Top analyst tweeters (via TweetLevel)

19Jan10

There is little doubt that the growth of Twitter has fundamentally changed the way that conversations, ideas and influence are spreading around the web. Previously the domain of a few in the analyst world, several months ago, there was a noticeable shift in the adoption of Twitter by this community and it was fair to say that it had reached its tipping point.

However, with this growth, AR pros reached a common problem. Should they spend their limited time interacting with analysts via twitter (as well as through all the other channels). The answer was most succinctly answered by Jeremiah Owyang of Altimeter Group in a quote that I like to refer to often:

I’ll be very clear on this. If you want to influence me, be in a conversation and dialog with me, in person, online, and wherever I go.

Consequently it became important to understand which are the analysts that are using this tool well. The concept of tiering is not new, we have been doing this for years to ensure that we spend our increasingly limited time with those people that have the largest influence. The problem however, with many of the tiered lists is that the greatest weight is given to the number of followers  someone has. In my opinion this is fundamentally flawed within AR as it is more important to understand which analysts are most engaged and trusted as oppose to merely popular.

TweetLevel aims to answer this question.

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).

I personally use this list by looking at a micro topic area (such as SaaS) and understand out of the 750+ analysts on Twitter which 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.

TweetLevel results for analysts

1. Kudos to Jeremiah who is not only the top analyst tweeter but also the top analyst blogger. This is an amazing achievement to which I salute you.
2. The big guns have now taken note of Twitter and are now actively engaging in conversations. Who’d have thought that when I first compiled this list Gartner presence was poor.
3. Small firms are far more visible using Twitter. Hat tip again to the RedMonk (and GreenMonk) crew for having a voice that reaches the masses.
4. Analysts that use Twitter don’t only specialise in social media (although those that appear near the top of this list tend to work in this space).
5. Details of over 750 analysts were put through the TweetLevel algorithm (names largely taken from the amazing directory at SageCircle). A big change to when I first calculated this

 

