54.7	59.7	44.9
16		charleneli	54.9	67	52.1	44.7
17		bmichelson	54.7	47.6	54.6	45.4
18		debs	54.5	56.5	57.1	41.9
19		jbernoff	54.1	59.2	54.4	43.9
20		gyehuda	54	50.9	52.7	43.8
21		stiennon	53.8	63.6	50.1	42.2
22		cote	53.8	53.1	48.3	42.6
23		denschaal	53.5	50.1	49.2	43.4
24		TomRaftery	53.5	57	53.1	49.6
25		storageio	53.3	46.4	58.8	43.3
26		hharteveldt	53.2	51.7	51	43.7
27		dankeldsen	53	55.2	53.2	39.5
28		graemethickins	52.9	51.5	51.4	41.8
29		Gartner_inc	52.4	64.9	37.3	52
30		sogrady	51.9	49.4	49.8	41.8
31		jameskobielus	51.4	47.6	39.2	42.2
32		lehawes	51.1	45.4	54.1	39.1
33		jberkowitz	51.1	51.6	37.6	42.6
34		jyarmis	51.1	51.1	45.3	39.5
35		nselby	50.8	45	50.5	40.3
36		rshevlin	50.7	44.3	63.4	38.8
37		FocusExperts	50.6	64.4	10	49.1
38		vendorprisey	50.6	47.8	53.8	38.9
39		megheuer	50.4	49	46.9	42.2
40		NeilRaden	50.4	46.2	61.3	43.7
41		stylishidea	50.2	47.3	44.1	37.5
42		VanessaAlvarez1	50.2	48.7	48.4	45.5
43		carterlusher	50.1	51.1	40.8	40.5
44		bobegan	49.6	45.2	44.6	41.4
45		dvellante	49.3	43.7	62.3	42.6
46		johnrrymer	49.2	46.4	40.9	41.7
47		PhoCusWright	49.2	51.2	36.6	48.1
48		rgruia	49.2	47.3	42.3	39.6
49		znh	49.1	46.1	44.6	38.2
50		bitterer	49.1	47.1	53.3	43.2
51		Mark_Mulligan	49	51	51.7	43.6
52		jamet123	48.9	45.4	37.5	42.7
53		john_chilmark	48.9	48.8	47.7	35.2
54		blairplez	48.9	45.7	51	38.3
55		rossrubin	48.9	47.3	57.6	33.9
56		cuttertweets	48.8	46	37.4	42.6
57		gilliatt	48.8	48.9	49.2	36.5
58		dealarchitect	48.7	44.2	61	40.2
59		rmogull	48.4	48.9	53.4	35.3
60		cmooreforrester	48.4	44.3	48.1	43.2
61		cmswatch	48.3	52.4	35.5	41.7
62		stephenodonnell	48.3	54.3	48.3	37.4
63		SethGrimes	48.2	48.1	58.4	43.4
64		idarose	48.2	49.5	43.9	34.8
65		cswolf	48.2	45.5	51.4	38.1
66		fiberguy	48.1	41.4	47.1	40.7
67		jclarey	48.1	52.5	43.7	35.1
68		Enderle	48.1	57.4	44.1	46.4
69		Hitwise_UK	48	58.3	45.7	44.5
70		kasthomas	47.7	61.7	35.5	36.2
71		bkwalker	47.2	48.6	35.4	37.3
72		jonno	47.2	46	55.6	31.2
73		mgualtieri	47.2	46.2	44.3	40.9
74		fscavo	46.8	46.1	42.7	37.5
75		oliviepaul	46.2	37.8	47.6	35.8
76		Claudia_Imhoff	46.1	47.6	50.1	38.7
77		samirb	46.1	48.7	55.6	29.6
78		Hitwise_US	46.1	58.9	37.2	43.5
79		steven_noble	45.8	49.3	48.3	29.2
80		InFullBloomUS	45.7	48.5	41.3	40.3
81		tomcummings	45.3	49	54.9	25.9
82		adamhleach	45.3	40.3	45.6	34.5
83		Jake_Hird	45.1	44.5	39.3	41.1
84		abnerg	44.9	47.1	47.8	31.6
85		JEBworks	44.9	48.2	48.2	36.3
86		jblock	44.7	53.7	37.4	32.6
87		gleganza	44.6	42.5	47.9	36.1
88		markmadsen	44.5	45.4	54.9	33.2
