Top analyst tweeters (via TweetLevel)

22Feb10

imageWho 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).

image

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

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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 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.

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IceRocket Tags: analyst relations,ar,analyst,analyst tweeters,top analyst tweeters,technobabble,tweetlevel,edelman,influence,engagement,trust

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8 Responses to “Top analyst tweeters (via TweetLevel)”

  1. Congratulations for putting together such a massive database and ranking of analysts. It is really is quite an accomplishment and I am honored to be included in the top 10.

    Perhaps an obvious question given how you structured the rankings, but in your view is engagement truly of almost equal standing with influence?

  2. Then there are those of us who SageCircle doesn’t consider “real analysts”, so doesn’t include on their twitter list. :)

  3. Sandy, I suspect SageCircle’s definition of “analyst” is more outmoded than that of Edelman. Eventually, even SageCircle will innovate in this domain and recognize the convergence of traditional analysts with bloggers and other influencers.

  4. Interesting analysis! Thanks for doing this! Appreicate it!

  5. Nice analysis showing how different lists can be used with various tools that display multiple metrics. It seems that with social networking as with many other things, people by nature want to know how they compare to others, or, to show their ranking to boost their personal branding.

    Some like to use total number of followers or tweets for example, some prefer influence, some like interaction or perhaps popularity, as well as trust as seen via tweet level results. Certainly different viewers will have interest and thus need for metrics other than what someone else does.

    One of the trends I’m seeing is that some social media or nontraditional lists do not always include or consider or treat traditional media or analysts as bloggers or independent influencers, probably because those lists do not know of those people.

    Likewise, if traditional list such based on sagecircle, tekrati, your choice does not include someone, not surprising people will not show up on it.

    This begs a question of if the responsibility of a blogger, twitter, analyst, reporter, journalist, writer, author or whoever you are to be known to a list lies with a) the individual to get on the list (e.g. wefollow, tekrati, etc), b) the owner of the list to find and discover everyone, c) a third party should maintain lists via discovery, d) none of the above…

    IMHO it comes down to a bit of both which is individuals who want to be included in some lists, directory, ranking or results need to take some initiative to get included. Likewise, lists, directories and discovery tools should branch out to include others where applicable. Third party discovery tools need to become more robust to auto discover lists and people on them to include.

    Oh, and do I practice what I preach? Sure, when I find a list that I’m not on and if I determine that I should be on it, I sign up or get involved, whether it is notifying the list owner and interacting, or registering for it to be included.

    I do concur with the thought of some others which is take all metrics, rankings with a grain of salt as well as if you don’t like the results, there are plenty of other lists and tools out there, some of which can be gamed to get what you want or prefer ;)…

    Cheers gs
    Greg Schulz @storageio


  1. 1 Social networking: Influence, followers, and ‘nexus leaders’
  2. 2 Mobility Digest - Windows Phone - AT&T To Receive 8 Million Window Phone 7 Devices
  3. 3 Connecting « Snarketing 2.0

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