		Account	Influence	Popularity	Engagement	Trust
1		jowyang	73.1	71.1	52.7	75.9
2		Econsultancy	63.6	63.1	41.4	71.8
3		forrester	62.7	67.5	45.5	63.7
4		rwang0	60.8	55.7	43.3	55
5		monkchips	60.1	58.1	49.6	56.3
6		TomRaftery	56.4	56.8	51.5	55.5
7		hharteveldt	55.6	51.4	50.1	51.5
8		Gartner_inc	55.4	64.5	38.1	57.9
9		ekolsky	55.3	46.6	58.8	49.2
10		gyehuda	54.7	50.6	52.1	47.6
11		charleneli	54.6	66.5	52.5	47
12		drnatalie	54.3	56	42.8	46.7
13		richi	54.2	54.6	60.7	46.5
14		cote	54	52.9	47.7	46.1
15		jyarmis	54	50.9	55	45.8
16		augieray	53.6	53	47.6	45.9
17		jbernoff	53.2	58.8	56.4	45.7
18		rshevlin	52.5	44	65.9	42.8
19		mfauscette	52.3	56.7	37	44.1
20		merv	52.2	48.2	56.5	41.2
21		storageio	51.9	46	57.1	41
22		sogrady	51.7	49.1	47.7	41.4
23		rmogull	51.7	48.6	56.6	38.4
24		vendorprisey	51.4	47.5	54.8	39.1
25		denschaal	51.3	49.7	49.3	42.8
26		VanessaAlvarez1	50.8	48.1	49.3	47.2
27		cuttertweets	49.9	45.7	37.4	44.6
28		jclarey	49.8	52.2	47.1	37.3
29		Hitwise_UK	49.8	58	45.7	48.3
30		kasthomas	49.7	61.2	37.1	44
31		nselby	49.7	44.3	53.8	39.9
32		debs	49.6	56.3	54.7	37.3
33		stephenodonnell	49.6	49.5	48.1	41.9
34		john_chilmark	49.5	48.4	42.1	39.7
35		Gartenberg	49.5	55.2	48.8	44.9
36		jameskobielus	49.4	47	37.5	39.5
37		bmichelson	49.2	47.2	48.2	38.1
38		carterlusher	49.1	50.9	42.4	38.7
39		bkwalker	48.7	48.3	36.5	39.4
40		vtri	48.7	45.3	45.5	39.3
41		blairplez	48.6	45.4	46.9	39.6
42		Enderle	48.5	57.2	41.6	48
43		samirb	48	48.4	51.8	36.3
44		steven_noble	47.9	49	48.2	36.2
45		bobegan	47.6	44.7	48	38.8
46		Hitwise_US	47.4	58.5	39.5	47.3
47		NeilRaden	47.3	45.6	59.5	39
48		lehawes	47.1	45	52.9	31.1
49		CurtMonash	47	51	43.8	40.6
50		AndiMann	47	46.9	44.6	43.8
51		dealarchitect	46.8	43.3	58.7	37.9
52		robinbloor	46.7	55.6	9	38.2
53		gilliatt	46.6	48.7	49	32.2
54		nate_elliott	46.6	50.6	42.8	33.3
55		JimLundy	46.3	46.7	53.5	39.8
56		InFullBloomUS	46.3	48.1	41.3	42
57		stiennon	46.1	63.4	49.7	28.5
58		jonno	46	45.9	54.5	30.5
59		Claudia_Imhoff	46	47.1	54.9	40.1
60		SethGrimes	45.9	47.6	59.2	40.7
61		idarose	45.8	49.3	43.4	30.8
62		PhoCusWright	45.6	50.6	33.4	41.8
63		Mark_Mulligan	45.6	50.7	48.6	38.8
64		dvellante	45.4	43.1	58.9	30.1
65		s_crawford	45.2	44.6	46.3	33.3
66		arj	45.1	44.8	52.5	33.9
67		lauraramos	45.1	50.6	29.9	40.8
68		mark_koenig	45.1	40.7	46.8	38.9
69		ghaff	44.9	42.3	46.3	36.1
70		dankeldsen	44.7	54.9	45.9	26.6
71		bitterer	44.6	46.7	50.3	26.9
72		megheuer	44.4	48.4	41.3	33.2