89		vtri	44.4	45.6	45	26.6
90		jhurwitz	44.3	50.9	40.3	33.8
91		gilbane	44.3	48.8	38.9	38.5
92		caostheory	44.2	42.8	0	43.3
93		passion4process	44.1	42.8	45.7	34.7
94		LinusGreg	44.1	46.5	40.3	42.4
95		clivel_98	44.1	43.4	50.1	31.1
96		ianfogg42	44	43.1	38.4	32.7
97		SteffWatson	44	38.8	51.7	34.8
98		stevedupe	44	46.6	34.7	35.1
99		krestivo	43.9	46.6	47.9	26.4
100		stephenmann	43.9	44.1	49.9	29.8
101		JimLundy	43.8	47	45.8	34
102		neilwd	43.8	42	45.2	35.9
103		NigelFenwick	43.7	46.4	52	37.3
104		johnlovett	43.6	46	50	36.9
105		bradholtz	43.6	42.9	36.7	35.8
106		dale_vile	43.5	44.7	59.7	26.5
107		jonathaneunice	43.4	46.8	58.5	27.9
108		the451group	43.4	50.4	24.1	37.5
109		jimholincheck	43.3	44.8	37.2	38
110		bfinucane	43.1	49.3	52.4	28.5
111		ggheorghiu	42.9	45	35.4	31.9
112		maslett	42.9	40.5	37.3	35.4
113		robinbloor	42.9	55.6	31.5	26.8
114		gwoodill	42.7	50.6	43.1	35.1
115		AndreaDiMaio	42.4	44.6	43.9	42.1
116		antallan	42.3	42.5	48.7	25.5
117		nate_elliott	42.3	50.7	37.2	24.6
118		biz_mobility	42.3	41.9	43.9	30.2
119		jeffmann	42.2	46.5	41.4	23.9
120		christineptran	41.9	41.8	47	25.6
121		vgtero	41.8	44.2	34.9	26.7
122		arj	41.8	45.2	52.1	33.6
123		ghaff	41.7	42.5	47.2	31
124		dnm54	41.7	39.1	38.5	29.6
125		jpmorgenthal	41.6	44.3	41.7	25.8
126		glennodonnell	41.6	42.6	48.5	32.1
127		rreitsma	41.5	41.7	35	38.6
128		apoorv	41.4	40.1	44.3	31.2
129		randygiusto	41.4	44.2	32	31.4
130		andreascon	41.4	43.7	33.6	37.1
131		brian_riggs	41.4	42	32.6	34.8
132		tebbo	41.3	43.8	53.2	24.1
133		IDC	41.3	55.5	27.5	42
134		alyswoodward	41.3	38.8	47.5	33
135		CurtMonash	41.2	51.1	41.7	27.2
136		rayval	41.1	48.2	36.8	32.1
137		keith_rng	40.9	43.8	47.7	25
138		rogermud	40.9	42.8	29.6	30.9
139		TonyBaer	40.8	40.8	48.2	33.9
140		s_crawford	40.7	44.8	44.6	23.6
141		mikeferguson1	40.6	37.3	38.1	34.3
142		RayLucchesi	40.6	42.8	60.3	34.4
143		storageswiss	40.5	44.3	35.6	33.6
144		Wikibon	40.4	43.8	30.5	36.8
145		StefanRied	40.4	43.4	30.4	35.7
146		gilbanesf	40.4	44.5	37.8	38.2
147		Dana_Gardner	40.4	50	31.1	33.4
148		etravelproject	40.4	46.4	36.7	28
149		SarahBurnett	40.4	46.5	47.5	29.8
150		s_v_g	40.3	37.8	44	32.1
151		pnjarich	40.3	34.5	36.1	33.3
152		timbo2002	40.2	44	40.1	27.9
153		paulhamerman	40.1	42.4	29.8	34.6
154		douglas_quinby	40.1	44.6	40.7	29.5
155		rschmelzer	40	41.3	44.9	26.6
156		michaelwolf	39.9	41.6	36.4	29.1
157		janovum	39.9	40.2	54.5	22.9
158		timehhh	39.7	41.3	40.5	25.2
159		bevelson	39.6	40.1	37.7	34.3
160		Montejam	39.6	42.8	43.1	28.7
