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   Author  Topic: Predicting Virulence of posted memes  (Read 3002 times)

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Predicting Virulence of posted memes
« on: 2010-04-12 08:52:13 »
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1. The words are the most powerful Memes.
Tech Proposal:
A tagging system where resources (information, links, or simply text) are marked with strings of tags

1. Every tag has an initial virulence value of Unknown, or a floating point number in the range of 0.0.
2. A string of tags (words) represents a single Bookmark (a meme-complex) with one or more resources (text or URL's)
3. The game is multiplayer and everyone can share bookmarks of information in the forms URLS, blogs entries or simple text.
4. Each time someone copies a Bookmark (and possibly rates it) the virulence of the attached informational resources increases with some value ( or function depending on rating and fixed increase)

5. Splicing of Bookmarks and Tags is to be allowed, where by splice i mean when two or more Bookmarks and their tags are being combined by people and their virulence gets updated and moderated by the users.

6. Predicting Virulence.
You can predict virulence on the fly during entering the tags. when words match a small number is displayed for the virulence of the word or the whole string.

Each time someone copies, links and material into the system it will be automatically rated for virulence... But i don't have idea for the mathematics of this thing... How exactly will the virulence increased for instance, the function is not yet designed.

Here's some Python code I have with a Django web app project...
I am showing the models.py file


class User:

class Group:

class Tag(Model):
    virulence = None
    word = VarChar(max_length=1024)

class Resource:
    uri = VarChar(max_length=1024)
    text = VarChar(max_length=4096)
    rating = Int()

class Bookmark(django.db.Model):
    tags = OneToMany(Tag)
    resources = OneToMany(Resource)
    users =  OneToMany(User)

    def total_rating(self):
        pass # :)

I had also the idea of realizing the previous with a functional model under Haskell but am I real newbie there, so I started the project with Python and traditional OO. I will host the project on GitHub(http://github.com)
« Last Edit: 2010-04-12 09:07:56 by Bohandez » Report to moderator   Logged

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"We think in generalities, we live in details"

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Re:Predicting Virulence of posted memes
« Reply #1 on: 2010-04-13 05:16:47 »
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[Blunderov] A warm welcome to you Bohandez

Congregants who know more about programming than I will doubtless comment on your useful and interesting idea. I'm only just beginning to discover the power of tags in my own day to day computer operations.....

Best regards.
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Gender: Male
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Re:Predicting Virulence of posted memes
« Reply #2 on: 2010-04-13 22:46:50 »
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The system should also have the capability to track all incoming URL requests (like "?q=tag1+tag2+...+tagn")  and adds them as Bookmarks (list of tags -> list of resources) to NULL resources. That is, a new information is dynamically added to the pool of data. The system should be able to predict tag 'virulence' by the actions on the URL at least.

* URL Actions:

  • : splices requires adding another string of tags at q in the beginning delimited by ';' or it could splice with itself producing horrible zombie mutant .

    Copies and splices also requires users (vectors) and a total count will be maintained.
  • « Last Edit: 2010-04-13 22:49:06 by Bohandez » Report to moderator   Logged
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