73		jamet123	44.4	45.1	32.7	35.8
74		jonathaneunice	44.3	46.8	49.2	28.1
75		jhurwitz	44.1	50.8	40.6	33.5
76		dale_vile	43.9	44.5	57.4	29.8
77		stevedupe	43.8	46.2	32.9	38
78		clivel_98	43.7	43	54.2	29.4
79		JEBworks	43.7	47.7	47.4	35.6
80		cmswatch	43.6	52	36	41.4
81		stephenmann	43.6	44	45.9	31.1
82		caostheory	43.6	42.3	0	42.8
83		EricaDriver	43.5	45	40.1	38.3
84		Dana_Gardner	43.5	49.8	32	41
85		krestivo	43.5	46.5	48.6	27.7
86		Jake_Hird	43.4	43.9	40.1	38.6
87		mgualtieri	43.4	45.4	37.8	35.9
88		cmendler	43.3	44.2	45.1	34.1
89		abnerg	43.1	47	44	29.3
90		fscavo	42.9	45.9	5.7	33.8
91		rgruia	42.9	47.2	48.1	24.4
92		janovum	42.8	40	54.3	30.5
93		fiberguy	42.8	40.6	43.4	31.9
94		the451group	42.7	50	23.6	37.4
95		atmanes	42.7	44.9	39.7	29
96		gilbane	42.7	48.7	34.5	37.8
97		michaelwolf	42.7	41.3	40.7	33.5
98		btudor	42.3	42.5	46	32.1
99		jblock	42.2	53.6	34.3	28.5
100		biz_mobility	42.2	41.4	44.8	31.8
101		KarenYetter	41.9	59.8	10	34.7
102		Thomas_Husson	41.9	44.4	34.8	41.4
103		markmadsen	41.8	45	49.6	22.5
104		pragkirk	41.7	43.9	38.4	35
105		seuj	41.5	34.5	53.9	27.4
106		maslett	41.5	40.1	35.9	33.1
107		antallan	41.5	42	46.5	24.9
108		znh	41.5	45.8	37.8	26.7
109		stylishidea	41.3	46.7	36.5	22.2
110		jimholincheck	41.3	44.6	35.4	35.5
111		johnrrymer	41.2	45.8	37	34.7
112		adamhleach	41.2	39.5	47.3	27.9
113		802dotchris	41.1	42.7	44.7	35.7
114		gwoodill	41	50.6	42.9	31.3
115		martinatherton	41	40.7	45.7	28.2
116		SteffWatson	40.8	36.2	37.5	36.1
117		dschatsky	40.7	43.5	31.6	30.8
118		dnm54	40.7	38.4	33	30.4
119		infotrends	40.6	45.4	32.2	34.7
120		JeffreyBreen	40.6	44.4	39.5	32
121		sureshvittal	40.3	45.4	37.2	35.1
122		storageswiss	40.2	43.9	32.1	34.8
123		mosterman	40.1	41.2	7.7	33.6
124		stessacohen	40.1	41.9	39.3	34.1
125		bradholtz	39.9	42.8	34.5	29.5
126		Montejam	39.9	42.7	40.3	31.9
127		IDC	39.9	55.1	4.7	41.1
128		Douglas_Quinby	39.8	44	42.4	33.6
129		TonyBaer	39.8	40.1	52.3	31.9
130		Staten7	39.8	42.8	33.7	37.2
131		pattyinboothbay	39.7	47	30.1	27.8
132		timehhh	39.7	41.3	40.5	25.2
133		lisarowan	39.7	44.2	33.2	30.9
134		brian_riggs	39.6	41.6	31.8	33.9
135		neilwd	39.5	41.6	40.4	27.6
136		AndreaDiMaio	39.4	44.1	5	38.7
137		tomcummings	39.2	48.7	49.8	20.9
138		rayval	39.2	48.1	35.8	28.1
139		bfinucane	39.1	46.9	48.6	24.7
140		paulhamerman	39.1	41.4	27.7	34
141		rossrubin	39.1	46.8	45.6	21.3
142		cswolf	39.1	45.1	46.5	21.5
143		IDCInsights	39.1	49.8	35.8	32.8
144		iglazer	39	42.7	45.4	22.5
145		jeffmann	38.8	46.4	37.8	22.4
146		Wikibon	38.8	43.3	29.6	33