161		jesstsai	39.5	41.9	32.5	25.5
162		pattyinboothbay	39.3	47.8	37.1	26.3
163		jim_techclarity	39.3	40.1	42.3	30.7
164		billtrippe	39.3	46.9	39.4	28.6
165		802dotchris	39.2	43	40.4	24.5
166		paulfroberts	39.2	43.7	35	29
167		infotrends	39.1	45.8	31.1	31
168		dschatsky	39.1	43.6	31.6	24.1
169		davidcard	39.1	46	36.1	24.9
170		TonyByrne	39.1	46.5	38.7	34
171		JeffreyBreen	39	45.1	44.1	25.4
172		martinatherton	39	40.8	45.7	21.1
173		nengelbert	39	42.7	34.9	30.7
174		aschmitt	38.9	37.4	33.7	30
175		adriaanbloem	38.8	39.9	50	25.8
176		jhammond	38.7	43.8	37.7	24.4
177		Thomas_Husson	38.7	44.9	35.2	32.4
178		seuj	38.7	34.8	50.1	19.9
179		mosterman	38.6	41.8	36.5	26.9
180		IDCInsights	38.6	50.2	34.4	32.5
181		CurrentAnalysis	38.5	45	5	33.4
182		ventanaresearch	38.5	44.1	31.4	33.1
183		lauraramos	38.4	51	29.9	24.1
184		btemkin	38.4	45.2	0.7	36.8
185		ironick	38.3	38.5	42.2	25.2
186		darranclem	38.3	41.7	37.6	26.6
187		smulpuru	38.2	46.4	27	30.8
188		KarenYetter	38.1	60.1	10	26.9
189		cmendler	38.1	44.6	44.4	21
190		imlazar	38.1	42.3	43.7	25.5
191		Zak_Kirchner	38.1	41.4	35.3	26
192		lisarowan	38	44.5	31.7	22.5
193		Staten7	37.9	43.3	33	31.7
194		fjeronimo	37.9	37.7	28.9	32.9
195		AlanWebber	37.9	45.8	35.8	33.3
196		NickLippis	37.6	44.2	29	31
197		jalvear	37.5	41.7	42.6	19.9
198		sureshvittal	37.5	45.9	37.6	22.8
199		thinkovation	37.4	42.1	44.5	23.9
200		iSuppli	37.4	45.2	5	32.8
201		pjtec	37.3	37.3	33.3	28.8
202		visionmobile	37.3	43.1	30.2	35.2
203		msbrumfield	37.1	42.5	29.6	26.5
204		YankeeGroup	37.1	47.9	5	35.1
205		allenweiner	37.1	45.5	35.3	21.3
206		stessacohen	36.9	42.2	41.7	25.2
207		erainge	36.8	42.9	32.1	28
208		siriusdecisions	36.8	45.6	36.7	25.8
209		theresaregli	36.7	43.5	38.1	28.7
210		nyuhanna	36.7	48.9	28.9	25.7
211		iglazer	36.7	42.9	42.5	21.3
212		mbrandenburg	36.7	41	42.6	22
213		GigaOMPro	36.6	44.1	30.3	30.2
214		MaribelLopez	36.6	45.9	46.2	23.2
215		FreeformCentral	36.4	43.1	35.6	35.5
216		pragkirk	36.3	44	38.3	27.7
217		omriduek	36.3	40.3	41.5	21.1
218		bsdunlap	36.3	41.2	37.1	23.3
219		Neovise	36.1	38	33.5	31.2
220		SimonBramfitt	36.1	36	47.6	31.3
221		mlees	36.1	39.6	30.3	25.7
222		sliewehr	35.9	37.5	45.8	22.8
223		AndiMann	35.8	47.3	44.5	23.1
224		hkisker	35.8	36.6	26.6	31.8
225		ekmurphy	35.8	38.5	40.6	20.8
226		atmanes	35.7	45.1	32	19.4
227		Jossgillet	35.4	34.2	39.1	25.7
228		bryanyeager	35.4	38.1	32.9	23.5
229		Gabeuk	35.3	41.5	41.9	22.2
230		pfersht	35.2	45.3	44.3	22.9
231		Shawn_McCarthy	35.2	41.8	36.2	30.1
