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Action Packs -- Discussion

This version was saved 14 years, 2 months ago View current version     Page history
Saved by Christine Egger
on February 11, 2010 at 5:15:35 pm
 

Contents

 

  • Introduction
  • Contributors
  • Use Case
  • Action Items
  • Time Line
  • Data
  • Resources and Links

 

Introduction

 

In January 2010, Ehren Foss of Prelude Interactive shared an update on his Social Actions Tuner project.  The update led to a discussion about applying TF-IDF functionality to the Action Packs in order to more intelligently sort actions by cause area, geographic location, or theme. In non-technical terms, each action pack would 'learn' to display relevant actions on a cause area, geographic location, or theme based on the keywords contained in actions that have had a high number of click-throughs (and possibly RTs) in the past. On February 11, 2010, Social Actions convened an open call on the topic. This wiki page was setup in advance of the call to serve as an organizing tool for implementing the innovation.

 

Contributors

 

 

Technical Overview

 

  • Filtering with Social Actions API only gets you so far
    • data points
  • Description of Social Actions Tuner
    • Built on technology called LDA (latent _____ allocation): analyzes groups of text using the terms and their frequency and how they appear across all articles. Attempts to categorize data coming in.
    • Used it to build a tool for someone who'd log into Social Actions Tuner, search by category/term/etc. Would vote results -- thumbs up if it was what you were looking for, thumbs down if not.
    • Broad application -- if you can behind the scenes record what people are looking for, develop an idea of what was searched for and ultimately clicked on. Improve those search results, and you increase the chances they'll click through and get involved in an action.
    • Lots of possible parameters - Number of terms to search, lots of ways to do this. Need to test it alot and hone it for your application. Can what the output.
  • Term Frequency to Inverse D__________ Frequency
    • Looks at terms used in particular documents that AREN'T used in all of the documents.
  • You could used "literacy action" that uses books etc. and never use the word literacy. Both techniques help you find those actions.
  • Most appropriate for Social Actions is most likely TFIDF

 

Use Case

 

(notes)

 

Time Line

 

(notes)

 

Action Items

 

(notes)

 

Data

 

Social Actions API

 

  • Title
  • Description
  • Action Source
  • Action Type
  • Hits

 

Social Actions API Click-Through Data

 

 

Twitter API

 

  • RTs (maybe)

 

Resources and Links

 

 

 

 

 

 

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