147		andreascon	38.6	43.3	32	34.1
148		billtrippe	38.5	46.6	37.2	22.1
149		johnlovett	38.1	45.5	43.8	29.6
150		NickLippis	38	44	29.9	31.3
151		PhilippBohn	38	42.2	40.9	31.3
152		vgtero	37.7	44	33.8	21.5
153		darranclem	37.6	41.6	37.9	23.3
154		jhammond	37.6	43.6	33.7	23.8
155		siriusdecisions	37.4	45.2	35.5	30.6
156		smulpuru	37.4	46.1	26.2	30.4
157		btemkin	37.3	44.5	0.7	36.4
158		esganalysttmac	37.3	42.1	42.1	33.8
159		erainge	37.3	42.6	31.6	31.4
160		adriaanbloem	37.3	39.6	49.8	22
161		ema_research	37.2	44.5	6.3	35
162		sliewehr	37.2	36.5	41.4	32.7
163		FreeformCentral	37	42.9	36.4	37.5
164		jalvear	36.9	41.4	40.4	21
165		bryanyeager	36.8	37.8	34.4	28.8
166		MaribelLopez	36.8	45.6	44.4	23.3
167		iSuppli	36.7	44.5	5	32.3
168		aschmitt	36.7	37	34.3	25.7
169		randygiusto	36.4	44	33.6	22.8
170		mbrandenburg	36.3	40.8	40.2	24.7
171		allenweiner	36.2	45.5	30.7	19.3
172		ianfogg42	36.2	42.7	32.8	21
173		omriduek	35.9	39.7	36.3	22.2
174		davidcard	35.8	45.8	29	23.1
175		keith_rng	35.6	43.7	7.7	20.4
176		paulfroberts	35.5	43.5	33	24.1
177		JeffPR	35.5	45.6	36.4	25.5
178		rogermud	35.3	41.6	28.3	23.5
179		StefanRied	35.3	42.8	26.5	28.5
180		rnantel	35.2	39.7	28.2	25.3
181		glennodonnell	35.2	42.3	44.8	21.3
182		SarahBurnett	35.1	46.1	42.6	21
183		jim_techclarity	35.1	39.7	40.6	25.5
184		RayLucchesi	35.1	41.8	55.7	27.1
185		Mark_Goldberg	35.1	37.8	30.7	31
186		YankeeGroup	35.1	47.5	5	31
187		Lciarlone	35.1	42.8	38.1	24.9
188		joeltweet	35.1	60	41.3	21.7
189		etravelproject	34.8	46.2	33.6	19
190		dkusnetzky	34.8	41	43.3	24.2
191		ravenzachary	34.7	46.2	29.6	19.1
192		toppundit	34.7	42	5	31.5
193		tebbo	34.7	43.7	5	20.2
194		npdgroup	34.6	48.6	4	35.3
195		timbo2002	34.6	43.7	34.4	18.8
196		Gabeuk	34.3	41.3	42.6	23.3
197		rreitsma	34.3	39.8	29.8	32.3
198		lauriemccabe	34.2	39	30.5	26.8
199		Zak_Kirchner	34	41.3	7	23.8
200		demartek	33.9	42.3	32.1	22.6
201		EquaTerra	33.9	42.3	6.3	36.2
202		Payment_Expert	33.9	48.8	29.2	34
203		TomGrantForr	33.9	41.9	25.4	27.4
204		BarryRabkin	33.8	43.3	41.7	21.8
205		gleganza	33.8	42.2	45.9	18.6
206		nickpatience	33.7	38.7	33.4	28.4
207		hyounpark_AG	33.6	42.5	29.8	27.8
208		jpmorgenthal	33.6	44	33.7	18.6
209		Heuristocrat	33.4	44	29.6	20.8
210		bevelson	33.3	38.6	34.4	26.9
211		TKspeaks	33.3	55.4	35.1	30.2
212		LockTD	33.3	39.8	39.8	19.6
213		bmirabito	33.2	50.4	30.6	19.2
214		thinkovation	33.1	42	40.5	18.6
215		BurtonGroupIT	33.1	46	6.3	28
216		pfersht	32.9	45.1	45.3	21.6
217		gilbaneboston	32.9	50.5	33	19.2