232		joeltweet	35.1	60	41.1	21.9
233		LockTD	35.1	40	42	20.6
234		kmcpartland	35	33.6	34.3	28
235		nealhannon	35	41	35.3	21.9
236		TomGrantForr	34.9	42.1	6.3	27.9
237		epopova	34.8	38.5	27.6	25.3
238		katehanaghan	34.8	40.9	39.6	24.3
239		Mark_Goldberg	34.8	38.2	29.4	26.3
240		iangjacobs	34.8	42.8	34.8	19.7
241		ravenzachary	34.7	46.2	29.6	19
242		Celent_Research	34.6	43.7	28.7	33.8
243		toppundit	34.6	42.2	33.6	22.7
244		PhilippBohn	34.6	42.4	43	23.6
245		jeunice	34.4	39.1	36.7	23.9
246		BurtonGroupIT	34.4	46.5	6.3	28.6
247		cee_m_bee	34.3	41.6	40.5	19.5
248		vvittore	34.3	35	33.5	23.3
249		nsturgill	34.1	34.3	24.2	34.2
250		btudor	34	42.6	42.8	22.9
251		dkusnetzky	34	41.4	44.2	19.4
252		Heuristocrat	34	44	28.9	22.6
253		TKspeaks	33.9	55.5	35.8	30.5
254		rob_bamforth	33.9	36.9	35.4	20.1
255		bmirabito	33.8	50.4	31.1	19.5
256		lauriemccabe	33.7	39.7	31.6	22
257		EricaDriver	33.6	45.2	35	21.2
258		gilbaneboston	33.5	50.9	33.8	19.6
259		Bersin	33.4	52.4	27.8	32.7
260		rbkarel	33.4	40.1	35.1	31.4
261		dfrankland	33.3	44.4	32.7	22.6
262		benwood	33.3	43.7	27.9	26.1
263		joestanhope	33.2	38.4	37.7	19.9
264		pund_it	33.1	37.2	33.6	21
265		mlevitt	32.9	44.1	24.5	19.7
266		mark_koenig	32.8	40.8	41.9	17.7
267		Lciarlone	32.7	43.1	37.1	20.7
268		jgptec	32.6	35.6	7.7	19.9
269		dxmitche	32.6	45.4	34.3	18.6
270		SeanCor	32.5	47.9	40.1	24.1
271		OvumICT	32.5	37.6	3.7	35.2
272		ripcitylyman	32.4	39	7	19
273		demartek	32.4	42.5	33.5	20.3
274		ferrusi	32.4	37.2	32.9	20.9
275		punirajah	32.4	38	33.3	17.4
276		markbowker	32.3	40.8	26.3	30.2
277		PHassey	32.2	35.8	35.5	21.7
278		hyounpark_AG	32	42.8	31.4	20.8
279		ca_bshimmin	31.9	43.3	28.6	21
280		nickpatience	31.9	39.2	33.8	22.9
281		JeffPR	31.9	45.9	33.5	20.7
282		lcecere	31.8	42.5	30.5	24.8
283		esganalysttmac	31.8	42.5	40.1	20.4
284		GaryatWikibon	31.6	39.5	31.9	21.5
285		npdgroup	31.6	49	4	27.7
286		gcolony	31.5	57.1	24.9	21
287		shigginski	31.5	37.3	30.4	17.1
288		Payment_Expert	31.4	49	27.8	29.7
289		jblin	31.3	39.5	26.3	16.7
290		mtauschek	31.2	34.1	36.7	18.9
291		ARC_Advisory	31.1	44	29.7	20.8
292		royillsley	31	42.1	28.2	19.1
293		BarryRabkin	31	43.9	42.5	18.7
294		Burgess_Gary	31	35.4	34.4	26
295		MWDAdvisors	31	34.9	26.5	32.5
296		KeithHumphreys	30.9	44.7	34.7	20.4
297		erichknipp	30.9	38.8	31.1	18.7
298		AndyBrownSA	30.7	31.6	40.3	22
299		Gartnergreg	30.7	41.3	32	20.1
300		nora_freedman	30.7	43.5	42.6	17.7
301		hokun	30.7	39.6	36.7	19.7
302		passingnotes	30.6	42.1	35.7	17.5
