What I Learned from Your Twitter Discoveries

Last Friday @VenessaMiemis and I had the following exchange in Twitter:

vm-ek-1

We exchanged a few DMs offline to discuss a potential way to do it, and then she twitted out to the world.

VM-2

I then created the #MonTwit hashtag, advertised it a few times, “counseled” (coerced would probably be a better term) a few people to write about it — and the result was, well almost overwhelming for what I was expecting and for only a day or two advertise the experiment and get the word out.

First, some stats — as the writing of this blog there were 86 contributors (people using the hashtag) 164 times.  Twenty blog posts, and 14 opinions expressed via Twitter.  Check out the rest of the stats at WhatTheHashtag, or get a transcript if you prefer from there.

Some people (14 – list below) just tweeted their discoveries (yes, Twitter is a microblog – so perfectly acceptable).  Some others (10 – list also below) wrote posts or posterous or similar entries on their lessons learned and discoveries.

I read them all, as long as they were properly hashed and I could find them, commented on a few of them, and learned a lot of very interesting things in the process.  Here is my summary of lessons learned on the fist iteration of the MonTwit (Monday Twitter).

Will there be more?  Conversations are underway to try to produce it better, spread the word farther, and looking for better focused and more concrete topics.  Short answer? more than likely.  Stay tuned.

Lesson #1 – Tribal Knowledge Rocks — On Demand.  Asking people to talk about something they know, at a certain time and with proper structure brings you a lot of different views.  This is good.  One of the largest problems with crowdsourcing or wisdom of the crowds is that the largest voices influence the smaller voices (or more powerful or more influential – pick your word to use).  Setting a specific timeframe for the answers takes away the “bully” effect inherent to wisdom of the crowds.  You will notice if you read through the entries the influence that early ones begin to have on latter ones.  Setting a specific time takes away a lot of this and provides very interesting, different perspectives.

Lesson #2 – Twitter is About People, not Technology or Content.  Yep, virtually everyone wrote about the contact with people they did not know before, or met via Twitter, as the most critical part of what they discovered about it.  Twitter is a community, as I always said, and the knowledge sharing is inherent to the model of community. People want to connect to people, and what is what Twitter offers — the largest “brain phone book” in the world to find the people you want, to tap into brains and knowledge that you think must exist but are not sure how or where to find.  See @WimRampen’s entry for more on this, as his was the most RT one during this experiment (barely edging Venessa in reach and reads).

Lesson #3 – Know Your Purpose.  Twitter can suck the life out of you… yes, it is that addictive.  Close to 100 million people talking about — well, just about anything can really cause you to lose track of time even worse that spending time on YouTube.  Why are you on Twitter is the first and last question you should always ask yourself.  Sure, it works great as a time-killer, but even better as a community – and communities are about sharing knowledge.  What are you trying to learn today?

Lesson #4 – I Still Know Little.  I realized what I know and what I am still to learn.  I like to say that I am constantly evolving and learning and did confirm some of my suspicions and best practices by reading the blogs today, but I also realized that there are so many aspects of any issue I am not considering, or discarding too quickly.  Twitter is a great mind-expansion tool and you should always, always look at if for that: an unfiltered window into the tribal knowledge of the world.

Tweeted Entries (chronological order)
@RobbertBouman
@JFenderBoa
@doris_rj
@mexxMarketing
@TProctor83
@mexxMarketing(2)
@openworld
@WildCat2030
@csd70
@deanpomerleau
@soulwhispers
@soulwhispers(2)
@ToughLoveforX
@ToughLoveforX(2)
Blogged Entries
@WimRampen
@ekolsky (me)
@timkastelle
@mjayliebs
@MarkTamis
@prem_k
@mauricioswg
@seamuswalsh
@VenessaMiemis
@twitrvenky
@renatalemos
@mgua
@Metalifestream
@nigelwalsh
@pragerd
@mfauscette
@kengillgren
@CRMStrategies
@GoodCRM
@ideahive

Now, it is your turn.  Did you read them all? some? most? What did you learn? What is new or different that you picked up from today’s experiment? Do you have any ideas on how to do it better?  woudl love to hear your thoughts…

Update (12/23/2009): The #MonTwit hashtag will be revived in 2010 for more like experiments.  If you are interested, keep a search column in your favorite client to stay updated.  Thanks for the persistent asking everyone.

Late Update (01/02/2010): David Carr (@carr2n) wrote a compelling #MonTwit entry — without hashtag.

9 thoughts on “What I Learned from Your Twitter Discoveries”

  1. Great article! Sorry I missed the day! I thought the summary was dead on – except for the very last line. For me Twitter isn’t an ‘unfiltered’ look into tribal knowledge, rather it’s filtered by the best type of filter possible. In other words it’s not filtered by key word or search term or bizarre formula or any other machine construct, rather it is filtered by smart people who I trust!

    I’ve given up bookmarking, and won’t pick up social bookmarking, because of this for all the reasons you cite in this great piece!

    1. Estaban – thank you for doing this, Venessa also. Is there something we can do with a wave sometime? A larger format group something? What is it about the one, and not the other, combination microblog to separate blogs to summary with comments. What comes next? How do we feed this knowledge gained back into the system to magnify value? What is the meaning to be found? This has been a valuable exercise. What has been the community generated?

      Hmmundahl … yes absolutely … i think your point is spot on. That filtration by trust is amazing. Information filtration has been historically by the controlling voice (media ownership) now information filtration is through your network.

      Twitter is a meta neural network of trusted information filters. An extra-nervous system. It shifts my relationship to the world _because_ those relationships are trusted. Their filtration of the world is sufficiently similar to mine, that I trust it. But with every tweet one has to say ‘does this have meaning to me?’ The process is just accelerated.
      .-= David Hodgson´s last blog ..Tackling (Holiday) Complexity with Pens and Paper…and Good Cheer =-.

  2. Esteban (and Venessa),

    Great experiment and yet another lesson on how Twitter can be used:

    A couple of ideas, a quick electronic message exchange between new Twitter friends, and boom – crowdsourced feedback and cumulative readership likely approaching the thousands by now.

    Twitter is a “people sampler”, and a “people mover”. 🙂

    Thanks again,
    Brian
    .-= Brian Vellmure´s last blog ..My contribution to the #MonTwit experiment: What I’ve discovered about Twitter =-.

  3. Yes, this was a terrific experiment. I’m still catching up with all the learnings and insights folks had to share. I woke up this morning with another post in mind on the Zen of Twitter–which, pending your decisions on the future–I’ll release next Monday!
    My own immediate experience was the use of Twitter to scale information–so you can dig deeper into intriguing titles and “suspend” others–and the sense of gaining higher resolution in surfacing key learnings through broader participation.
    And that while filtering is critical, it’s more important to become conscious of what filters are in play, and then take responsibility for what we intentionally–or unintentionally–filter in or out. [Oops, sorry, I flipped into a different level of reflection–but that’s what Twitter does for me–it makes available multiple levels of insight.]
    .-= Ken Gillgren´s last blog ..Twitter as collective stream of wisdom, tipping point, and network activator =-.

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