218		punirajah	32.8	37.6	32.6	19.2
219		TonyByrne	32.8	46.1	36.8	25
220		msbrumfield	32.7	42.6	26.3	19.7
221		Bersin	32.7	52	27.7	32.4
222		LinusGreg	32.7	46.1	33.5	23
223		ekmurphy	32.7	38.3	36	19.2
224		MarkRaskino	32.7	42	29.5	24.4
225		pjtec	32.6	34.2	28.6	26.9
226		dxmitche	32.6	45.4	34.3	18.6
227		imlazar	32.5	41.9	39.4	19.6
228		rschmelzer	32.5	41.2	32.9	19.4
229		Frost_Sullivan	32.4	42.2	5	25.5
230		visionmobile	32.2	41.9	25.7	28.2
231		markbowker	32.1	40.5	25.2	31.6
232		MattRosoff	32.1	44.6	29.8	25.1
233		ironick	32	38.5	36.2	19.1
234		CurrentAnalysis	31.9	44.3	0.3	28
235		pund_it	31.7	37	33	18.9
236		adamjura	31.6	38.8	40.8	19.8
237		mlevitt	31.6	44.1	23.9	17.6
238		jblin	31.3	39.5	26.4	16.7
239		shigginski	31.3	36.9	29.2	16.9
240		bsdunlap	31.2	40.9	28.7	17.4
241		scottn7	31.2	40.1	24.1	17.1
242		dfrankland	31.2	44	30.1	22.9
243		katiesmillie	31.1	40.7	31.4	18.9
244		Jossgillet	31.1	33.5	36.7	20.4
245		TheNakedChief	30.9	35.3	25.5	24.2
246		gilbanesf	30.8	42.1	30.3	24.1
247		lightplay	30.8	33.2	36.3	25.2
248		NPDFrazier	30.8	41.8	37.7	29.1
249		tjkeitt	30.8	37.9	34.9	19.4
250		ca_bshimmin	30.7	43.2	28.5	18.9
251		iangjacobs	30.7	42.5	31.1	17.5
252		nora_freedman	30.7	43.4	42.6	17.7
253		SeanCor	30.7	47.5	36.3	24.5
254		jmcquivey	30.6	43.7	39	18.8
255		nyuhanna	30.6	48.6	28.6	18.5
256		cee_m_bee	30.6	41.3	33.5	17.4
257		alyswoodward	30.5	38.2	44.6	16.9
258		pmcginnis	30.5	42.9	32	17.1
259		Gartnergreg	30.5	41.2	31.3	18.5
260		jonathanbrowne	30.3	44.1	33.3	17.3
261		epopova	30.3	38	24.4	23.4
262		cmooreforrester	30.3	42.9	26.7	20.5
263		theresaregli	30.3	43.2	34.6	19.5
264		Celent_Research	30.2	43.2	27.3	25.5
265		passingnotes	30.2	42.1	37	17.3
266		passion4process	30	41.7	27.6	18.9
267		erichknipp	30	38.6	5.3	19.5
268		analystnick	29.7	40.8	33.7	17.2
269		rbkarel	29.6	38.7	32	21.1
270		mbkmbk	29.6	38.6	28.3	20.2
271		katehanaghan	29.5	40.5	39.2	17.1
272		dmavrakis	29.4	37.8	35.6	16.5
273		jjegher	29.3	42	34.9	17.5
274		brucemr	29.3	35.1	28.9	15.6
275		NewsShark	29.3	44.3	24.8	21.1
276		mlees	29.2	39.1	25.2	19.2
277		royillsley	29.2	42	26.6	16.7
278		euroLAN	29.2	39.4	30.3	25.8
279		AlanWebber	29.1	44.7	32.5	18.5
280		nengelbert	29	42.4	28.2	16.6
281		nickster2407	29	39.9	33.5	16.5
282		Neovise	28.9	37.2	28.3	21.6
283		jcapachin	28.9	38.7	27.8	19.5
284		minicooper	28.9	45	34.9	17.4
285		chris_townsend_	28.9	42.5	40.5	17.3
286		rob_bamforth	28.8	36.7	29.8	16
287		SA_Update	28.8	39.3	3	29.1
288		ripcitylyman	28.7	38.6	6.7	17.1
289		DenisPombriant	28.7	42.7	26.8	23.5
