image

A process cannot be understood by stopping it. Understanding must move with the flow of the process, must join it and flow with it.

..and yet the above quote from Frank Herbert is exactly what many people ignore when trying to understand who is influential on any given topic. Conversations are not fixed points in time but are dynamic and agile with different participants contributing throughout.

Why This is Important?

If people are trying to influence a conversation to ensure their message resonates throughout their target audience it is essential that they target the right people, at the right time in the right manner. Too often people only focus on who are the right people but haven’t access to the right tools to help with the later areas.

Working in tandem with Ramine Tinati from the the University of Southampton, we analysed multiple conversations via a unique tool called TweetFlow for common trends. The results were astounding and has directly impacted the way I work.

In previous discussions, I have explained how:

influence is defined by how information flows in a conversation

As part of this, much of the focus has been on two of the primary personas within the topology of influence. The idea starter and the amplifier – however, what I have only recently realised in my own Eureka moment is that a forgotten but critical player is the curator needs to be engaged with. This person, often overlooked due to their relatively low popularity has proven to be a significant driver of influence.

When analysing the three types below, it is clear to see that as marketers we must adopt both technology tools and sociological profiling to help us interact with people. Idea starters start early in great detail but often do not engage when the conversation reaches maturity and amplifiers publish and move on. If we were to engage with these two types when a conversation has been established in the market for some time because we rightly understood that these two people were instrumental in making this happen, then we would be wasting our effort. Instead it is the local expert who maintains the conversation and enables it to grow.

Maturity of Conversation Flow via Influence

Taking the Gartner hype cycle concept, I have adapted this to the growing maturity of a conversation topic. As marketers, we need to identify at what stage of a conversation we are engaging in, so that we in turn can ensure that our limited time and focus is spent concentrating on the right people who have the greatest chance of influencing others.

Stage 1: Conversation Trigger

Regression analysis of conversations often point to a few individuals who initiate the concept. These ‘idea starters’ often collaborate with curators to refine the concept and amplifiers to help publicize their thoughts. Engagement with the idea starter via collaborative discussion holds the greatest opportunity to influence the conversation.

Key influencer: Idea Starter

Preferred Engagement Behaviour: Collaborative discussion

Stage 2: Peak of Concurrent Conversations

When an ‘amplifier’ becomes interested in a conversation, it has the opportunity to reverberate around communities. With a large audience, an amplifier’s voice is disproportionately loud and for this reason has often been the target of many influence campaigns. Engagement with group has the greatest chance of success provided a relationship exists. However, this opportunity is often extremely hard to achieve and hence marketers have instead opted for influencing the influencers of this group (i.e. idea starters) or using paid methods (e.g. advertorials). Nevertheless, what cannot be doubted is that when an amplifier publicises content, it generates a huge volume of conversation.

Key influencer: Amplifier

Preferred Engagement Behaviour: Pre-packaged content that is easy to reproduce

 

Stage 3: Trough of Early Adoption

As any blogger will tell you, after the initial excitement of a meme, there comes a quick and sudden lull in conversation volume. What is most apparent is that the early catalysts for discussions no longer actively participate in the conversation. This is a crucial stage as it is here where the traditional key influencers of idea starters and amplifiers make way for the curator. Curators are the niche experts who are known within their circle as the go-to-person about a niche area. They may not have a huge number of followers but they maintain the conversation when others have left.

Key influencer: Curator

Preferred Engagement Behaviour: Q&A, scenario discussion

 

Stage 4: Slope of Enlightenment

For an idea to manifest, it takes time and evidence-based discussion to prove that it is an idea worth following. It is at this stage that once again we see the curator as being a focal point in idea adoption.

 

Stage 5: Plateau of Mainstream Adoption

You may never get as high a degree of volume of discussion as with the Peak of Concurrent Conversations but it is at this final stage where key commentators are dominating the conversation. Adoption of the idea is widespread with the initial idea starter and amplifier having progressed to other areas some time ago.

 

Proof of Theory – TweetFlow

In this video created with TweetFlow, you can see how idea starters start early in the conversation, amplifiers give it mass growth but it is the curators who make it last.

TweetFlow–created in partnership between Jonny Bentwood (Edelman) and Ramine Tinati (University of Southampton)

 

The Topology of Influence in Detail – idea starter, amplifier, curator, commentator and viewer

Idea Starters – this small collective of people are the creative brains behind many of the thoughts and ideas that other people talk about. Even though they may not necessarily have a large audience themselves, their insightful opinions often flow and are repeated throughout conversations long after they have left. They are typically well connected to other idea starters (where they collaborate on thoughts) and amplifiers (who they often rely upon to spread their views). Idea starters tend to be well connected to curators and amplifiers.

Amplifiers – these people frequently have a large audience and following. Their expertise may be deep but often they rely upon other contacts to provide opinion to which they then let their readership know about. They often have professional or commercial motivations such as journalists or analysts but are also more often than not self-created experts and avid sharers of information. Their advantage and their burden is their huge number of followers they need to keep satisfied. This behaviour ensures that they need to receive pre-packaged content that they can easily repost, retweet or repurpose so that their audience does not diminish. Amplifiers are frequently well connected to idea starters as the source of their content.

Curators – this group though having a far smaller audience are perhaps one of the most influential groups. Long after the idea starter and amplifier have left a conversation, it is the curator that maintains discussion. This niche expert collates information about a specific topic and is frequently sought after for advice about this specific area. They often take part in discussions with idea starters and are avid readers of topic-specific amplifiers.

Commentators – these people individually have little influence. Their behaviour often resembles little more than adding a comment without contributing greatly to the conversation. Their influence should not be ignored but should instead be viewed as a collective to measure the trend of opinion around a subject. An interesting factor is that this group are often self-moderating – when negative comments are posted often these contributors will often intervene to correct inaccuracies or a unfounded negative views.

Viewers – In the conversation this invisible group who we call viewers don’t leave a foot print except through Google. Indeed it is through Google, and the impact of viewers on search results, that these other groups become influential and evolve their role within a conversation. Authority rests with the search patterns of those who simply observe in a democratic world.

Conclusion

In order to stand the greatest for marketers to influence a conversation, they must appreciate what maturity stage the conversation currently is at. Upon doing that they will need to target the most appropriate person from within the topology and engage with them according to their behavioural characteristics.

image

End note: We are currently beta-testing the next iteration of TweetLevel which will allow anyone to identify what type of influencer a tweeter is via its algorithm. If you would like a beta-access password, please contact @jonnybentwood

HomelessOne of the most talked about pieces of news to come out of this year’s SXSW was not shiny new tech but the “Homeless Hotspot” campaign by BBH Labs, the innovation unit of the international marketing agency BBH. According to Jenna Wortham writing for The New York Times, BBH outfitted 13 ‘volunteers’ from a homeless shelter with Wi-Fi hotspot devices and T-shirts bearing their names: “I’m Clarence, a 4G Hotspot.” They were reportedly paid $20/ day (£13) to go to the most densely packed areas of the conference and were allowed to keep whatever customers donated in exchange for the wireless service. What BBH dubbed a “charitable experiment” has undeniably backfired with industry pundits and media calling the campaign “exploitive” and “tasteless.” Wired magazine even described “Homeless Hotspots” as something which sounds like it is out of a “darkly satirical science-fiction dystopia.” But is it really all that bad?

BBH has defended its thinking framing the initiative as an attempt to “modernise the Street Newspaper (similar to the UK’s Big Issue) model employed to support the homeless populations”. This has only triggered further criticism. In the past 24 hours, an official response from BBH has been released: “Obviously, there’s an insane amount of chatter about this, which although certainly villainizes us, in many ways is very good for the homeless people we’re trying to help: homelessness is actually a subject being discussed at SXSW and these people are no longer invisible… we wanted to share a few key facts: We are not selling anything. There is no brand involved. There is no commercial benefit whatsoever.” You can read the full comment on BBH’s Homeless Hotspots website.

The campaign for SXSW has failed so spectacularly and so publically. Using Edelman’s TweetLevel tool to evaluate Twitter buzz over the past couple of days, the campaign’s hashtag "#HomelessHotspot" was itself virtually invisible until hybrid media picked up on story on Monday (12/02/2012). The most shared links for the topic, again from TweetLevel, reflect the fierce criticism and debate this campaign has triggered in social and hybrid media since the close of SXSW (interesting to note here that articles by traditional media (BBC, Telegraph, The New York Times) are not fuelling the debate but are only reporting on it.

So why has this initiative failed so spectacularly and so publically? It’s mostly a matter of perception. Countless social programmes promote jobs for the homeless and encourage (and/ or require) the benefactors to participate rather than give hand-outs; the Street Newspaper/ The Big Issue and Habitat for Humanity, for example. But this wasn’t a social programme, let’s be frank here, this was a PR campaign by a marketing agency and the agency failed on one of the most critical principles of any digital marketing campaign; context. As a result, the campaign left users and pundits feeling uncomfortable and with a negative perception of the BBH brand.

The objectives of this campaign were mostly sound and pretty good – connect the visiting SXSW technology community with the local Austin community by highlighting the social problem of an ‘invisible’ homeless population – but the context, and some of the content, was all wrong. BBH lacked a fundamental link connecting the plight of Austin’s homeless with the core audience and objective for the marketing agency.  Instead if feeling like they’ve done something for good, users said they felt awkward about the whole thing. That’s not good at all. 

You may argue that this was a CSR or even a local community support initiative (BBH does) however contextually BBH – a UK-based agency – did not have a building block of sustained social credibility local market/ community to support such a campaign. We all know that context is king. BBH failed to question; what kind of marketing message are conference goers receptive to in this context? And, is the platform (in this case the homeless participants) contextually relevant to our business and our customers. If this campaign initiative was run by a local charitable organization or local city of Austin chamber of commerce type organization, it’s quite possible we’d be talking about an ingenious campaign designed to promote the local community with the technology elite who descend on Austin once a year. But why an agency? What is the connection?

Surly, as a marketing agency BBH should have known better? Question what you will about the motivations for the campaign, the truth of the matter is that contextually, the language of the campaign was all wrong as well. The mechanics of the campaign gave observers an impression that the initiative lacked purpose and therefore the language used fell flat and communicated exploitation of the homeless participants instead of municipal support. Speaking about the criticism detailed in media reports, journalist and freelance writer Mic Wright said, “It was all in the language. [The homeless participants] WERE the hotspots.”

Behind the scenes and once you visit the BBH website, you might feel otherwise, but as digital marketers we know that the first 5 seconds is what counts. Saneel Radia, the director of innovation at BBH Labs who oversaw the project, told one reporter that the company was not taking advantage of the homeless volunteers. He said, “We saw it as a means to raise awareness by giving homeless people a way to engage with mainstream society and talk to people,” he said. “The hot spot is a way for them to tell their story.” But giving a homeless man a t-shirt that effectively says “I am a homeless hotspot” – where is the tact in that?

