Top analyst tweeters (via TweetLevel)
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:
- Popularity (i.e. How many people follow you)
- Influence (i.e. What you say is interesting, relevant and many people listen)
- Engaged (i.e. You actively participate within your community)
- 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
- 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.
- 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.
- 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.
- 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).
- 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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Filed under: analyst relations, social media | 41 Comments
Jonny thanks for all you do for the industry, you’re a real asset to Edelman and the space. It’s great to learn from what others are doing. Keep up the great work
#SalutesBack
Thanks Jonny, we now feel especially influential, but not quite as influential as the mighty Jeremiah!
Jonny,
What Jeremiah said – this is a superb resource which was obviously non-trivial to produce – kudos to you and many thanks for this.
Thanks of including me.
Is the influence score black on dark blue background an attempt to obscure things from older analysts like me 😉
Love it. Great analysis.
Jonny,
Thanks for the inclusion. Much appreciated. Keep on keeping on!
-Sam.
Please add me to your analysis next time. I would have been #27 on this list with an Influence Score of 50.
Jim Berkowitz
CRM Mastery, Inc.
Thanks so much.
Hi there Jonny,
Thanks for including me as well. As number 164, it gives me great incentive to reach number 1 next time!
Seriously, this is a great, fascinating service. As an independent analyst, it’s much harder to have that involvement with clients, since I’m not the main client contact.
Twitter still isn’t being used by the analyst community much. There’s definitely an opportunity here.
Enjoy,
Jose
Many thanks for including me, especially since I’ve been pretty quiet in the Twitterverse as I recover from shoulder surgery. Much more coming as I regain use of my left hand on the keyboard.
Hi Johnny,
Thanks so much for this. As #162, I’m honored just to be included with the very well-respected individuals on this list. Thanks so much for this service, it’s obviously quite a feat to produce.
All the best,
Scott
@sliewehr
A great list and congratulations to all the fine analyst-tweeters who are represented here!
Jonny,
Thanks for including me and for the work you did on this. Very interesting.
Thanks for sharing this with us. I just created a Twitter list of the top 50 analysts ranked here. If you find this topic interesting, you should definitely follow it:
http://twitter.com/SeanWF/top-analyst-tweeters
Great list!
We would like to have been on it, though. J. Boye regularly tweets about market trends, vendors and customer experiences. You can find us here: twitter.com/jboye
Hope to join the gurus in version 2 🙂
Johhny, this is an outstanding idea, thanks for sharing. Could we borrow some of your thinking to create a UK focussed list?
Jonny, this is a great idea, thanks for sharing. Could we borrow some of your thinking to create a UK focussed list?
Congratulations to everyone represented here! It is great to be included on a list that was generated using a clear, well-documented methodology The rank to which I was assigned (#48) has much more meaning as a result. Thank you for taking the time to generate this list, Jonny. I hope others will find it useful.
Interesting metrics. Of course, quantitative metrics are for analysts who tweet by the numbers. I tweet to clarify my own thinking, and secondarily to publish it (for various reasons). Numbers don’t move me to tweet or do anything else.
This is hilarious. I score in the top half, several places above my CEO, and mine is a personal Twitter account that I don’t use for professional purposes — when I bother to tweet at all.
Thanks Johnny
I love a good league table.
BTW new blog is now: http://marcduke.wordpress.com/
Marc
Thank you for making compiling this important list of analysts. Does this list exist on Twitter or TweepML? Would be extremely useful to follow everyone with one click of a button.
Thanks,
Judith
great methodology! You should make “Analyst Tweeters” a category just like you did for “Analyst Blogs” but have both automatically update weekly. These scores change constantly depending on the activity in the blogosphere and Twitterverse each week.
There were rapid swings for my scores between Jan 19 (this post), your post on analyst blogs, and today.
But great idea to create a real-time gauge for AR and PR.
Randy
This is indeed a great and interesting research effort. I’m hoping it will help to make for both smarter and more effective analyst-AR/PR communications AND help users to connect with analysts who can help them make better IT decisions. (I have to admit that it’s also pretty gratifying to be in such illustrious company, especially given that social media is often portrayed as somehow beyond MOACAs (men of a certain age) such as myself! 🙂