290		ventanaresearch	28.6	43.5	26.9	17.8
291		howett	28.6	32	34.5	15.5
292		heyshiv	28.5	35	46.6	16.3
293		joltsik	28.5	40.2	19.6	29.1
294		GaryatWikibon	28.5	39.4	29.7	16.9
295		TECtweets	28.5	37.2	25.8	25
296		KeithHumphreys	28.5	44.2	34.3	19.3
297		vvittore	28.3	34.8	29.5	17.2
298		mcgeesmith	28.3	37.4	31.5	21.3
299		billnagel	28.2	34.3	32.3	15.7
300		VLI	28.2	35.8	36.7	16.2
301		Hitwise_AP	28.1	50.9	8.7	21.8
302		mgrey	28	39.4	3.7	17.6
303		gcolony	28	56.9	22.6	19.3
304		AgingTech	27.9	41.6	27.4	24.2
305		jensbutler	27.9	36.3	29.2	16
306		ferrusi	27.9	36.6	30.1	15.6
307		darrenbibby	27.8	39.4	23.2	17.6
308		cdhowe	27.8	34.5	26.2	21.3
309		tim_walters	27.7	40.1	30.1	18.2
310		stor2	27.7	40.3	23.2	15.5
311		DaveMario	27.6	38.2	31.9	20.3
312		infotrends_mps	27.6	37.9	30.7	15.4
313		kreidy	27.4	41.1	39.2	18.1
314		azornes	27.3	38	4.3	15.5
315		PHassey	27.3	35.5	32.4	16.2
316		REdwards	27.2	41.1	22.5	20.7
317		JoeGalvin	27.1	37.4	25.5	25.1
318		CRozwell	27	41.3	31.1	19
319		jeunice	27	38.2	29	15.7
320		iTGuru	26.8	38.6	24.3	25.6
321		algillen	26.8	35.6	27.1	20.8
322		PaulBrown_SA	26.8	36	29.5	20.8
323		NormTravelTech	26.8	42.5	31.6	20.4
324		reastman	26.8	39.6	28.5	15.6
325		diteb	26.7	41.9	33.4	16
326		acfrank	26.7	41.2	24.7	15.5
327		melanieturek	26.7	38.1	31.7	15.8
328		richwatson	26.7	40.2	28.7	19.6
329		benwood	26.7	43.1	25.7	19.6
330		KeithKTsang	26.7	39.3	30.9	17.2
331		sjschuchartCA	26.6	38.9	46	16.9
332		fjeronimo	26.6	36.5	25	15.1
333		lcecere	26.5	41.1	27.9	16
334		ForresterJobs	26.4	49.4	32.4	18.4
335		heidishey	26.4	36.8	33.8	15.2
336		jfrey80	26.3	40.8	22.9	19.3
337		alexkwiatkowski	26.3	39.7	27.4	15.7
338		smwat	26.3	33.6	22.8	16.1
339		Rick345	26.3	40.1	25.8	18.1
340		jaysonsaba	26.2	41.5	27.8	16
341		nigelwallis	26.2	32.5	36.3	14.5
342		bseitz	26.2	31.5	36.4	14.1
343		idccanada	26.1	44.9	7.7	15.8
344		ARC_Advisory	26.1	43.7	24.8	16.8
345		whita	26.1	36.9	39.7	15.4
346		zmcgeary	26	43.2	39.3	16.1
347		hokun	25.9	39.5	33.5	15.8
348		Horses4Sources	25.9	41.6	25.5	23.4
349		carldoty	25.9	38.6	25.4	15.2
350		zkerravala	25.9	38.6	21.1	15.3
351		ChristianKane	25.8	39.3	32.7	16.3
352		D_Hong	25.7	42.6	6.3	16.1
353		strothkamp	25.7	35.9	24.8	21.9
354		stavvmc	25.7	32.6	33	16
355		DaveBoulanger	25.6	46.8	51.7	17.8
356		Teresacottam	25.6	38.8	26.7	23.2
357		fgilbane	25.5	41.3	31.6	15.6
358		securityjeff	25.5	37	38.9	15.2
359		smcleish	25.5	37.8	28.6	18.7
360		mtauschek	25.4	33.6	30.3	14.5
361		diane_clarkson	25.4	41.8	22.7	19.2
