Writing for Click Z, The #mgartr13 Influence Project & Web Journal June 25th – July 12th, 2013

I will start this post of with a few announcements:

1. I am starting to contribute to  a new column for Click Z about Convergence Analytics and my first post is scheduled to be published on July 29th, 2013 and I am working on it now.  I already have some ideas that haven’t been expressed anywhere else, so stay tuned.



2. My latest thoughts about the subject of Influence and Influencers is driven by research connected with the course I teach at Rutgers University for the Mason Gross School of the Arts online (MGO).

Last semester in the section focusing on influence I gave students an assignment to find influentials aligned to their major  areas; I spent over a 100 hours trying to work with the data, merge and clean it (to the extent I could clean it) and figure out how to make those choices useful and actionable.

My goal in this project was take the data I got – add additional metadata and insights to it, then reflect it back to the students.  All students who took the course in this or previous semesters (or in the future) will get the research, and I’ll also release a condensed version of it here.

I urged students to use Followerwonk to set up a few queries to bring of influencer twitter accounts – including Geo-located influencers for the tri-state area.

With a total of 52 student submissions (51 from Spring 2013 and 1 from this Summer semester class I’m teaching) I complied a list of each major area based on the composition of my class (below), putting an online version of the list into Klout (below).    I believe you will be able to see my lists if you are logged into Klout, otherwise, they will just bring up the log in screen of Klout.

I also considered Facebook in this set of lists, but incorporated that information separately.

I came up with this approach to crowd-source the selection of influence because I wanted to stack the odds in favor of my students being able to find the best influencers and approach them (esp those graduating) – I hoped to give them a “book of Influence” and someday, I just might.

But I found the work just too much.  I needed more time, time I just didn’t have … but I wanted to keep my promise and release something – so I put out the first cut of the information with an emerging approach that I briefly go over below.


Major Pages Liked using Facebook Graph Search (source: WebMetricsGuru INC)


Computer Science http://klout.com/#/webmetricsguru/list/332295
Dance http://klout.com/#/webmetricsguru/list/332296
Sports/Wellness http://klout.com/#/webmetricsguru/list/332297
Food http://klout.com/#/webmetricsguru/list/332312
Fashion/Beauty http://klout.com/#/webmetricsguru/list/332311
MARCOM http://klout.com/#/webmetricsguru/list/332313
Music http://klout.com/#/webmetricsguru/list/332314
Theater http://klout.com/#/webmetricsguru/list/332317
TRAVEL http://klout.com/#/webmetricsguru/list/332318
Visual Arts http://klout.com/#/webmetricsguru/list/332319
Web/Graphic Design http://klout.com/#/webmetricsguru/list/332321
Writing http://klout.com/#/webmetricsguru/list/332322

Students also picked a specific influencer they would pitch personally – here’s that list http://klout.com/#/webmetricsguru/list/332323.

After spending a bit of time working on the #mgartr13 project(often fruitlessly, this Spring and early Summer) I finally got my hands around the data enough that I developed a point of view (POV) about this subject that was different from what I expected, and different somewhat from what I started with.

  • I decided that the social media influence technologies are still too immature to be as useful as they need to be, but that many free tools, triangulated together could provide better information than most paid tools, regardless of price.
  • I believe the right methodology to approach influence and influentials are much more important than any of the influence tools that currently exist today.   For most people, including agencies, the free tools I choose are more than enough, esp with the type of approach I’m evolving.  The biggest investment you have to make is to devote enough time and consistency to the influence search.
  • The tools I chose (below) can be substituted or extended as we become aware of new and better ones, or should any of these  cease to work.


Klout-  Easy, very familiar to most people, has gotten better and has a “topic analysis”, includes data from many social properties.
PeerReach.com – Similar to Klout, has interesting visualizations and its own list, breaks down influence by geography.
NeoFormix.com – a Data Analysis / Text Analysis blog that has some nifty tools such as Spot and Tweet Topic Explorer.
Bluenod.com – Community visualization of influencers  of any twitter account.

Advanced Twitter Search – use Twitter API to geo-locate relevant conversations nearby your location and find Twitter accounts you can friend.


Here’s an example based on the Visual Arts list:


Source: http://peerreach.com/MuseumModernArt

(with the top 4 influencers audiences compared to each other)

Focusing on the top influencer in the list I used the Tweet Topic Explorer to see the entirety of the latest set of tweets from a text analytics perspective that was actually pretty darn good for free tool (and better than most paid tools I’ve seen in this space).   The point would be to “see everything” as much as you could – taking a bird eyes view of the people or businesses you want to approach.

Source: http://tweettopicexplorer.neoformix.com/#n=MuseumModernArt

Tweet Topic Explorer was used with MuseumModernArt (MOMA) and I saw the “rainroom” and it looked interesting so I used the text analytics to dig in deeper.  I was totally unaware that the MOMA rainroom was going on, and in fact, will be closing in a few weeks.

Source: http://www.youtube.com/watch?feature=player_embedded&v=7cem71cR0S0

I wanted to know more about the Rainroom, so I used another tool called “Spot”.

Source: Spot Visualization of the last 100 tweets about @MuseumofModernArt shows those who are talking about the rain room right now are a small group. http://neoformix.com/spot/#/MuseumModernArt

While I would  like to make it over to MOMA’s rainroom before it closes – with an average 8 hour wait – I think I’ll passwilling to get “all wet” outside the museum ha!!