303		ABI_4G	30.6	36.4	5	28.9
304		joltsik	30.5	40.6	20	31.5
305		bpmfocus	30.5	39.3	24.6	28.1
306		logisticsviewpt	30.4	40.5	32.1	22.9
307		brucemr	30.3	35.2	29.2	16.2
308		jonathanbrowne	30.3	44.2	33.3	17.3
309		Teresacottam	30.2	38.8	28.2	31.3
310		howett	30.1	32.4	38.1	16.3
311		mbkmbk	30.1	39	29.2	17.4
312		scottn7	30.1	40.1	23.5	16.5
313		katiesmillie	29.9	40.8	31.1	16.8
314		ema_research	29.9	44.8	6.3	22.3
315		jboye	29.8	39.3	30.6	17.4
316		rnantel	29.8	39.8	25.9	16.5
317		msmvnj	29.7	37.7	26.9	25
318		peter_w_ryan	29.7	30.5	38.2	15.8
319		billnagel	29.7	34.5	33.9	17.8
320		minicooper	29.5	45.5	35.4	17.8
321		kevinnolanuk	29.5	40.7	26.1	23.3
322		adamjura	29.4	38.9	38.8	17
323		azornes	29.4	38.4	20.5	19.6
324		cdhowe	29.3	35.2	25.8	21.8
325		markpmcdonald	29.1	39	4.3	20
326		MattRosoff	29.1	44.9	26.8	22.1
327		MarkRaskino	29.1	42.1	29.1	18.1
328		ChristianKane	29.1	39.2	34.7	18
329		carldoty	29	39.1	27.1	19.9
330		pmcginnis	28.9	42.8	7.7	16.1
331		e2theb	28.9	31.3	35.5	20.4
332		chris_townsend_	28.9	42.5	40.9	17.3
333		nickster2407	28.9	40	32.9	16.5
334		Frost_Sullivan	28.9	42.8	5	20.8
335		lauradidio	28.9	37	4.3	21
336		infotrends_mps	28.8	38.6	31.5	16.2
337		fgilbane	28.8	41.4	34.5	24.5
338		weckerson	28.8	41.7	35.2	18.5
339		heidishey	28.7	37.1	34.4	16.3
340		mcgeesmith	28.6	38.1	29.9	16.8
341		ajbowles	28.6	39.7	25.4	18.6
342		stor2	28.5	40.8	25.9	16.1
343		waband	28.4	42.3	25.7	21.3
344		infonetics	28.4	43.9	5.7	22
345		brightfly	28.4	40.7	5	16.8
346		davidmsmith	28.3	40	27.3	19
347		Nemertes	28.3	39.9	6.7	21.8
348		wif	28.3	37.2	26.2	21.1
349		NewsShark	28.2	44.8	25.3	17.5
350		tim_walters	28.2	40.4	30.4	19.8
351		tjkeitt	28.2	38.3	33	17.9
352		VLI	28.1	35.8	36.7	16.2
353		mizkonary	28.1	31	29.1	21.9
354		analystnick	28.1	41.1	31.8	16.3
355		Josh_Bersin	28.1	53.3	26.3	22.6
356		DenisPombriant	28.1	42.8	26.9	21.7
357		TheNakedChief	27.9	35.6	25.4	16.6
358		reastman	27.9	39.6	28.3	16
359		diteb	27.9	42	33.4	16.5
360		EquaTerra	27.9	42.5	22.7	25.7
361		PaulBrown_SA	27.8	36.4	31.9	16.8
362		jmcquivey	27.8	44.4	34.4	17.3
363		ePubDr	27.7	33.5	26	18.8
364		rodnelsestuen	27.7	30.6	22.5	26.6
365		D_Hong	27.6	42.7	28.2	17.2
366		MWardley	27.6	33.1	23.1	26.2
367		mgrey	27.5	39.4	19.7	15.9
368		billtancer	27.5	39.4	37	18.7
369		darrenbibby	27.3	39.7	23.6	15.8
370		tamarabarber	27.3	32.4	26.6	22.1
371		chinamartens451	27.3	33.2	21.5	16.3
372		adriandrury	27.2	32	25.8	18
373		Canalys	27.2	40.7	4.7	22.2