362		TGView	25.4	36.5	26.8	15.3
363		Shawn_McCarthy	25.4	37.9	30.9	15.7
364		fgens	25.3	42.3	26.2	17.6
365		drjimmys	25.3	35.9	20.7	14.1
366		thickernell	25.3	36	22.3	14.7
367		markpmcdonald	25.3	38.6	4	16.7
368		TechMarketView	25.3	35.4	21.5	18.3
369		susankevorkian	25.2	37.6	20	16.4
370		annenielsen	25.2	40.7	23.9	15.2
371		mobilesimmo	25.2	37.1	24.5	14.8
372		Nemertes	25.1	39.3	6.7	18.7
373		Burgess_Gary	25	34.9	29.6	17
374		mHingley	25	40.1	5.3	19.4
375		Andrew_Boyd	25	46.4	31.8	16.9
376		dlevitas	24.9	38.2	26.5	14.7
377		dpotterton	24.9	35.2	18.7	15.8
378		Josh_Bersin	24.8	52.9	24.6	18
379		markhrobinson	24.8	41.3	24.3	15.8
380		andreasantonop	24.8	39.4	36.1	14.9
381		Aberdeen_CMTG	24.7	49.4	25.3	17.4
382		hkisker	24.6	35.3	21.7	14.2
383		PeteCunningham	24.6	39.5	33.9	16.9
384		jonarber	24.6	35.4	29.3	15.1
385		chrischute	24.6	31.8	28.6	15.4
386		DouglasHayward	24.6	33.2	25.2	19.9
387		AMR_Research	24.6	52.3	24.2	17.9
388		debswee	24.5	34.2	29.5	14.1
389		jeffkagan	24.5	47.1	21.6	17.6
390		rwhiteley0	24.5	40.3	28.5	15.2
391		tonyiams	24.4	31	22.2	18.8
392		FillMurphy	24.4	34.1	25.4	20.2
393		richardwinter	24.4	36.6	44.7	14.5
394		jamystewart	24.3	41	22.1	16.7
395		brightfly	24.3	40.7	5	14.8
396		mgilpin	24.3	38.4	30.9	15.2
397		jonathansteel	24.3	40.6	26.7	15.3
398		Craw	24.2	41	28.4	18.1
399		waband	24.2	41.7	24.6	17.7
400		nsturgill	24.1	34.3	20.6	15.7
401		100Gigabit	24.1	44.1	24.4	16.1
402		MattyHatton	24.1	40.7	33	16.1
403		jctec	24	30.4	21.6	18.1
404		Senformation	23.9	37.8	33.7	15.3
405		instantBP	23.9	39.4	26.7	15.5
406		jamesbrehm	23.8	36.6	21.5	15.8
407		jmp_katz	23.7	39.5	28.2	14.8
408		kevinnolanuk	23.7	40.3	23.4	14.4
409		ajbowles	23.7	39.5	21	14.6
410		rsilvalondon	23.7	31.9	2	13.3
411		Madtarquin	23.6	42.1	33.9	16
412		itstorage	23.6	40.7	33.9	15.3
413		SW_Support	23.5	32.2	27.3	24.6
414		philkendall	23.5	37.1	23.9	13.9
415		jamiemlewis	23.5	40.4	30.6	15
416		CompassIntel	23.5	41.3	21.4	15.4
417		tarzey	23.4	38	39.7	14.7
418		MWDAdvisors	23.4	34.3	22.9	21.2
419		lauradidio	23.4	37	4.3	13.8
420		michaelgreene	23.3	37.7	25.7	14.5
421		wkernochan	23.3	31	46.4	13.7
422		OvumICT	23.3	35.4	3.7	21.5
423		wmcneill	23.3	40.2	20.6	14.6
424		instat	23.3	40.3	19.2	14.8
425		infonetics	23.2	43.5	5.3	14.9
426		fab_biscotti	23.2	39.1	25.7	14.3
427		emmaking_escp	23.1	39.4	30.2	14.8
428		geoffblaber	23.1	39.8	28.1	14.7
429		barryparr	23.1	41.9	24.6	15.1
430		mbarbagallo	23.1	39.7	27.5	14.7
431		lisabradner	23	42.5	23.7	15
432		wjmalik	23	34.1	33	13.8