I also wanted to encourage my students to  explore the “community” around any influencer they wanted to connect to – and for that I found Bluenod.com has a pretty neat tool.

Source: http://bluenod.com/user/museummodernart

While Followerwonk is a great tool (now part of MOZ Analytics) the part of the tool I wanted to hone in on wasn’t really free, and rather than stay with this tool, I went back to the source itself, Twitter, for what I consider to be a better approach if your willing to put the time into it.


Twitter Advanced Search with Geo-Location can be used to cull a list of topical localized influentials – you just have to spend the time to regularly collect the data, qualify it for yourself, either use keywords or just look at the interactions of people with the influentials – there in turn will be the people who can, hopefully be approached to pitch an idea (but you have to be careful about that).

Here’s part of the Visual Arts Influence List:

Klout List – http://klout.com/#/webmetricsguru/list/332319

Visual Arts name Klout URL  Market Research Account Topic Word Cloud (last 1500 Tweets) Spot Intelligence – Neformix Community of this Influencer
91 MoMA The Museum Of Modern Art http://klout.com/user/MuseumModernArt http://peerreach.com/MuseumModernArt MuseumModernArt http://tweettopicexplorer.neoformix.com/#n=MuseumModernArt http://neoformix.com/spot/#/MuseumModernArt http://bluenod.com/user/MuseumModernArt
86 Natalie Hall http://klout.com/user/NatalieCoughlin http://peerreach.com/NatalieCoughlin NatalieCoughlin http://tweettopicexplorer.neoformix.com/#n=NatalieCoughlin http://neoformix.com/spot/#/NatalieCoughlin http://bluenod.com/user/NatalieCoughlin
85 Frieze http://klout.com/user/frieze_magazine http://peerreach.com/frieze_magazine frieze_magazine http://tweettopicexplorer.neoformix.com/#n=frieze_magazine http://neoformix.com/spot/#/frieze_magazine http://bluenod.com/user/frieze_magazine
82 Vitaly Friedman http://klout.com/user/smashingmag http://peerreach.com/smashingmag smashingmag http://tweettopicexplorer.neoformix.com/#n=smashingmag http://neoformix.com/spot/#/smashingmag http://bluenod.com/user/smashingmag
82 Christopher Jobson http://klout.com/user/Colossal http://peerreach.com/Colossal Colossal http://tweettopicexplorer.neoformix.com/#n=Colossal http://neoformix.com/spot/#/Colossal http://bluenod.com/user/Colossal
81 Creative Time http://klout.com/user/creativetimeNYC http://peerreach.com/creativetimeNYC creativetimeNYC http://tweettopicexplorer.neoformix.com/#n=creativetimeNYC http://neoformix.com/spot/#/creativetimeNYC http://bluenod.com/user/creativetimeNYC
81 Rebecca Minkoff http://klout.com/user/RebeccaMinkoff http://peerreach.com/RebeccaMinkoff RebeccaMinkoff http://tweettopicexplorer.neoformix.com/#n=RebeccaMinkoff http://neoformix.com/spot/#/RebeccaMinkoff http://bluenod.com/user/RebeccaMinkoff
80 Zack Arias http://klout.com/user/zarias http://peerreach.com/zarias zarias http://tweettopicexplorer.neoformix.com/#n=zarias http://neoformix.com/spot/#/zarias http://bluenod.com/user/zarias
80 David Hobby http://klout.com/user/strobist http://peerreach.com/strobist strobist http://tweettopicexplorer.neoformix.com/#n=strobist http://neoformix.com/spot/#/strobist http://bluenod.com/user/strobist
79 Fashionista.Com http://klout.com/user/Fashionista_com http://peerreach.com/Fashionista_com Fashionista_com http://tweettopicexplorer.neoformix.com/#n=Fashionista_com http://neoformix.com/spot/#/Fashionista_com http://bluenod.com/user/Fashionista_com

This approach is still evolving, and I also want to get some feedback to see how  this “visual” approach will work with those who are primarily artists –  though students can come from all areas at Rutgers, including programmers and computer science.

Using Facebook Graph Search

I used the magazines that students read and found from the Influence assignment to develop an approach that could help them find jobs by exploiting the possibilities that Graph Search opened up this year.  Here’s an short excerpt of the list:

Among the ways I used Graph Search are with queries like this:

Fashion Designers working in NYC who like Glamour Magazine https://www.facebook.com/search/26815555478/likers/110684922292220/job/108424279189115/employer-location/employees-2/present/intersect

In some cases, where it made sense and there was enough data, I used an influencer on the list and combined it with a topical magazine and a location.

Targeted to Pitch Influencer  Cathy Horynand live in New York, NY https://www.facebook.com/search/108424279189115/residents/present/108254102529145/likers/intersect

Here’s a few people on that Graph Search result

This is a long post and rather than get into the Web Journal part – I’ll end it here by saying that the “triangulation”with when it is combined and used with someone who is motivated, who is willing to put focus and effort into data collection will result in superior results .  This is my belief.

WebMetricsGuru Social Intelligence

It’s this “Lens” approach that uses tools that don’t necessarily all line up in their outputs (given similar or the same input) that none the less, with the right practice, provide superior results – that is an aspect of the WMG SI (WebMetricsGuru Social Intelligence) that I have been evolving, together with a few strategic partners – though the selection of this list and the particular triangulation I have done comes entirely from me.

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