Image via WikipediaPointless babble? I continue to have issues with the Pearanalytics study of 2000 tweets, over 2 weeks, sampled during the day from 11:00 a.m. to 5:00 p.m. I have issues with their categories and how things are placed within the categories.
(Model, Iman's, photo fits with the title because as host of Project Runway, she uses those words to say good-bye to ousted contestants.)
I decided to do a totally unscientific analysis of the most recent tweets I have received from the 52 people I am following on Twitter. I'm finding that who I listen to on Twitter translates into what seems important in social media right now. I realize that many of you have way more than 52 people you are following. I suppose you could look at the most recent 50 tweets that have entered your Twitter stream. First of all you would need to look at the Pearanalytics study to understand their system of categories. (http://www.pearanalytics.com/wp-content/uploads/2009/08/Twitter-Study-August-2009.pdf)
I have created a Twitter survey using Google Docs. The survey can be found on this website. (http://docs.google.com/View?id=dfzz97d8_28gmfkm45f) (I'm sure there would have been a better way to create this so I welcome innovation or spreadsheets or some other means of data collection. Tell me what you have created.)
When I did my totally unscientific (no machines involved) study of the most recent tweets from the people I follow (including one dead president--John Quincy Adams @JQAdams_MHS), here are my category results (since I am following 52 people, I pretended it was 50 and multiplied by 2 to arrive at the percentages):
Self-promotion: 2% (I follow one local Saskatoon store.)
(Question: Should people saying, "Read my blog post at ___________", fall into self-promotion?)
Image via Wikipedia
Pointless babble: 38% (using Pearanalytics definition. This includes people sharing URLs and promoting own blog posts. If no @ or RT, does not count as conversation or pass-along. I question this.)
Pass-Along Value: 16%
(Since this was unscientific, I've missed 2% somewhere. My percentages add up to 98%.)
When I analyzed the most recent 10 of my own tweets, I discovered 5 pass-along tweets (50%), 3 conversational tweets (30%), and 2 pointless babble tweets (20%). I am not exactly conforming to Angela Maier's suggested 70% pass-along tweets, 20% conversational tweets, and 10% of pointless babble tweets (although she calls it chit-chat about your life). (See http://www.angelamaiers.com/2008/09/my-twitter-enga.html) I believe that Angela would disagree with Pearanalytics categories as well. Under Pearanalytics system, the tweets during which I am sharing website addresses or other people's blogs (but not as a retweet)
Image by ian g via Flickrwould fall into the "pointless babble" category. However with Angela's system, those tweets would be pass-along tweets since you are sharing resources.
Here's what I wonder: If you analyzed your own tweets, what would you find? If you analyzed a sample of the tweets you follow, what would you find?
Could it be, that in order to alter the "noise to signal ratio" you might have to say (in the words of Iman on Project Runway Canada), "You just don't measure up" and cut some people loose? If you and I are reflective about our own "noise to signal ratio", are there other people who will be cutting you or me loose?