433		chinamartens451	22.9	32.4	21.1	12.9
434		Canalys	22.9	40.5	4.7	15.4
435		dconnor	22.9	40.1	21.2	14.3
436		dtwing	22.9	38	21.8	16.1
437		rkoplowitz	22.9	39.3	26.2	14.8
438		msmvnj	22.9	37.5	23.9	14.1
439		christinaylee	22.8	34.3	32.6	13.7
440		red_gillen	22.7	30.3	19.2	17.9
441		jarrodgingras	22.7	43.3	28.6	15.5
442		jeffheynen	22.6	36.7	24.2	14
443		adriandrury	22.6	31.2	22.9	12.7
444		angie_p	22.6	35.7	31	13.8
445		Nathan_Safran	22.6	36.1	32.1	14.5
446		danolds	22.5	33.4	18.2	13.4
447		NinaLytton	22.5	33.6	28.8	13.9
448		galvar60	22.5	35.9	4.3	13.3
449		Telesperience	22.5	39	19.5	16.7
450		Hils_of_IT	22.4	38.5	30.3	14.8
451		carriejohnson	22.4	38.6	27.2	14.4
452		srepps	22.4	39.7	29.6	14.6
453		pdebeasi	22.4	40.5	19.2	19
454		MobileAberdeen	22.3	42.1	26.1	15.6
455		srog	22.3	40.9	41.8	15.1
456		CCSInsight	22.3	40.7	21.1	18.8
457		tombitt	22.3	40	34.1	14.8
458		miravperry	22.3	37.8	26.2	13.9
459		wif	22.2	37.1	25	14.8
460		wfz	22.2	33.5	5	12.6
461		bswtelecom	22.2	39.1	19.3	16
462		elizabethstark	22.2	35.5	27.9	13.6
463		dkerrsa	22.1	30.2	20.2	16
464		TheTowerGroup	22.1	35.8	23.5	18.2
465		gphifer	22.1	41.1	4.3	14.3
466		tjennings	22	39.6	25.1	14.3
467		RichBohn	22	46.1	8.3	15.4
468		MKRasmussen	22	42.2	4.7	18.7
469		dougwashburn	22	38.2	26.7	14.1
470		bgassman	21.9	40.1	27.1	14.7
471		wzhou	21.9	35	22.2	13.3
472		dreeves1	21.9	29	32	12.5
473		jackzimmerman	21.8	38.1	17.5	13.9
474		TedSchadler	21.8	41.9	18.8	19.2
475		vernonxt	21.8	36.9	19	13.7
476		tcorbo	21.7	38.4	24.1	13.9
477		mgerush	21.7	38.4	22.6	13.8
478		cborovick	21.7	37.4	22.8	13.6
479		zapthink	21.7	32.2	14.9	13.1
480		angela_ashenden	21.7	30.8	19.8	16.1
481		pete_lacey	21.7	30.8	21.6	12.4
482		adelesage	21.6	36.6	27.1	15.4
483		davidmsmith	21.6	39.7	21.7	14.2
484		LFrankKenney	21.6	35.7	28.7	14.5
485		shauncollins	21.6	39.3	20.5	14
486		ABI_4G	21.5	34.3	19.7	15.3
487		davecapp	21.5	34.9	24.7	13.3
488		sebastiancoss	21.5	36.7	19.1	13.4
489		marksdriver	21.5	37.6	3	13.5
490		CushingAnderson	21.4	39.5	30.2	14.7
491		scottsantucci	21.4	37.1	20.2	13.4
492		tim_shepherd	21.4	40.3	14.7	14.1
493		140letters1idea	21.4	32.7	22.1	12.7
494		julie_craig	21.4	31.8	18.4	12.8
495		ChrisOber	21.3	35.3	23	14
496		monkchipsconf	21.3	32.6	23.2	12.8
497		DortchOnIT	21.3	40.3	27.9	15.1
498		DavidJWest	21.3	36.3	3	20.9
499		ITAnalysis	21.3	30.5	4.3	14.2
500		benwoony	21.2	36.5	20.7	13.5

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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