If BBH had employed events staff to wander around the show broadcasting wireless hotspots, we would have had no problem with this. It is that fact that they felt the need to make a point with employing the homeless and made it so visible that impacted reception of the campaign. Within the context of SXSW, this simply didn’t gel and the experience left users and pundits feeling uncomfortable. Better, BBH should have employed local community members and activists/ influencers with a message to SXSW attendees to get to know local Austin, the good and the bad. In fact, we’ve used TweetLevel to find a simple list of influencers in the Austin, TX area talking about the homeless. In terms of delivery, a cleanly designed app would have neatly connected SXSW conference goers with stories about their adopted home for the long-weekend. In the right context, with some killer content, this could have been a powerful campaign.

@jacqui_fleming

bearFishing where the fish are is something that bears have known for years but many folk who use Twitter seem to have forgotten. We cannot simply think our message will be heard by tweeting ourselves which is why we try and target influential people via tools like TweetLevel and BlogLevel.

However, this isn’t the only way of doing it. What I have been doing successfully over the past year is taking part in twitter chats. These are regular conversations that take place about a specific subject on twitter normally for an hour and owned by a specific hashtag.

For example,

· if you are targeting the SME market then look no further than #smallbizchat

· If you are focussing on innovation then #Innochat on Thursdays is the one for you

· Are you a small business that uses LinkedIn (client) – why not use the chat that shares best ways for businesses to use this service on #linkedinchat

My personal favourites are #influencechat and #measurepr – but suggest you look at this larger list to see which ones can help you

Any questions, just chat with me @jonnybentwood

End note: My thanks to Judy Gombita for pointing this list out to me who also wants me to plug Windmill Networking #PR column Wed, Social Capital Byte: Institutionalizing Parity in B2B Relationships

@jonnybentwood

Some of you may well have seen this research from the Guardian earlier this week, which aimed to highlight the top journalist tweeters in the UK – headed by Neil Mann, aka @fieldproducer, digital news editor at Sky News.

There just seemed to be one problem – the list was, perhaps unsurprisingly, absolutely dominated by Grauniad hacks, with half the top ten being employed by the paper running the research. The highest placed non-Guardian ‘paper scribe on the list was the FT’s Tim Bradshaw who came in a lowly eighteenth, while the Times could only muster one journalist in the top 50 – Michael Savage, in at #35.

Shurely shome mishtake?

We’ve run the findings through the tweetlevel  algorithm instead to give it some more context, and the same list appear in a very different order, with Charles Arthur the highest placed hack on the list, and afore-mentioned Tim Bradshaw rocketing up to eighth.

Check out the revised list here.

top tweeters grab

Picking a couple of other tech journos at random, there were notable exceptions in the original list: from The Times, Murad Ahmed would have been in the top fifty; the Telegraph’s digital media editor Emma Barnett would have triumphed in at #20; while arguably one of the UK’s most influential tech industry bods, Mike Butcher, would have come in joint with Tim Bradshaw.

To be clear, we’re not saying ‘our list is better than yours’, nor are we saying our methodology is better – we’re just saying that if you’re producing a list of the influential people in your industry, it might be a good idea to widen the scope to people who don’t work for you.

Let us know what you make of our version of the list originally produced by the Guardian. For more info on the algorithm used, make your brain hurt reading this.

TweetLevel and BlogLevel are two purpose built tools for the PR industry that aim to be a GPS for navigating influence. At its heart is an open and transparent algorithm that seeks to measure who is important within each social media channel.

image

Resting behind the methodology are several key insights:

Influence without context is irrelevant

Understanding measurement is more than simply putting a name into an algorithm. It’s a process. If you are looking at influence, then go for Justin Bieber – however, if you are looking to get the right people to speak about you and engage on your behalf then understanding context is critical. This is what the first step in TweetLevel that we always recommend anyone follows is context. Using Boolean logic, anyone can enter a search term to identify who are currently the most influential people about a certain subject. Only when you have identified who these people can you source relevant measurement metrics. The process that it follows is:

  1. Which people have the largest share of voice about a specific search topic
  2. Ranking the top 100 people by their SOV, we then import these names into TweetLevel to identify their influence score
  3. We recommend that brands should focus on people with a score between 65 and 85. Above that score people are significant but are in the realms of the “Today Show” and PR pros must question how likely is it that their message will want to to be heard by this target.

Much as we would like to engage with every relevant person, the sad truth is that most people do not have the time or resources to do so. We therefore need to prioritise which people to focus on. This process explains how to find them.

Popularity does not equal influence

The above statement is bold and almost 100% true. I am not naive if you are popular then by default you are more likely to be influential. However, this is just one factor that can measure how important someone is. The numbers of followers someone has is interesting to me but not as key as how somebody engages in relevant conversations or create ideas that then resonate through the social web.

Engagement is not the same as activity

People have long understood the difference between broadcasting and engaging. As communication channels become more dynamic and interactive, true influence is derived by having two-way dialogues, asking questions and by posting interesting and informative content.

This is the time of the new influential – idea starters and amplifiers are both influential in their own way

If you compare the lists of top tweeters from TweetLevel with other tools on the market, there will be a marked difference in that in our lists you will see some people with comparatively few followers and yet with a higher influence score than their peers who may be extremely popular. The reason for this is that TweetLevel identifies which people create ideas which are then amplified. This isn’t to say that both types of people aren’t important but more that they are both key targets and should be engaged with.

We are at a tipping point where sociology and technology can assist us in engagement

imageContinuing the argument above, we are at a wonderful position whereby sociology and technology are merging to assist us in understanding how to engage with different audiences in the most appropriate manner. TweetLevel can identify what type of person an individual is by their online behaviour. We call this the ‘Topology of Influence

We believe that influence is derived by how information flows between different people. Backed-up by the Web Science team at the University of Southampton, influential people can be: idea starters, amplifiers, curators, commentators or viewers.

People within these different categories all portray behavioural attributes that when complemented are more likely to promote the spread of a message. For example with Idea Starters I would engage in a deep structured discussion and with amplifiers I would understand their need to satisfy their readership and provide them with pre-packaged information that they can easily repurpose.

TweetLevel measures influence and more…

Understanding which people engage with is just half the story. Nothing irks me more than hearing someone has emailed their boss saying that “so-and-so has just retweeted us and they have 30 thousand followers”. Big Deal.

What is more important is ‘has there been a significant change in the amount of conversation that you have catalysed’ and ‘defining whether people are talking and sharing the points you want them to’. These are key measurement metrics which Tweet and BlogLevel also measures.

image   image image

image

However, I would always counsel having a consistent measurement approach:

  • At the beginning of a campaign: to set a benchmark and ensure your message is relevant
  • During the campaign: are there peaks at the right time? Do we need to course correct, issues hijack or amend our message?
  • At the end of the campaign: how have we done? Have the right people engaged? Has the right message been echoed and spread?

What the tools can and can’t do

TweetLevel and BlogLevel are tools that help PR pros take what would be either an expensive or time consuming process into a free (these sites don’t cost) and quick job (reduces the analysis time from days to minutes). However, they don’t fully automate the identification or measurement role – this is intentionally done as a human mind always needs to validate and sanity check the results.

There are of course other excellent tools in the market. However, TweetLevel and BlogLevel are not trying to compete with them. These are purpose built to mirror the way we work so we do not need to retrofit our work to complement their tools. These are games or perks but simply a way that we can do our job better.

Of course there are some added extras that go beyond measurement – for example identifying what individuals most frequently discuss, who they influence, who influences them and other people who talk about similar subjects.

image

I like to say that these tools are in continuous beta. As new developments arise or demand for specific features are required, we will update the tools accordingly.

What’s next?

To answer this simple question I would like to refer you to a simple quote that Jeremiah Owyang once said to me:

If you want to influence me, be in a conversation with me – wherever that conversation takes place.

I will be discussing both TweetLevel and BlogLevel at the forthcoming #measurePR chat on 30 August at 12-1 pm ET. I hope you can participate and join the debate. @jenzings will be hosting and my thanks to @shonali for organising.

In a scene reminiscent of the 90s cult classic ‘Lawnmower Man’, it is understood that Jonny Bentwood – pioneer of the celebrated Tweetlevel online popularity contest – has been consumed by his own Twitter algorithm; in the process becoming the first human being to become physically socially networked.twitter all

Concerns were first raised when semi-digital manifestations of Bentwood appeared on computer screens thoughout the company, during prolonged physical absence from his desk alongside the Edelman Technology team – concerns that were confirmed when he disappeared altogether and became wholly digital.

The first unexplained phenomenon was the appearance of a series of seemingly motivational – yet slightly unsettling – Twitter related posters (right).  These were followed by ghostly bangings from the server room, alongside muffled, digital screams about ‘influence’, ‘popularity’, ‘engagement’ and ‘trust’. White-noise and repeated yells of the word ‘retweet’ have also been heard in what is being dubbed a ‘polterzeitgeist haunting’ by experts.

It is now feared that Jonny has used his integration into the Twitter portal to access other online territories, with the Edelman server under attack, and traditional media lists being erased from client files to be replaced by an audio file of someone reading out complex and often nonsensical algorithms and laughing maniacally.

"At this stage we’re at a loss as to what to do," commented a spokesperson, "this is entirely without precedent and we’re unsure whether to eradicate the threat; monitor and analyse it; or whether this is in fact an innovative route to influencers not seen before, which we can exploit and use to bypass newly evolving platforms being used by early adopters. Harnessing Jonny’s new digital access and power has the potential to put us so far ahead of the curve we may well rebound back onto ourselves and BECOME the curve."

It is understood several Helpdesk tickets have been raised, and allocated to a member of the IT team to handle; although an unnamed IT member said that they were unsure as to how to tackle this threat other than "turning something off and on again and hoping for the best"

image

Analysing the top 1,000 twitter accounts associated with technology, Edelman has found that more and more brands – rather than individual people – are using Twitter  to influence and engage with their customers and prospects. According to tweetlevel.com analysis, in April,  over 40 of the top 200 technology influencers were brands such as Google and Bing up from approximately 25 in January.

Many of these brands saw their influence rise dramatically too.  Between January 2010 and April 2010,  Ubertwiter jumped from 57th place to 13th, HTC leapt 103places from 194 to 91st and Google rose to 15th from 17th while its rival bing fell from  118th to 186th.  The highest ranked brands were  Wired (9), TechCrunch (11), Ubertwitter (13) and Google (15).  I have included media mastheads that tweet or rather broadcast as brands but classed as personal when it is the individual journalist who tweets as this tends to narrowcast and involve personal as well as news content. These numbers are accurate as of 23 April.

