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Cloud of Knowing set up
Analysis and Interpretation: The cloud of knowing

how it started
Can they be brought together?

Text
Analytics

Research
E-Anthropology E-ethnography
Is it bricolage? Ie an acceptable
Methodology?
Or is it picking dead stuff
‘because it looks interesting’?
Textual analysis on the web – how good is it?






RSS feeds
Tagging
Key words searches
Dodgy social media measurement techniques


Eg deduct neutral tweets and subtract negative
tweets from positive ones. That’s the score!!
Some questions about the originators










Who are they, where are they from and how did they
come to post this content?
What did they mean?
Which audience were they writing for?
What is the context?
Are they being paid?
Is this their genuine opinion or are they stooges –
marketing constructs?
Who do they represent?
Themes to resolve








How to source and structure textual web
content (and other media forms as well)
How to validate it
What if we worked with other web users what
would we get them to do?
How do we sort and grade content?
How do we sort and grade web users?
Grab bag of issues






Sourcing – RSS feeds
Hunting – recruiting webusers to go taggin
Grading – recruiting webusers to grade what
comes in
Profiling – using scoring models as in direct
marketing profiles
Open source approach


Work on it together because the issues are
too big for any one research agency
Open source approach


Work on it together because the issues are
too big for any one research agency

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Cloud1: the set up for the Cloud of Knowing project in 2009

  • 2. Analysis and Interpretation: The cloud of knowing how it started
  • 3. Can they be brought together? Text Analytics Research
  • 4. E-Anthropology E-ethnography Is it bricolage? Ie an acceptable Methodology? Or is it picking dead stuff ‘because it looks interesting’?
  • 5. Textual analysis on the web – how good is it?     RSS feeds Tagging Key words searches Dodgy social media measurement techniques  Eg deduct neutral tweets and subtract negative tweets from positive ones. That’s the score!!
  • 6. Some questions about the originators        Who are they, where are they from and how did they come to post this content? What did they mean? Which audience were they writing for? What is the context? Are they being paid? Is this their genuine opinion or are they stooges – marketing constructs? Who do they represent?
  • 7. Themes to resolve      How to source and structure textual web content (and other media forms as well) How to validate it What if we worked with other web users what would we get them to do? How do we sort and grade content? How do we sort and grade web users?
  • 8. Grab bag of issues     Sourcing – RSS feeds Hunting – recruiting webusers to go taggin Grading – recruiting webusers to grade what comes in Profiling – using scoring models as in direct marketing profiles
  • 9. Open source approach  Work on it together because the issues are too big for any one research agency
  • 10. Open source approach  Work on it together because the issues are too big for any one research agency