374		jctec	27.2	32.3	26.1	18.3
375		nigelwallis	27.2	33.1	37.3	14.9
376		Horses4Sources	27.2	41.9	27.4	24.1
377		prussom	27.1	33.7	28.9	22.7
378		jjegher	27	42.1	32.7	16.3
379		rwhiteley0	27	40.4	29.7	19.5
380		georgetubin	27	26.4	23	27.2
381		CXPOeilExpert	27	36.6	5.7	17
382		heyshiv	26.9	35.2	44.1	15.3
383		securityjeff	26.9	37.3	37.7	15.6
384		Andrew_Boyd	26.9	46.4	32.7	19.2
385		Hitwise_AP	26.8	51.1	8.7	18.2
386		jcapachin	26.7	38.9	28.1	15.3
387		acfrank	26.7	41.1	24.9	15.5
388		whita	26.6	37	40	15.7
389		TGView	26.6	36.8	26.9	17.4
390		sjschuchartCA	26.6	38.9	46	16.9
391		stavvmc	26.5	32.8	34.7	14.7
392		markhrobinson	26.5	41.3	23.8	15.5
393		michaelgreene	26.4	38	27.4	19.1
394		dmavrakis	26.3	37.8	6	14.6
395		chrischute	26.3	32.3	26.6	17.7
396		Craw	26.3	41.3	29.6	17.8
397		instat	26.2	40.7	21	18
398		REdwards	26.2	41.1	22.2	18.5
399		bseitz	26.2	31.6	36.4	14.1
400		Telesperience	26.2	39	22.8	18.5
401		kreidy	26.2	41.4	38	16
402		dfloyer	26.1	33.4	3.7	18.3
403		CRozwell	26	41.9	30.2	17
404		mobilesimmo	26	37.3	24.6	15
405		tonyiams	26	31.6	22.4	26.5
406		zmcgeary	26	43.2	39.3	16.1
407		drjimmys	26	35.9	20.9	14.3
408		idccanada	25.9	45.2	7.7	15.9
409		dougwashburn	25.7	39	29.9	17.7
410		bobblakley	25.6	35.5	42.4	15.4
411		DaveBoulanger	25.6	46.7	51.7	17.7
412		elizabethstark	25.6	36	30.2	17
413		dlevitas	25.6	38.4	26.3	14.9
414		mgilpin	25.6	38.6	34.4	17.1
415		alexkwiatkowski	25.6	39.6	26.8	15.3
416		TECtweets	25.5	38.2	23.6	19
417		angela_ashenden	25.5	31.7	27.1	18
418		melanieturek	25.5	38.2	30.5	15.2
419		jaysonsaba	25.5	41.7	26.9	15.7
420		carriejohnson	25.4	38.8	29.6	15.9
421		StephenB_TG	25.4	28.7	25.3	26
422		LFrankKenney	25.3	36	30.7	21
423		ForresterJobs	25.3	49.7	31.3	17.9
424		lightplay	25.3	33.4	33.2	16.3
425		CCSInsight	25.3	40.9	23.1	21.9
426		annenielsen	25.2	40.7	24.1	15.2
427		jonarber	25.2	35.4	30	15.4
428		DavidJWest	25.2	37	4.7	24.2
429		lisabradner	25.1	43	25.7	15.9
430		euroLAN	25.1	40	28.3	17.6
431		debswee	25.1	34.2	29.8	14.4
432		Rick345	25	40.1	25.1	15.9
433		JonCrotty	25	31	7.3	18.9
434		jensbutler	25	36.5	25.9	14.5
435		dconnor	25	40.2	23.4	15.2
436		galvar60	25	36.3	4.3	16.1
437		MKRasmussen	24.9	42.4	4.7	20.1
438		gcreese	24.9	39.6	2	19
439		IDC_EMEA	24.9	26.2	54.9	17.8
440		jneeson	24.9	37.9	20.6	19.6
441		AgingTech	24.9	42.1	26.6	18.2
442		jonathansteel	24.9	40.6	27.3	15.6
443		tjennings	24.9	39.6	27.4	15.8
444		andreasantonop	24.8	39.6	36.2	15
445		thickernell	24.8	36.3	21.9	14.5
446		Chris_Gaun	24.8	34.3	25.8	20