On the whole, relatively  few media make it into the top 200 of the 45 brands and only 7 were these kind of branded media. Individual journalists also only form a small proportion of the list where the biggest group remains industry gurus or consultants. Indeed half of the top 10 fall into this category: Jeff Pulver (3), garyvee (5), rww (6), Chris Brogan (7) and Tim O’Reilly (8). Nevertheless as a journalist, Mashable’s Pete Cashmore remains clearly in number one position for influence though very low on engagement.

The blurring of traditional influencer roles is also matched by a blurring of personal and brand tweet names, the clearest example being Scobilizer: guru, journalist, bird or plane?  Even analysts are becoming strangely personalized, James Governor’s Monkchips being a great example. Top of the analyst ranking is Jeremiah Owyang who comes in at number 18.

Is this de-personalisation of Twitter polluting the platform and reducing it value as an objective way of looking at the technology landscape?  I definitely think a level of authenticity is being lost even when a person was tweeting as an employee there was often a belief in single point of view albeit often either very positive or negative when it came to their employer.  However, brand tweeting is by its nature more faceless and an amalgamation of the collective views behind the brand.  This is clearly helpful in that it is officially the view of the brand owner but in truth, this also becomes less engaging.  I would love to hear your thought.

If you want to follow this list, you can automatically follow the top 250 by clicking here, or 251 – 500, 501 – 750 and 751 – 1000)  (via Tweepml) or via this twitter list.

For the full list see below:

Account Influence Popularity Engagement Trust
1 mashable 82.3 94 8.7 95.4
2 huffingtonpost 82 86 46.7 93.7
3 jeffpulver 80.6 82.9 80.1 83.5
4 stephenfry 80.5 92 58.2 85
5 garyvee 80.4 88.5 82.6 81.5
6 rww 79.7 89.8 46.6 87.5
7 chrisbrogan 79.6 76.5 74.3 83.3
8 timoreilly 77.7 91.9 56 84.1
9 wired 77.2 84.8 47.9 86.1
10 jack 76.7 92.6 54.9 82.1
11 TechCrunch 76.4 91.6 7.7 94.7
12 GuyKawasaki 76.2 80 52.2 90.6
13 UberTwiter 75.8 89.6 86.9 86.8
14 zappos 75.7 92.9 42.5 81.2
15 google 75.4 94.7 45 84.3
16 engadget 75.2 74.6 3.7 86.5
17 biz 75.2 92.8 44.2 81.6
18 jowyang 74.7 71.6 50.8 78.3
19 loic 74.7 69.1 66.6 76.8
20 problogger 74.6 74.3 61.5 77.2
21 BarackObama 74.6 98.1 44.8 93.8
22 ijustine 74.3 90.4 56 71.4
23 om 73.9 91 56.2 77.6
24 socialmedia2day 73.9 65.8 42.4 80
25 MCHammer 73.8 93.5 63 81.4
26 anildash 73.8 82.4 66.8 77.4
27 whitehouse 73.8 93.2 41.5 83.6
28 kevinrose 73.8 90.5 64.1 74.9
29 foursquare 73.2 70.7 54.6 77.6
30 NASA 73 82.9 48 89.3
31 Scobleizer 72.9 75.9 64.6 80.8
32 gizmodojapan 72.9 64.3 9 76.1
33 jeffjarvis 72.8 68.9 55.6 74.3
34 ev 72.6 90.7 73.2 80.8
35 kogure 72.2 79.8 64.3 70.4
36 sitepointdotcom 72.1 71.5 66 71
37 sacca 71.9 91.4 70.7 70.7
38 steverubel 71.8 68.8 60.2 73.7
39 Gizmodo 71.7 72.2 46.2 88.5
40 aots 71.7 72.5 54.9 64.9
41 laughingsquid 71.7 71.9 53.9 74.1
42 taguchi 71.4 80.4 58.1 71.9
43 copyblogger 71.4 70.2 55.9 75.8
44 rleseberg 71.4 75.7 53 69.8
45 arstechnica 71.4 70.6 10 73.2
46 ndorokakung 70.9 59.9 65.3 82.2
47 sciam 70.8 70.9 44.7 73
48 brainpicker 70.7 63 39.2 77.1
49 zaibatsu 70.7 74.8 59.8 79.4
50 gruber 70.7 70.9 72.2 76.7
51 g4tv 70.5 90.3 40.2 74.8
52 prsarahevans 70.2 69.1 64.2 66.6
53 Jason 70.1 74.3 63.6 74.5
54 MariSmith 70.1 72 71.4 75.6
55 kotoripiyopiyo 70.1 79.9 67.5 66.1
56 sasakitoshinao 69.8 68.4 44.7 66.1
57 suziperry 69.8 69 47.9 70.7
58 abduzeedo 69.8 70 44.2 73.2
59 olhardigital 69.7 69.4 8 74.1
60 gonzague 69.7 58.3 73.1 64.9
61 facebook 69.7 79.7 50.9 78.3
62 leolaporte 69.7 78.6 45.1 66.8
63 zoecello 69.6 91.6 67.8 63.5
64 TheNextWeb 69.6 84.4 49.1 78.9
65 guardiantech 69.5 92.6 42.8 65.8
66 shanselman 69.4 65.6 73.2 61.6
67 majornelson 69.2 75 36 74.8
68 mikebutcher 69.1 62.5 56 68.1
69 crackberry 69.1 68.6 55 75.9
70 codinghorror 69 66.7 52 72.9
71 Pogue 69 91.4 48 77.5
72 kittykills 69 68 70.8 61.5
73 Veronica 68.7 92.7 78.4 70.4
74 jasonfried 68.7 66.8 66 68.2
75 karaswisher 68.6 87.1 62.4 67.4
76 Cardoso 68.6 64.5 59.6 77.7
77 interney 68.6 68.5 51.8 67
78 charlesarthur 68.5 60.6 69.2 66.3
79 DanSchawbel 68.4 72.9 51.2 76.3
80 earcos 68.4 73.8 58 67.8
81 jayrosen_nyu 68.1 67.7 49.3 65.8
82 tokuriki 68.1 79.3 58.2 63.3
83 davewiner 67.7 66.8 49.1 65.2
84 skydiver 67.4 71.1 41.4 65.9
85 ChrisPirillo 67.4 73.2 42.7 76.2
86 Bauart 67.3 63 38.2 83.9
87 briansolis 67.3 70.5 58.7 71.6
88 jemimakiss 67.3 62.5 53.7 66.3
89 ruskin147 67.3 62.4 53.5 64.3
90 nobi 67.2 79.5 55 61.3
91 htc 67.2 79.6 58 65.5
92 msuster 67.2 59 68.9 62.5
93 mattcutts 67 71 64.7 63.4
94 amazonmp3 66.9 92.1 43.8 68.4
95 dollars5 66.9 75.7 10 67.3
96 gatesfoundation 66.9 83.3 39.9 77.1
97 eMarketer 66.8 65.4 42 77.6
98 parislemon 66.8 62.7 48.4 59.7
99 migueldeicaza 66.7 60.9 64.8 62.5
100 mollywood 66.7 67.1 65.1 61.2
101 masason 66.6 80.2 44.5 80.4
102 marshallk 66.6 62.9 46.6 62.1
103 acedtect 66.6 65.6 59.8 61.9
104 armano 66.4 66.1 64.7 71.4
105 MSWindows 66.3 72.7 75.1 67.9
106 nickellis 66.2 58.3 65.7 60.2
107 jmatuk 66.2 61.6 61.8 61.3
108 hnshah 66.1 63.5 48 61.8
109 stop 66 68.5 59.1 74.5
110 ekozlov 65.8 70.9 47.8 58.5
111 ambermac 65.7 68.9 73.9 57.2
112 tferriss 65.7 74.7 39.4 66.5
113 benjamincohen 65.7 53 59.3 66.6
114 scottgu 65.7 65.1 46.8 69.8
115 alexalbrecht 65.7 72.7 44 61.8
116 mathewi 65.5 61.4 52.3 61.6
117 elijahmanor 65.5 67.1 53.3 60.7
118 craignewmark 65.4 64.7 57.4 59.4
119 andybeal 65.4 62.8 56.4 64.2
120 tsuda 65.4 70.7 46.9 55.7
121 davemcclure 65.4 66.3 55.5 59.4
122 TEDchris 65.3 90.5 41.2 76
123 woot 65.3 92.8 2.7 68.5
124 jackschofield 65.3 59.8 54.5 61.3
125 sixrevisions 65.1 63.9 48.1 73.6
126 mcuban 64.9 76.7 51.8 61.7
127 IDGNow 64.8 66.1 9.3 73.2
128 dalmaer 64.8 56.5 51.4 62.1
129 pkedrosky 64.8 67.1 52.4 61.4
130 rsarver 64.8 81.6 68.4 54.9
131 adanvecindad 64.7 54.5 59.4 58.6
132 boxee 64.7 66.5 61.9 56.1
133 martinvars 64.6 62 42.7 63.5
134 bitrebels 64.5 57.5 8.3 63.3
135 JasonFalls 64.5 65.7 62.7 65.6
136 waze 64.5 54.6 77.9 55.2
137 chrismessina 64.5 64.6 56.2 58.7
138 taromatsumura 64.5 78.9 50.6 56.7
139 al3x 64.4 63.6 64.5 59.4
140 kevinmarks 64.4 61.3 53.5 59.5
141 marilink 64.4 74.7 51.6 59.8
142 fshin2000 64.3 79.1 58.7 53.8
143 Vivoemrede 64.3 65.7 40.5 80.8
144 CaliLewis 64.1 71.9 46.6 68.2
145 DAChesterFrench 64.1 89.5 62.6 62.2
146 ariannahuff 64 83.8 40 64
147 fredwilson 63.9 71.5 44.3 58.4
148 johnbattelle 63.9 66.7 50.4 57.4
149 yahoo 63.9 67.2 42.3 59
150 monkchips 63.8 58.5 51.3 58.9
151 uschles 63.7 71.8 79.3 51.9
152 olyapka 63.7 53.8 73.1 56
153 pandora_radio 63.6 68.3 68.9 52.9
154 waynesutton 63.6 66.8 60.2 55.2
155 djtechnasty 63.5 64.3 49 56.3
156 ollehkt 63.5 64.4 75.3 62.2
157 VentureBeat 63.4 60.6 8.3 71.7
158 bfeld 63.4 60.8 51 57.8
159 chasejarvis 63.3 67.6 45.3 58.4
160 xguru 63.3 62.2 56.1 51.9
161 gamekicker 63.2 65.4 10 60.5
162 SteveCase 63.2 82.6 43.4 68.3
163 Padmasree 63.1 91.8 67.3 64.9
164 Zee 63.1 64.4 58.5 63.2
165 SocialMedia411 63.1 72 40.8 67.3
166 Korben 63 57.4 56.4 67
167 forrester 63 68.3 45.7 58.4
168 Moonalice 62.8 73.4 45.6 68.4
169 arturoelias 62.7 66.3 7.3 59.9
170 BlackBerry 62.7 72.2 56.5 74.2
171 forces2 62.6 67.8 39.5 61.6
172 joshuatopolsky 62.5 62.1 49.6 60.9
173 thurrott 62.4 67.4 52.7 56.3
174 jolieodell 62.4 59.3 51.5 57.1
175 alt1040 62.4 63.3 2 63.3
176 renailemay 62.4 53 57.7 52.6
177 timbray 62.3 59.5 50.7 56.8
178 developerworks 62.3 66 38.6 58.7
179 JasonBradbury 62.3 70.4 76.3 57.9
180 doctorow 62.3 70.6 63.9 61.7
181 tsuruaki 62.2 58.9 46.3 55.1
182 bing 62.2 69.3 68.1 51.3
183 prblog 62.1 62.8 59 53.8
184 ginatrapani 62 71 47.6 57.4
185 shwood 61.9 62.9 58.3 53.3
186 suadd 61.9 79.7 44 54.2
187 pcworld 61.8 63.1 44.5 65.6
188 dharmesh 61.8 64.5 55.6 62.5
189 macworld 61.8 70 38.9 56.9
190 dannysullivan 61.8 69.6 46.6 63.7
191 japonton 61.7 55.2 73.3 51.8
192 tamar 61.7 62.5 62 56
193 BethHarte 61.7 64 74.3 57.9
194 Phonedog_Noah 61.7 60.1 58 62.8
195 thomaspower 61.7 60.7 42.7 53.5
196 stilgherrian 61.6 54 42.9 53.9
197 rwang0 61.6 57.3 54.7 54.2
198 patricknorton 61.5 69.3 47.2 57.3
199 hermioneway 61.5 56.7 63.9 52.8
200 arrington 61.4 66.4 44.1 57.4
201 bbgeeks 61.3 66 74 51.1
202 RicardoZamora 61.3 60.4 73 59.1
203 algore 61.3 94.4 33.8 74.9
204 digg_technews 61.3 62.7 39.7 62.5
205 anakcerdas 61.3 47.6 65.7 53.5
206 LanceUlanoff 61.1 63.3 43.8 62.1
207 posterous 61.1 61.1 51.1 60.1
208 willsmith 61 58.3 67.9 52.1
209 thecreativeone 61 58.1 58.5 50.1
210 stoweboyd 61 60.4 55.8 50.3
211 prtini 60.9 55.4 59.2 53.8
212 natalidelconte 60.9 66.8 70.3 50.5
213 howtogeek 60.9 59.2 64.8 57
214 qwghlm 60.8 49.6 56.6 58.6
215 yoris 60.8 53.3 46.9 59
216 Ihnatko 60.8 67.4 58.2 60.5
217 glennhilton 60.8 66.5 67.1 50.8
218 jsnell 60.7 61.4 71.9 49.5
219 ConversationAge 60.7 63.2 65.3 59.1
220 MuscleNerd 60.7 70.2 75.5 60
221 walidmrealtor 60.6 67.6 44.7 53.7
222 info_plantao 60.6 65.9 2 72.9
223 apisanty 60.6 53 55.3 51.6
224 rk 60.6 81.6 66.7 52.9
225 link_estadao 60.5 61.5 6 59.5
226 tim 60.5 56.6 56.9 55.3
227 hdmoore 60.5 56.9 70.1 52.4
228 caro 60.5 61 53.6 53.2
229 ba_anderson 60.5 54.9 51.5 55.4
230 Jesse 60.5 65.6 45.5 60.4
231 raduboncea 60.5 67.1 10 56.4
232 adamostrow 60.4 63.9 45.8 53.1
233 harrymccracken 60.4 63.5 50.6 53.3
234 JesseNewhart 60.4 69 47.8 62.4
235 delbius 60.4 82.2 65.5 50.2
236 ryancarson 60.3 64.7 69.9 49.3
237 ryanstewart 60.3 58.8 63.1 52.5
238 CNETNews 60.3 71.9 0.7 72.7
239 g1tecnologia 60.2 64.6 3 63.8
240 nick 60.2 60.9 8.3 58.7
241 HTC_Ru 60.2 55.9 80.7 54.8
242 erickschonfeld 60.1 61.4 56 57.5
243 Kotaku 60.1 64.7 0.3 72.8
244 prebynski 60 65.6 55.4 53.3
245 iamlittleboots 60 70.3 49.5 52.6
246 Econsultancy 60 64 39.6 64.1
247 JohnFMoore 60 61.5 52.6 59.4
248 mediaphyter 60 62.8 62.7 50.2
249 tacanderson 59.9 57.2 48.3 51.8
250 aulia 59.9 51.5 64.2 51.8
251 louisgray 59.9 61.9 48.4 52.8
252 jblm 59.9 70.9 46.6 49.5
253 mg 59.9 56.2 64.5 53.8