447		bojan_simic	24.7	36.4	28.5	14.5
448		duncanwjones	24.7	24.1	27.1	21.2
449		jarrodgingras	24.7	43.4	30	17.9
450		tcorbo	24.7	38.5	24.7	16.8
451		PeteCunningham	24.7	39.7	34	16.9
452		SA_Update	24.6	39.8	3	19.3
453		instantBP	24.5	39.4	27.2	15.8
454		NormTravelTech	24.5	42.9	30.2	16.1
455		EveGr	24.4	36.9	25.1	15.9
456		dtwing	24.4	38	23.9	16.6
457		richardwinter	24.4	36.6	44.7	14.5
458		TechMarketView	24.4	35.6	20.5	16.5
459		shauncollins	24.3	39.4	21.6	18.2
460		alexcullen	24.3	38.6	3	22.1
461		jimlmurph	24.3	42.7	24.4	15.4
462		zkerravala	24.3	38.9	19.9	14.5
463		fgens	24.2	42.7	25	15.5
464		tombitt	24.2	40.4	35.2	17.3
465		Aberdeen_CMTG	24.1	49.4	24.8	17.1
466		tarzey	24.1	38.3	40.7	15.1
467		KSterneckert	24.1	42.9	28.8	15.8
468		AMR_Research	24	52.6	23.7	17.6
469		NPDFrazier	24	42.1	33.4	18.2
470		ABIresearch	24	35.3	0	25.6
471		smwat	23.9	33.7	21.3	13.4
472		dianemyers	23.9	35.8	25.5	18.5
473		jmp_katz	23.9	40	29	15
474		jfrey80	23.9	40.8	22.2	15
475		fab_biscotti	23.9	39.1	26	14.5
476		richwatson	23.8	40.6	27.3	15.1
477		paulbudde	23.8	35.9	17.5	14.1
478		mbarbagallo	23.7	39.7	27.6	14.9
479		jamystewart	23.7	41.1	21.9	14.9
480		Madtarquin	23.7	42.1	34.2	16
481		susankevorkian	23.6	37.8	19.3	14
482		dpotterton	23.6	35.3	18.4	13.7
483		creativestrat	23.6	30.9	1	14.2
484		wfz	23.6	33.7	5.3	13.2
485		DortchOnIT	23.6	40.3	29.7	16.3
486		100Gigabit	23.6	44.2	24.1	15.9
487		itstorage	23.5	40.7	33.7	15.3
488		DaveMario	23.5	38.4	29.8	15.2
489		MattyHatton	23.5	40.7	32.5	15.7
490		G_ant	23.5	33.1	33.7	17.6
491		CompassIntel	23.5	41.3	21.3	15.4
492		jamiemlewis	23.5	40.3	30.6	15
493		rkoplowitz	23.4	39.6	26.5	15.2
494		barryparr	23.4	42	24.9	15.4
495		jeffkagan	23.4	47.5	21.2	15.6
496		wkernochan	23.3	31	46.4	13.7
497		wmcneill	23.3	40.2	21.1	14.6
498		GartnerTedF	23.3	33	29	16.6
499		Senformation	23.3	38	31.7	14.9
500		dwiklund	23.3	32.1	25.9	16.9

Algorithm and 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. 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.</p>

Twitter Lists – without a doubt this feature addition to Twitter will significantly change the influence score. Even though Twitter has released their API to us, this particular metric is not yet included. When it is, a TweetLevel 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 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 – 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.

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