254 zephoria 59.9 66 45 55.5
255 cxi 59.8 62.9 49.9 49.7
256 blam 59.8 59 63.6 49.9
257 rustybrick 59.7 58.3 40.4 55.1
258 OReillyMedia 59.7 62.6 48 58.2
259 BBCClick 59.7 93.2 42.5 69.1
260 dtapscott 59.6 61.9 38.7 55.7
261 CandiceNicolePR 59.6 57.6 56.3 59
262 dsilverman 59.5 59.5 55.6 50.7
263 film_girl 59.5 61.9 63.7 48.2
264 AaronStrout 59.5 61.3 68.1 54.8
265 hharteveldt 59.4 52.7 50.6 54.2
266 gigaom 59.4 62.5 2.7 65.5
267 joehewitt 59.3 63.1 64.7 54
268 moniguzman 59.3 58.7 58.3 48.8
269 uncrate 59.3 65.4 10 60.8
270 jbruin 59.2 60.2 49.9 50.6
271 InsideGaming 59.2 65 45.6 58.1
272 onlyapplenews 59.2 63.1 10 55
273 JPBarlow 59.2 59.8 44.6 62.4
274 ClaytonMorris 59.2 64.9 58 55.8
275 zsafwan 59.2 60 53.2 50.4
276 whereivebeen 59.1 69.4 48.2 58.9
277 PRCog 59.1 52.6 72.6 54.9
278 benparr 59.1 62.5 49.9 58.1
279 petezin 59 54.5 8.7 52.4
280 Stammy 59 61.1 59.2 57.7
281 Spotify 59 68.6 65.4 53
282 bokardo 58.9 60.3 49.3 52.2
283 sanuzis 58.9 62.2 37.3 53.4
284 amous 58.9 66.8 58.2 47
285 MalcolmMoore 58.9 53.2 44.5 62.4
286 shonali 58.9 57.3 64.4 47
287 fseixas 58.8 58.7 60.5 50.6
288 budip 58.8 53.1 59.3 51.4
289 vendeesign 58.7 54.8 42.2 54.6
290 danielbru 58.6 82.3 41.1 51.2
291 patphelan 58.5 54.2 54.8 51.4
292 PhilipNowak 58.5 62.9 52.7 57.4
293 outsideorg 58.5 50.6 75.8 45.2
294 bxchen 58.5 54.6 49 49.7
295 greentechmedia 58.5 57.8 37.5 54.2
296 THErealDVORAK 58.5 72.1 68.3 55.5
297 Mediabistro 58.4 71.2 42.7 58.8
298 amymengel 58.4 52.3 59.7 47.5
299 florianseroussi 58.4 67.3 53 48.1
300 ch9 58.3 60.8 4.7 60.6
301 TheFeed 58.2 64.8 4 66.5
302 tomcoates 58.1 57.8 58.8 48.7
303 shanerichmond 58.1 54.8 50 50.7
304 meyerweb 58.1 65.6 53.2 50.3
305 ryan 58 67.1 60.1 59.2
306 charleneli 58 67.6 52.1 50.9
307 CarinaK 58 71.6 59.7 52.8
308 seanbonner 58 57.1 51.4 51.5
309 reybango 57.9 52.2 67.1 45.8
310 jasonhiner 57.9 60.4 41.1 53.7
311 johnmaeda 57.8 69.2 34.4 55
312 radioedit 57.8 46.4 72.2 50.1
313 ApplisiPhone 57.8 59.2 42.7 61.2
314 augieray 57.7 55.1 56.1 47.4
315 emilychang 57.7 63 56.2 47.5
316 iwaonakayama 57.7 57.3 44.2 56.7
317 householdhacker 57.7 63.4 54.2 48.5
318 Joe 57.7 65.8 51.4 59.9
319 richardlai 57.6 48.2 71.1 47.4
320 tomforemski 57.6 58.9 44.7 49.3
321 peterkim 57.6 61.1 57.4 50.6
322 stiennon 57.6 63.9 52 48.7
323 EntMagazine 57.6 65.1 43.1 59.6
324 billt 57.6 59.6 49.7 49.9
325 jobsworth 57.6 55.3 58.4 44.6
326 pierre 57.6 82.5 53.3 46.9
327 BTCare 57.5 56 83.5 45.7
328 jopkins 57.5 48.4 56.2 53.1
329 dsearls 57.5 60.1 46.8 50
330 gamespot 57.5 73.7 3.3 54.8
331 wolwol 57.5 44.3 68.2 49.8
332 TomRaftery 57.4 57.2 56 56.2
333 pud 57.3 61.9 53.1 49
334 ForbesTech 57.3 71.5 41.9 58.3
335 telediariomty 57.3 56.3 2 56.5
336 cote 57.3 53.5 52.2 47.1
337 timesjoanna 57.3 54.3 61.1 47.3
338 VodafoneUK 57.3 60.8 83.3 43.2
339 tekusuke 57.3 55.4 44.5 43.1
340 Gartenberg 57.2 57.1 58.8 55.1
341 geekdotcom 57.2 56.5 8.7 53.3
342 gnat 57.2 54.1 63.3 45.8
343 cfleury 57.2 62.9 59 43.9
344 eldarmurtazin 57.2 57.8 61.8 50.1
345 mfauscette 57.2 57.6 41.8 50.4
346 bangwinissimo 57.1 47.6 55.1 51.3
347 centernetworks 57.1 59.3 56.4 46.5
348 whiteafrican 57.1 56.5 44.6 50.6
349 rilescat 57.1 66.3 41.3 47.1
350 MrPointyHead 57 59.5 66.6 51.3
351 Genuine 57 60.4 61.5 53.3
352 rands 57 61.2 6.3 53
353 jhagel 57 51.3 41 51.2
354 FortuneMagazine 56.9 72.1 34.5 61
355 ThisIsSethsBlog 56.9 67.2 0 74.2
356 latimestech 56.9 61.2 8.7 52.5
357 Computerworld 56.9 60.8 39.4 56.4
358 simonw 56.9 57.4 49.1 46.2
359 dotmotion 56.9 56.3 62.1 46.4
360 ScienceChannel 56.9 69.9 40 59.7
361 anddjournal 56.8 70.2 45.7 44.8
362 dailyrt 56.8 54.1 41.4 49.8
363 richardbranson 56.8 82.2 38.8 55
364 garotasemfio 56.8 63.1 49.8 47.6
365 fromedome 56.8 55 52.3 45.7
366 AlisonBrodPR 56.7 56.3 46.7 57.9
367 juliaallison 56.7 63.8 40 49.4
368 hulu 56.7 66.9 51.4 46.9
369 reckless 56.7 54.9 43.2 48.9
370 jimmy_wales 56.7 62.7 48.2 53.7
371 StockTwits 56.7 74.8 44.6 55.7
372 spotonpr 56.7 53.8 51.2 48.1
373 riccardowired 56.6 61.8 54.1 48.8
374 drnatalie 56.6 58 42.5 47.1
375 ramyaprajna 56.6 47.4 58.7 49.9
376 saschasegan 56.6 55.7 59.1 48
377 gillianshaw 56.6 53.8 61.8 47
378 hoovers 56.5 57.3 61.4 45.7
379 mkapor 56.5 63.1 43.1 52
380 AdweekDotCom 56.4 65.4 37.8 56.7
381 threatpost 56.4 53.3 34.7 52.7
382 Werner 56.4 60.9 44.5 57.2
383 DavidSpinks 56.4 53 62.8 53.7
384 PR_Couture 56.4 62.8 59.4 53.6
385 geekpage 56.4 56.5 54.9 46
386 dberkowitz 56.3 59.4 64.9 43.1
387 stedavies 56.3 54 63.7 45.8
388 imhassan 56.3 47.7 53.2 49.4
389 jeff 56.3 59.2 38.4 51.1
390 ValerieSimon 56.2 55.3 51.5 55.1
391 willfrancis 56.2 70.6 42.7 46
392 hpnews 56.2 62.7 37.1 49.8
393 MicrosoftPress 56.2 60.7 50 56.2
394 hisa_kami 56.2 56.4 52.3 40.3
395 CAmorales 56.1 48.5 67.5 51.1
396 cnetuk 56.1 58.4 9.7 49.8
397 mtrends 56 55.1 44.1 48.1
398 jasonkincaid 56 56.7 51 43.8
399 markevans 56 55.4 41 50.2
400 psmith 56 49.6 52.9 48
401 furrier 56 57.1 46.1 46.9
402 sarahintampa 56 58.3 52.1 45.7
403 svartling 56 58.1 42.9 46.9
404 nixxin 56 49.2 59.1 46.4
405 shamir 56 69.4 42.9 45.7
406 chrisheuer 56 61.1 47.4 44.7
407 ekolsky 56 48.6 57.4 50
408 bre 55.9 59.7 40.9 44.1
409 jkottke 55.9 68.4 45.2 51.9
410 patrick 55.9 55.9 47.4 50.7
411 micah 55.9 58.7 55.5 44.5
412 CiscoSystems 55.9 66 49.6 55.6
413 shoinoue 55.8 45.9 61 44.6
414 johnbiggs 55.8 53.8 47.5 45.4
415 rafatali 55.7 54.4 40.8 50
416 markdavidson 55.7 70.8 57.6 41.4
417 EverythingMS 55.7 59.7 8 58.7
418 joystiq 55.6 64.8 6.7 50.2
419 TapTapRevenge 55.6 61.2 74.6 53.5
420 pandemia 55.6 61.1 35.1 49.6
421 AndrewWarner 55.5 63.2 57.5 51.5
422 stuartbruce 55.5 52 45.7 49.6
423 bijan 55.5 58.7 43.5 44.5
424 webdicas 55.5 69.4 44.5 46.6
425 JoeTrippi 55.5 90.3 40 53.9
426 heathermeeker 55.4 55.9 57.1 42
427 elizabethsosnow 55.4 55.2 54.8 44.8
428 gray_ru 55.3 54.6 51.7 47
429 Boris 55.3 59.8 45.8 54.9
430 peterrojas 55.3 61.9 56.6 45.3
431 labnol 55.3 59.3 55.3 43.3
432 SoldierKnowBest 55.2 61.7 48.5 54.2
433 FoneArena 55.2 50.9 58.1 52.5
434 Verizon 55.2 57.9 55.6 49.7
435 ejacqui 55.1 55.3 56.7 42.5
436 chartier 55.1 52.5 56.6 42.2
437 caroline 55.1 82.3 44 52.3
438 lessig 55 68.7 37.6 53.4
439 BarbaraNixon 55 57.6 60.6 52.1
440 EricMessa 55 53 52.1 52.7
441 jedhallam 55 51.1 53 46
442 ChrisSpagnuolo 55 71.4 57.4 49.7
443 elisanajera 55 48 61.3 46
444 dani_escalante 54.9 56.6 51.1 46.1
445 amirk 54.9 44 71 44.2
446 mostlylisa 54.9 63.3 51.7 55.1
447 GavinNewsom 54.9 91.6 39.5 55.6
448 Joi 54.9 62.4 37.5 52.9
449 mager 54.8 55.9 46.7 43.2
450 WilHarris 54.8 62.8 40.4 56.8
451 alexlindsay 54.8 63.1 50.6 44.2
452 phonescooper 54.8 52.1 54.4 44.5
453 sorenmacbeth 54.7 76.1 55.5 41.3
454 dieralisson 54.7 68.9 10 43.5
455 Matt_Muir 54.7 45.1 58.5 49.7
456 mnpr 54.7 55.5 46.6 46
457 Melanie_Parish 54.7 64 10 51
458 TechnologyGeek 54.6 62.2 10 55.1
459 grader 54.6 70.5 47 37
460 mrbambang 54.6 44.4 57.3 48.4
461 channyun 54.6 55.9 48.6 42.2
462 FirstDigg 54.6 68.9 57.8 49.1
463 GoogleAtWork 54.6 65.7 36.9 56.8
464 kapkap 54.6 48.1 60.9 42.6
465 andrew_chen 54.6 57.6 7.7 49.4
466 torrentfreak 54.6 57.7 8.3 51.1
467 iskw226 54.5 56 36.7 43.4
468 photomatt 54.5 64.2 51.3 47.2
469 ravenme 54.5 53.7 51.4 41.4
470 linuxfoundation 54.5 61.7 36.9 50.5
471 digsby 54.4 74.3 73.7 39
472 arturogarrido 54.4 54.1 52 39.7
473 FragDolls 54.3 92 45.8 51.2
474 tpb 54.2 62.4 34.7 44.9
475 akiyan 54.2 78.2 52.1 38.7
476 iwantmedia 54.2 54.6 4.7 50.9
477 chrisharrington 54.1 76.2 59.5 37.3
478 craigmcgill 54.1 53.8 57.8 49.3
479 Office 54.1 61.4 56.9 54
480 wadds 54.1 50.5 58.3 44
481 stevenbjohnson 54.1 92.1 40.1 58.1
482 smokingapples 54.1 55.4 38.5 50.4
483 jkendrick 54.1 48.6 57.5 44.1
484 peterscampbell 54 43.8 67.2 43.6
485 gamasutra 54 57.3 3 51.7
486 robbrown 54 52.8 50.3 45.8
487 techradar 53.9 58.5 3.3 47.6
488 debs 53.9 57 57.4 40.2
489 LisaRedShoesPR 53.9 54.4 53.4 50.8
490 markmilian 53.9 53.7 61.2 44.7
491 mdh47 53.9 69.2 40.2 41.5
492 jr_raphael 53.9 54.7 50.7 45.8
493 willcom_blog 53.8 52.5 69.5 36.4
494 chris_norton 53.8 51.8 50.9 45.6
495 mahadewa 53.8 56.6 60.4 41.8
496 boxbyte 53.8 53.2 48 44.1
497 jason_pontin 53.7 56.8 47.2 43
498 pprlisa 53.7 57 71.1 36
499 pakman 53.7 81.8 46.8 43.9
500 girlygeekdom 53.6 54.3 57.6 41.1
501 gletham 53.6 50.9 53 42.3
502 rashmi 53.6 56.1 54.2 45
503 shellen 53.6 59.3 43.1 43.4
504 brada 53.6 51 64.8 43.7
505 bobbiejohnson 53.6 58.3 50 41.7
506 clashcityrocker 53.6 49.9 66.5 42.9
507 duncanriley 53.5 58.5 58.9 48.2
508 dickc 53.5 89 45.7 42.5
509 markhillary 53.5 50.4 47.2 43.6
510 dhinchcliffe 53.5 58.8 40.5 51.6
511 tom_warren 53.5 46.5 58.9 43.3
512 inafried 53.5 58.2 35.4 46.2
513 jesusdiaz 53.5 51.4 43.8 48
514 weeklyascii 53.4 58.7 39.5 44.5
515 jbernoff 53.4 59.5 53.7 39
516 deantak 53.4 53.9 39.5 42.4
517 HuffPostTech 53.4 54.6 35.5 56.4
518 EzineArticles 53.4 70.7 38 48.8
519 dweinberger 53.4 59.6 36.4 43.5
520 IndusLogic 53.4 61.9 10 52.9
521 rachelsterne 53.3 56.9 44.5 44.5
522 tariqkrim 53.3 56.8 47.3 44.7
523 brokenbottleboy 53.3 47.2 40.8 46.1
524 andylim 53.3 48.3 66.4 40.5
525 jprpublicity 53.3 57.1 50.3 42.9
526 VentureOutlook 53.3 64.9 10 53.4
527 futurescape 53.3 46.1 68.5 40.4
528 bitterer 53.3 47.6 56.6 43.5
529 zpower 53.2 50.8 48.3 44.2
530 gaberivera 53.2 58.8 49.4 43.3
531 pluggdin 53.2 50.5 36 48.4
532 louisedoherty 53.1 49.1 58 43.4
533 BestBuy 53.1 64.9 43.9 52.3
534 darrenwaters 53 55.2 58.9 39.8
535 teedubya 53 70.7 10 42.7
536 drkiki 53 65.2 53.1 47.9
537 MacworldUK 53 55.2 37.1 51.9
538 shibanijoshi 53 50.8 35.6 46.9
539 maggiefox 52.9 58 43.8 41.3
540 CIOonline 52.9 59 9 54
541 KellyeCrane 52.9 56.8 59.6 46.3
542 DougH 52.9 65.4 54.4 44.5
543 chr1sa 52.9 66.9 37.8 46.9
544 supersonicpr 52.8 57.3 54 47.4
545 LuisGyG 52.8 53.1 55.3 47.6
546 NewTechBooks 52.8 65.6 10 51.8
547 lovellpr 52.8 59.1 43.7 42.7
548 brit 52.7 59.3 45 44.6
549 mikemayhew 52.7 67.2 10 45.4
550 Plastidecor 52.7 54.6 49.4 51.1
551 fugita 52.7 54 49.3 42.3
552 virginmobileus 52.7 56.8 58.4 41.2
553 GizmodoBR 52.7 58.8 4.3 58.5
554 Orli 52.7 58.9 55.3 48.2
555 antonello 52.7 52.4 50.4 43.9
556 rampok 52.7 48.7 49 44.8
557 katiemoffat 52.7 51.6 62.5 39.1
558 johnmerritt 52.6 46.7 47.7 42.9
559 sprint 52.5 64.4 35.9 46.1
560 stuartfoster 52.5 57.7 55.3 47.9
561 rshevlin 52.5 45.1 63 42.9
562 sohear 52.5 48.8 51.7 40.2
563 merv 52.5 49.5 57.8 47.9
564 AdamSinger 52.5 56 50.5 50.6
565 Gartner_inc 52.5 65.6 36.8 52.1
566 mondoville 52.4 47.9 35 45.9
567 susumu_fujita 52.4 70 37.4 46.8
568 mobilezeitgeist 52.3 53.4 37 43.1
569 MacLife 52.3 61.4 34.6 53.6
570 raygun01 52.3 56.8 45.1 39.9
571 sherrilynne 52.3 52.4 45.8 41.7
572 BW 52.2 56.7 3.7 54.5
573 fredericl 52.2 53.4 43.7 41.8
574 OGILVY 52.2 65.8 36.7 50.2
575 ScepticGeek 52.2 48.4 52.8 49.6
576 nlw 52.1 47.6 47.8 44.6
577 dailysocial 52.1 49.3 43.6 44.8
578 jorcervan 52.1 51.9 8.7 42.8
579 Warcraft 52.1 73.3 35.5 51.1
580 nyt_tech 52.1 57.7 0.7 49.9
581 FrankGruber 52 58.8 48.7 46.2
582 Andrew303 52 73.5 67.3 47.7
583 randizuckerberg 52 60 36.8 46.6
584 geetarchurchy 51.9 48.6 55.1 41.5
585 grahamjones 51.9 53.7 45.1 43.4
586 RuthBarnett 51.9 55.6 51.9 50.4
587 JournalDuGeek 51.9 56.3 5.3 53.8
588 howardowens 51.8 49 57.5 41.4
589 nourayehia 51.8 57.8 37.8 45.9
590 stagueve 51.8 50.1 47 43.8
591 DownloadSquad 51.8 57.8 7.3 53.8
592 philippe_lagane 51.8 53.1 41.6 39.9
593 stephtara 51.7 51.7 45.8 41.6
594 iphone_dev 51.7 74.3 40.9 57.2
595 timbury 51.7 57.3 41 40.6
596 GordonMacMillan 51.7 51.1 52 49.4
597 rcadden 51.6 45.6 50.9 40.8
598 ProminencePR 51.6 55.5 48.5 46.9
599 dancosta 51.6 52.4 42.9 43.2
600 ginab 51.6 53.7 49.3 44
601 jtoeman 51.6 49.3 54.7 39.6
602 kuncoro 51.5 44.2 63.1 41.2
603 AppleInvestor 51.5 70.2 10 47.5
604 Bash 51.5 52.1 57.3 45
605 prsa 51.5 60.1 30.6 48.2
606 missusP 51.5 63.7 61.8 40.6
607 hiroyuki_ni 51.5 70.3 33.1 49.5
608 raesmaa 51.5 48 44.9 42.3
609 bmichelson 51.5 48.1 50.3 40.9
610 brettschulte 51.5 50.8 41.7 41.3
611 thetechnewsblog 51.5 63.3 47.3 42.3
612 Avinio 51.4 54.8 49.1 46.7
613 digg_popular 51.4 55.2 37.9 54.1
614 snscrm 51.4 59.9 9.7 41.7
615 richi 51.4 54.5 51.2 40.2
616 PRNewser 51.4 55.9 35.4 51.6
617 lizgannes 51.3 52.4 38 48.3
618 nokiabrasil 51.3 62.7 31.2 49
619 MeredithGould 51.3 50 56.7 44.4
620 Aura_ 51.2 53 51.7 47.1
621 nokia 51.2 64.4 30.7 54.8
622 nicknotned 51.2 56.5 45 43.7
623 Ew4n 51.2 50.2 69.1 41.8
624 MacFormat 51.1 51.3 61.7 48.7
625 danfrakes 51.1 51.5 60 35.4
626 Gist 51.1 51.6 58.1 44.6
627 VanessaAlvarez1 51.1 49.6 54.8 46
628 jyarmis 51.1 51.6 50.1 39.6
629 ArabCrunch 51.1 49.4 7.7 50.2
630 briian 51.1 56.2 39.9 39
631 DellOutlet 51.1 92.5 55 50.9
632 alexandrapullin 51.1 47.6 49.8 39.1
633 dovella 51.1 42.2 50.1 43.6
634 rafaelziggy 51 54.3 44.2 48.2
635 novaspivack 51 58.8 41.6 41
636 rizzn 51 54.1 47.5 38.6
637 IanDouglas 50.9 52.1 55 46.2
638 JoeDuck 50.9 68.3 55.3 44
639 willsturgeon 50.8 47.2 51.9 40.5
640 cssglobe 50.8 60.1 34.7 42.6
641 PCWorldBrasil 50.8 55.8 8.3 52.7
642 AndyBumatai 50.8 62.3 47.2 44.3
643 mike_elgan 50.8 63.5 38.6 28.5
644 rossrubin 50.8 48.1 52.7 39.3
645 mediaczar 50.8 51.2 57.2 36.6
646 jerrymichalski 50.8 55.1 50 40
647 christianward 50.7 46.2 43.3 42.2
648 gyehuda 50.7 51.2 50.3 39.3
649 bobegan 50.6 46.3 53.7 40.5
650 Carnage4Life 50.6 53.6 36 49.8
651 fruchter 50.6 53.1 35 42.4
652 jayadelson 50.6 58.9 39.9 43.2
653 guycocker 50.6 51.2 45.6 39.4
654 robdiana 50.5 48 41.7 42
655 cfarivar 50.5 48.6 43.2 42.1
656 rcrwirelessnews 50.5 48.9 34.1 46.9
657 GrowMap 50.5 60.8 46.7 43.9
658 soulpancake 50.5 66.2 31.6 47.6
659 Astro_Mike 50.5 91.2 2.7 56.5
660 NetworkWorld 50.4 56.8 37.8 45.2
661 Orcon 50.4 48.7 69.4 40.8
662 snoopmikey 50.4 51.9 52.3 40.5
663 Yelp 50.4 68 61.5 50.8
664 bambenek 50.4 69.6 42.6 34
665 tbush 50.4 46.6 57.4 39
666 Stanford 50.3 60.8 39.1 51.1
667 BridgetAyers 50.3 63.6 52 42.8
668 amandachapel 50.3 59.4 46.6 36.7
669 manchate 50.2 46 50.9 38.9
670 identitypr 50.2 54.6 38.8 40.7
671 citizenrobert 50.2 45.9 48.6 41.8
672 Aubs 50.2 62.1 49.6 46.4
673 jameskobielus 50.1 48.7 38 38.7
674 demiwood 50.1 63.2 64 31.9
675 NeilRaden 50.1 47.1 62.4 42.1
676 SpaceFellowship 50.1 65.1 10 45.8
677 m4gic 50.1 48.4 48.4 43.5
678 nagaimichiko 50.1 49.8 8.3 40.2
679 StarCraft 50.1 66.9 37 54
680 Rafe 50 63.9 38 44.6
681 proserv 50 51.7 39.9 37.5
682 omendoza 50 46.9 55.2 34
683 amyronge 49.9 41.1 59.5 38.3
684 monikkinom 49.9 63.5 55.2 32.8
685 efectotequila 49.9 46.4 8.3 44.4
686 PhoneDog_Aaron 49.9 46.8 41.6 48.3
687 blackberrybr 49.9 52 46.2 41.1
688 netmag 49.9 59.3 46.2 40.6
689 anthonyha 49.9 50.8 45.5 38.2
690 tconrad 49.9 53.3 48.6 37.8
691 QuickPWN 49.8 59.6 42.3 52
692 pinaldave 49.8 47.1 42.1 40.6
693 cswolf 49.8 46.1 45.6 41.8
694 TiffanyPR 49.8 56.1 46 44.5
695 AdrienneBailey 49.8 49.9 52.3 43.5
696 sbisson 49.8 48.7 50.5 35.5
697 Enderle 49.7 58 51.6 47.1
698 stephagresta 49.7 62 51.3 30.9
699 DannyWhatmough 49.7 48.8 53.1 43.3
700 matthewgain 49.7 48.4 58 35.8
701 singularityhub 49.6 52.7 4 49.1
702 GregKumparak 49.6 50.5 49 42.9
703 lebrun 49.6 61 63.1 29.3
704 GbrilliantQ 49.6 75 10 44.8
705 android_france 49.6 47 31.4 46.9
706 markpinsent 49.6 42.9 56.5 39.6
707 ryanshrout 49.5 51.5 41.2 41.2
708 1Password 49.5 60.1 73 41
709 cdibona 49.5 59.2 49.1 45.7
710 RachelKrishna 49.5 60.3 58.1 38.9
711 jonloomer 49.5 40.5 58.6 39.8
712 10Yetis 49.5 47.5 54.8 41.5
713 litmanlive 49.5 53 53.6 42.6
714 katiesol 49.4 45.1 52.1 36.9
715 paulboutin 49.4 53.8 48.5 34.2
716 360iDev 49.4 46.8 58 40
717 stephenmann 49.4 44.7 52.8 40
718 chadnorman 49.3 49.1 50.7 38.5
719 laptopmag 49.3 50.6 39.1 46.4
720 Pocketlint 49.3 51.7 5.3 50.1
721 Ross 49.3 60.8 36.9 45.3
722 samirb 49.2 49.4 58.7 34.1
723 spode 49.2 47.4 54 35.1
724 denschaal 49.2 50.7 41.4 38.3
725 MobileCrunch 49.2 54.3 2 54.7
726 LeenaRao 49.2 54.3 47.8 42.2
727 brian_tong 49.2 56.4 52.9 37.7
728 SethGrimes 49.2 48.8 61.9 44
729 PerkettPR 49.2 55.8 45.2 44.2
730 AFasterPC 49.1 70.8 41.4 40.5
731 AndrewBloch 49.1 48.9 46.6 49.8
732 bytebot 49.1 46.3 52.1 36.6
733 DrBobParsons 49.1 65.9 48.7 45
734 Oracle 49.1 62.4 41.6 50.2
735 SilverlightNews 49.1 54.7 6.3 48.8
736 jyri 49 55.2 45.8 39.7
737 MassHighTech 49 55.4 35.1 44
738 edyson 49 57.9 43.9 40.4
739 emoltzen 49 62.1 10 36.8
740 MyAppleStuff 49 69.2 42.4 40.1
741 alextanPR 48.9 47.9 60.5 39.8
742 Claudia_Imhoff 48.9 48.2 51.7 43.1
743 GuyClapperton 48.9 53.4 65.9 36.4
744 GlobeTechnology 48.9 56.3 33.4 45.8
745 MelissaPR 48.9 55.5 56.7 39.5
746 andisboediman 48.8 46.2 52.4 41.9
747 largeburrito 48.8 43.7 37.7 42.5
748 iPhonfr 48.8 56 30.9 49.4
749 360i 48.8 55.8 38.3 41.1
750 QuantumGood 48.8 56.6 9 45.4
751 digg_enviro 48.8 56.8 32.2 45.5
752 uol_tecnologia 48.8 60 4.7 45.2
753 gigastacey 48.7 50.3 53.5 39.4
754 ceciliakang 48.7 46.7 47.9 39.8
755 ahmansoor 48.7 56.2 9 37.3
756 vendorprisey 48.7 48.3 45.1 36.4
757 mnetto 48.6 48 47.3 35.3
758 1000heads 48.6 48.3 48.4 36.7
759 jeffdachis 48.6 54.7 41.4 37.5
760 hmeguid 48.6 40.9 51.8 40.8
761 billschrier 48.5 49.2 31.8 41.6
762 chrisphin 48.5 44.2 64.7 30.9
763 ChrisKnight 48.5 63.8 38.5 41.8
764 robliberal 48.5 62.6 10 36.6
765 EnterpriseCity 48.5 48.7 40.3 45.8
766 davemorin 48.4 78.4 35.3 40.2
767 Knuttz 48.4 51.1 45.6 43.6
768 mobiFlip 48.4 46.4 49.5 42.5
769 KevinCTofel 48.4 52.7 48.1 40.4
770 aLineMedia 48.4 57 37.7 44.1
771 orenjacob 48.4 46.7 51.4 40.1
772 longzheng 48.4 52.1 55.8 40.4
773 jensmccabe 48.3 55.5 51.7 41.1
774 Whitman2010 48.3 80.1 37 45.3
775 paulcarr 48.3 59.1 36.9 34.5
776 igtecnologia 48.3 51.1 8.3 39.3
777 robpegoraro 48.3 52.6 42.1 44.1
778 kevinpurdy 48.3 53.3 47 36.1
779 Mickipedia 48.3 60.9 48.1 38.9
780 johnolilly 48.3 51.3 39.3 37.3
781 AaronMSB 48.2 49.2 51.3 39.6
782 twtfm 48.2 92.7 30.3 36.5
783 bjfogg 48.2 51.5 52.2 41.1
784 stevedupe 48.2 47.3 44.8 38.6
785 jclarey 48.1 53.1 48.2 32.4
786 barijoe 48.1 42.3 7.7 40
787 Office_Live 48.1 61.8 44.5 43.1
788 Hitwise_US 48.1 59.4 40.1 46.2
789 CanadaIT 48.1 55.8 9 44.8
790 nicolamattina 48 47 37.2 37.8
791 MSAdvertising 48 59.5 42.1 44.2
792 TDefren 48 62.4 46.3 53.6
793 lewiswebb 48 50.1 61.7 30.5
794 crossy 48 44.6 58.1 34.7
795 totaltechnews 48 54.1 8.7 38.3
796 sogrady 48 49.9 50.6 41.7
797 Caterina 48 62.9 46.5 47.7
798 JenerationPR 48 57.2 42.8 40.7
799 FierceWireless 47.9 50.3 40.8 44.7
800 dondodge 47.9 56.1 34.4 41.3
801 thevarguy 47.9 56.5 35.9 35.7
802 techpr 47.9 50.8 49.9 31.8
803 jonno 47.9 46.3 55.3 33.1
804 ragtag 47.9 45.5 8 38.7
805 waltmossberg 47.9 70.7 49.9 36.4
806 monsieurlam 47.9 53.3 26.4 38.1
807 ludwikc 47.8 72.4 46.7 28.6
808 jeffmann 47.8 46.9 40.1 37.3
809 wearesocial 47.8 67.7 35 42.8
810 dale_vile 47.8 45.1 57.2 37.7
811 RobmDyson 47.8 48.4 60 37.8
812 linkibol 47.8 62.8 38.6 31.9
813 kabster728 47.7 56.8 30.2 43.4
814 jordanv 47.7 46.9 52.1 36.3
815 bramcohen 47.7 51 43 37.3
816 atmanes 47.7 45.5 42.2 37.5
817 donreisinger 47.6 62 40.9 26.6
818 ATTNews 47.6 65.3 5.3 49.3
819 jamet123 47.6 46 32.4 38.9
820 nselby 47.6 45.9 48.9 32.9
821 jonfildes 47.5 48.4 45.7 37.9
822 markmadsen 47.5 45.9 55.5 34.6
823 JulieG 47.5 47.3 48.5 39
824 gilliatt 47.5 49.8 50.1 31.6
825 digaoi 47.5 54.1 47 39.5
826 johnhcook 47.5 52.5 34.3 36.7
827 ian9outof10 47.5 43.7 44.7 35.8
828 wtcc 47.4 72.3 51.2 32.3
829 TechRepublic 47.4 57.3 29.4 46.3
830 Skripnikov 47.4 41.2 56.1 41.8
831 bloglgt 47.4 49.1 69.4 25.8
832 ComputerworldBR 47.4 53.3 5 51.6
833 DellnoBrasil 47.4 65.4 41.9 37.4
834 TelecomNZ 47.4 56.6 72.3 30.3
835 broy 47.4 53 61.9 34
836 porternovelli 47.4 51.4 8.3 44.4
837 facebookbrandee 47.3 54.5 41.6 40.8
838 JulioAlonso 47.3 55.2 42.6 44
839 iuliusg 47.3 70.1 10 32.2
840 jwarnette 47.3 44.4 58 38.5
841 ActionLamb 47.3 45.8 57 38.8
842 CIOsConnect 47.3 52.9 50 39.7
843 shinykatie 47.3 52.5 53.1 29.1
844 MelKirk 47.3 56.3 43.3 39.7
845 matthickey 47.3 42.5 53.1 34.4
846 rmogull 47.3 49.4 52.8 30.5
847 irinaslutsky 47.3 56.8 8.3 28.9
848 rodriguezhernan 47.3 45.5 50 35.7
849 GregoryCollins 47.2 66 10 41.5
850 edzitron 47.2 39.1 54.9 36.7
851 GordonKelly 47.2 48.5 54.2 39.8
852 corpbusiness 47.2 45 33.6 35.9
853 PhoCusWright 47.2 52 33.3 44.2
854 interop_events 47.1 50.7 35.7 39.4
855 SMacLaughlin 47.1 46.5 45.7 40.2
856 ohnorosco 47.1 47.3 42 34.5
857 uriondo 47.1 45.4 37.7 35.5
858 shintabubu 47.1 48.5 43.6 40
859 jonasl 47.1 61.6 46.7 26.3
860 wonky_donky 47.1 38.7 51.5 36.7
861 prmaria 47 53.1 8.3 36.2
862 stevenjayl 47 60.3 31.2 38.2
863 andismit 47 47.6 7.7 36.8
864 NateLanxon 47 48.2 5 42.4
865 jlouderb 47 59.5 35.2 36.3
866 MarkSilver 47 51.5 35.3 42.1
867 JonoH 47 51.2 56.7 35
868 holdenpage 47 43.6 54.6 32.6
869 LarsHinrichs 47 56 35.4 45.2
870 carterlusher 47 51.4 38.7 34.4
871 shelly_palmer 46.9 55 32.8 31.5
872 artezonline 46.9 44.2 42.2 40.5
873 TheMan 46.9 51 58 38.9
874 tomcummings 46.9 49.4 49.4 31.2
875 JoannaStern 46.8 49.6 53.6 37.9
876 dburka 46.8 59.2 59.5 33.9
877 neofreko 46.8 44 49.4 30.9
878 Hitwise_UK 46.8 58.7 8 45.2
879 stevegillmor 46.8 58.7 29.7 31.1
880 avneron 46.8 49.7 35.8 36.8
881 VPEPR 46.7 49.5 39 41.9
882 wildfireapp 46.7 52.5 51.5 36.6
883 nathanmcdonald 46.7 49.7 42.9 34.3
884 gill_edwards 46.7 47.7 64.3 25.1
885 bhammerling 46.6 54.6 35.9 30.5
886 megheuer 46.6 49.6 8.7 36.9
887 JazCummins 46.6 50.6 38.4 39
888 peterdrew 46.6 72.3 45.9 29.7
889 laurenmcgregor 46.6 40.3 51.5 35.1
890 zem 46.5 49.5 6.7 39.3
891 CrisisCamp 46.5 49.6 37.7 43.9
892 CandaceKuss 46.5 49.4 44.9 38.8
893 CarbonHeart 46.5 70.4 38.3 42.6
894 LJRICH 46.5 52.7 43 36.6
895 al3xandru 46.5 52.6 45.2 31.5
896 JordanBrown 46.4 62.9 59.8 37.3
897 OneRiot 46.4 58.3 9.3 37
898 waxpancake 46.4 56.8 40.8 32.8
899 SteveLevine1 46.4 66.9 38.7 35.4
900 ThePeterHa 46.4 49.6 49.1 39.5
901 dmac1 46.3 53 38.4 34.4
902 tebbo 46.2 44.2 54.4 34.2
903 ahoppin 46.2 50.5 40.5 34.3
904 Square 46.2 62.6 58 44.5
905 abnerg 46.2 47.3 8 36.5
906 lehawes 46.2 45.9 51.1 27.5
907 cmooreforrester 46.2 45.6 43.3 38.8
908 bensoebiakto 46.2 36.8 38.1 42.6
909 AmandaSena 46.2 54.3 49.5 36.4
910 marissamayer 46.1 65.6 52 43.4
911 eldon 46.1 52.2 42.6 31.3
912 ghostexecutive 46.1 50.7 39.7 29.4
913 abbielundberg 46.1 52.2 8.3 30.4
914 msullivan 46.1 48.9 8.3 37
915 wroush 46.1 48.3 36 37.1
916 ceoworld 46.1 60.5 10 33.6
917 davelee 46.1 52.4 49.9 28.4
918 EdelmanHR 46.1 50.3 49.6 38.8
919 jaykeith 46 43.3 54.6 28.9
920 googlebrasil 46 61.8 27 48.9
921 techno_news 46 55 9 33.6
922 drimington 46 62.4 40 27.9
923 shaneschick 46 44.5 36 36.8
924 WhenGrowthStall 46 57.9 53.7 37
925 Frauenfelder 46 59.3 41.5 44.6
926 AndiMann 46 48 51.7 37.3
927 stephenodonnell 45.9 59.9 48.9 29.2
928 BobMetcalfe 45.9 52.6 32 43.2
929 aspirationtech 45.9 81.6 44.1 41.6
930 Toronto_PR_Guy 45.9 48.8 50.1 38.4
931 kmburnham 45.8 46.5 43.3 33.6
932 johnlovett 45.8 47.3 51.9 36.3
933 fiberguy 45.8 42.2 45.8 35
934 nahars 45.8 67.2 10 38.4
935 jordanstone 45.8 45.9 40.7 33.4
936 rickwray 45.8 50 50.2 31
937 waworld 45.7 51.6 35.2 35.9
938 BravoPR 45.7 54.7 45.4 40.5
939 MSSurface 45.7 53.8 59.6 40.3
940 cnet_j 45.7 51.9 0 38.3
941 timberners_lee 45.7 64.6 43.2 46.2
942 vealmince 45.6 42.6 57.1 30.2
943 gilbane 45.6 49.9 40.2 38.8
944 StoryAssistant 45.6 53 58.5 34.4
945 Mark_Mulligan 45.6 51.4 45.9 38.9
946 joshu 45.6 56 47.7 27.2
947 iglazer 45.6 43.2 48.8 33.3
948 CBSitweets 45.6 48.3 40 37.9
949 aramadge 45.6 45.9 49.5 33.5
950 montemagno 45.6 59.4 5.7 32.7
951 Vonage_Voice 45.6 52.8 67.8 33.2
952 FCC 45.5 82.6 3.3 49.5
953 pdouglas 45.5 42.9 55.4 33.3
954 parnellk63 45.5 57.6 42 25.5
955 pingfm 45.5 60.8 50.3 26.5
956 jeffsommer 45.5 43.8 31.1 37.7
957 venturaE 45.5 38.9 48.3 38.8
958 RetiredTeacherD 45.5 66 47.2 28.8
959 sonny_h 45.4 39.2 46.8 35.9
960 dangrabham 45.4 45 55 28.9
961 tseelig 45.4 50.9 36.7 37.9
962 FierceBiotech 45.4 49.8 33.9 41.8
963 elatable 45.3 57 39.6 34.9
964 rgruia 45.3 48 39.4 37.8
965 andreascon 45.3 45.4 37.9 40.5
966 susanmernit 45.3 53.6 8.7 28.7
967 dankeldsen 45.3 55 9 29.4
968 paultoo 45.3 56.3 5 33.1
969 chrisgreen 45.2 48.4 50.9 27.4
970 arnoldkim 45.2 56.3 43.2 27.5
971 JohnBorthwick 45.2 54.7 40.1 39.7
972 johnrrymer 45.2 47.3 39.3 40
973 cmendler 45.2 45.2 51.7 33.6
974 RickMacMerc 45.2 52.2 52.9 33
975 schillmoeller 45.2 43.2 54.9 33.1
976 ryanlawler 45.1 47.3 37.5 29.9
977 Xtel 45.1 49.8 47.8 33.5
978 CakeGroup 45.1 51.6 38.3 43.6
979 storageio 45.1 46.8 54.7 27.2
980 billtrippe 45.1 47.5 38.7 34.8
981 JEBworks 45 49.1 48 34.9
982 veriserpa 45 44.3 6.3 35.9
983 raxlakhani 45 58.8 56.5 25.6
984 LGdobrasil 45 55.4 63 30.6
985 plancast 45 45.9 69.7 26.3
986 Jamesholland 45 43.3 54.7 35.7
987 CERN 45 76.5 3.3 53.2
988 TelecomTalk 45 45.3 39.3 41.7
989 sarahcuda 45 64.1 38.5 26.7
990 maslett 44.9 41.7 43.1 38
991 rubbishcorp 44.9 43.4 5.7 37.5
992 MiaD 44.9 50.2 57.4 33.7
993 fedcomputerweek 44.9 47.3 7.7 35.9
994 IdentityWoman 44.9 52.1 41.5 35.3
995 programmableweb 44.8 51.3 2 38.9
996 lostremote 44.8 48.6 32.4 40.2
997 john_chilmark 44.8 49.2 43.4 27.1
998 dottavi 44.7 59 40.6 36.6
999 JoeHobot 44.7 69.3 50.3 29.1
1000 sonymusic 44.7 63 36.1 27.3

Algorithm and Methodology

tmp9CD 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.

Twitter Lists – TweetLevel calculates the number of times someone is included in a twitter list and the corresponding value of that list (as determined by the number of people following it. 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.

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.

Capturing the data.

The Twitter profiles for this survey were obtained by extensive internet-based research and submissions following a call to action in January to submit technology Tweeters to the list. Obviously, a list like this is dynamic and there are bound to be omissions or the odd errant addition. We encourage you to send any anomalies our way so we can continue to improve the quality of our data in this. Likewise, please send suggestions for inclusion.

Common complaints

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

1. This twitter account is someone who I would regard as being in technology.
The argument as to who is a technologist or not is largely moot. In our opinion if someone is actively talking about technology on Twitter – regardless of whether it’s their day job or not, then that qualifies them for inclusion. We know this will cause a huge amount of disagreement but as an outsider looking in this is the way we see it. This will doubtless lead to issues around non-technology Tweeters (Stephen Fry for example) ranking above more well known technology journalists or brands. But this surely serves to raise the issue of who is considered influential and if it sparks debate and discussion then it’s definitely a good thing.

2. The twitter handle is a brand or written by multiple authors.
We took the decision from the start that technology brands should be included along with individuals. Indeed, it’s the core tenet of our article around brands getting more savvy using the platform. The merits of a single twitter account author allows understanding of the tone of author without having to understand the many personalities that are associated with it but as the results of the survey have shown, brands are moving up the rankings so it would be nonsensical to exclude them and by planning them into context alongside individual Tweeters again debate and discussion is encouraged.

3. Hey – you have forgotten to include this list.
Please let me know the name and we will include it as an edit.

Cowabunga dudes

What makes someone – or something – influential?

For example, a PR with a client in telecoms is bound to say that the editor of a telecoms trade journal  is influential. They’d probably say the same about Stephen Fry. But ask them to prove that influence and I guarantee you’ll get either a blank stare or a conversation about circulation figures, web traffic and – if you’re lucky – followers on Twitter.

If we accept a definition of ‘having influence’ as being: “someone or something whose opinions and actions lead to a change of opinion and/or behaviour in the audiences they engage with or who engage with them,” then surely both the editor and Stephen pass the test?

Not quite. The problem I have is that most of the time we’re not getting an objective measure of influence but a subjective measure of significance, and that’s not good enough.

While the counsel given by PRs on the merit of interacting with different audiences is undoubtedly valued by clients, the massive fragmentation of the communications’ landscape coupled with the after-effects of a global recession have given them a reason to question the relevance of every penny of PR spend.

Clients are forcing the issue of proving influence because they are no longer certain that accepted campaign strategies and tactics will deliver to overall business goals. Telling them a person, or group of people, is influential and that they should be reached by a certain method is no longer enough, it has to be proven.

I’m already having daily conversations with clients who are keen to rip up the rule book of what constitutes a PR programme because they don’t accept that the old ways are working. The sacred cows such as press releases and press tours are being met with the question: “What value is this activity to me?” and I’m finding it massively liberating.

Of course, it’s an evolution rather than a revolution and there are still plenty of companies who require the activities that go to make up a typical PR programme. But the pace of evolution will only quicken as the global trend of influencer engagement starts to replace the one-to-many communication models prevalent in marketing today.

With that in mind, Edelman is on a mission to find and rank the 2,010 (see what we did there?)  most influential people in technology using our very own TweetLevel – a sophisticated influencer measurement tool that gives an accurate assessment of a person’s level of influence in the Twittersphere.

True, it’s currently limited to measuring influence within Twitter, but the fact that Twitter is being used by a large percentage of people involved in the technology sector makes it a perfect place to start. Look out for a call to action on how you can put yourself or someone else forward.

In the meantime I’d love to hear your views on influence and its importance in PR. Have I got this right? Is it the single most important factor for us PROs to understand and measure? Will it lead to a rethink around how we do PR or will the old ways always remain?

@PaulWooding

It’s had the gestation period of an elephant, but at last Jonny Bentwood – proud father that he is – has unveiled his latest baby, and it’s called TweetLevel. All babies are beautiful to their parents, of course, but not all babies are beautiful. In fact in my experience most are ugly little sods (my own being the exception, naturally).

TweetLevel has been so long in arriving – and Jonny has been banging on about it so much during the pregnancy – that a decent number of his Edelman colleagues were hoping that the geeks might have invented the next big thing by now, causing Jonny to bang his fists on his desk, hot tears of frustration spilling down his cheeks. But no.

Having said that, if you read the small print of Jonny’s post about how TweetLevel works – and the comically complicated sum that powers it – you’ll find this bit:

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.

What this basically means is: “Oh, bollocks. Twitter lists screw up the maths, but there’s no way I’m doing it again now. We’ll sodding do without.”

Hey, but look, whatever. Jonny’s done a lot of work so that TweetLevel can tell you how influential you are. And popular. And trusted.

You’re not as influential, popular and trusted as Ashton Kutcher. Frankly, you’re probably not as influential, popular and trusted as Jonny Bentwood. But don’t let it get you down. Get angry. And take TweetLevel’s advice, pull up your socks and get yourself up the rankings. It’s the path to fulfillment (possibly also unemployment).

The TweetLevel press release has done it’s best to entirely undermine the worth of the tool by claiming that based on its findings, Labour would win a landslide at the next election. It also invents the word ‘Tweetatorship’, which undermines the value of the press release.

But that really makes the point, isn’t it? You might be influential on Twitter (at least according to TweetLevel), but the Twittersphere doesn’t represent the broader population. Not anywhere near. We’ve got to keep it all in perspective, and place Twitter within the context of all the other channels of influence. Like talking to each other. And television. And…ummm…well, talking and telly at least.

The truth is, of course, that the really influential people have got more important things to do than tweet.

Oh, and this isn’t Jonny, but it is how he lives…