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Action Packs -- Discussion

Page history last edited by Christine Egger 9 years, 4 months ago

Action Packs

Live streams of calls to action, organized by theme

 

What can you do here?

 

  1. Find out what Action Packs are all about
  2. Browse the directory of active Action Packs
  3. Create an Action Pack  
  4. Add an Action Pack to your blog or website
  5. See the detailed log of active and proposed Action Packs
  6. Read notes and links relevant to the Action Packs program

 

Shortened URL for navigation page: http://bit.ly/ActionPacksNavigation

 

Social Actions Tuner + Actions Packs

BlogTalkRadio event

February 11, 2010

 

Notes

 

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 Dirichlet Allocation)
    • Attempts to figure out what the actions are. You don't say "I'm looking for literary actions." It's a data structure that consolidates that topic.
    • 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–Inverse Document Frequency
    • TFIDF helps you figure out if X is part of your topic.
    • 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. Both do similar work: pass in a corpus of data, tell it what you want to see

 

Use Case

 

Our goal is to build intelligent, feedback-based Action Packs that don't necessarily match a keyword used when the action was created, but that reflect keywords that evolve as people click on an action following a search.

 

Ehren -- that jumps ahead. You could use TFIDF to see last 500 things that kicked out. Use those examples to develop a filter with either technology. If it stopped generating results, would have to adjust it to how people are now

 

Peter: One approach: analyze what people have clicked on in last 30 days and say, this is what we want to build on.

 

Ehren: Great approach. Person doing the filtering wouldn't be biased.

 

LDA could tell you that alot of people are publishing things that fall between two action pack categories, such as literacy and music.

 

LDA helps you figure out the topics in your data.

 

We could announce, starting on this day, "please click-through tweets that have most compelling title."

 

Ehren: Great example. We can select based on body of action, can rely on community to select based on title. Would learn alot about what the titles are misleading, or not informative. We could find 5 of the most important words in the body. i.e. Grantwriter, vs. Grantwriter literacy children. Have you been recording clicks?

 

Action Items

 

  • Fix server, and perhaps move some of these tools to Social Actions server.
  • Find the right environment for the technical requirements.
  • Social Actions share click-through data.
  • Remove totally irrelevant actions out of action pack.
  • Get into deeper detail and analyze some logs.
    • Develop and apply a scientific method: apply different methods to different data. Not generalizing, or taking short-cuts. Very data-centric.
  • Get Ehren a grant so he can spend more time on this.

 

Time Line

 

Would like to revisit this on March 11th, a call for the Social Actions Developer community in general

 

Social Actions by then can:

  • Provide data with a clear description of where it came from and how it was selected, outline all assumptions
  • Creating filters on SA Tuner for Action Packs (one mark per action)

 

Ehren:

  • Make sure checked into subversion
  • Send list into Subversion, update to developer list
  • Set it up on Social Actions server
  • At that point, discuss minor changes to SA Tuner or an RSS feed, something streamlined so not 10 different steps to each action pack

 

Data 

 

  • Click-through information including referral URLs for actions when viewed on the profile page of Climate Actions or Education Actions, etc. We're recording that on the Social Actions log.
  • Raw Social Actions API data
  • We also have retweet information. I was research the Twitter API options for actions people are retweeting. Those all might contribute to the filter. Also
  • Friendfeed (clicking on it wouldn't contribute to click-through data) or anything similar (tweet this" sandbox to produce the filter)

 

Whether human review or click-throughs, the goal is to have 20-50 examples of what you do and don't want to see for each one.

 

What if there's no rhyme or reason to what is voted up or down? Is that a risk we run in voting up and down content?

 

  • Ehren: It can be. Could be people click randomly, or difference between "good" and "bad" is difficult for computer to understand. Expect it will catch most obvious: business environment vs ocean environment returns. We could definitely get started and get totally irrelevant returns out of there.

 

Another question: it's tough to keep up with creating Action Packs at the rate we can conceive of them. You mentioned the engine identifying clusters of activity. What about the next step: using these tools to identify action packs? Here's where there clusters. "Trending topics producing action packs." 

 

  • Ehren: Very interesting, but user interface. Computer will recognize significant terms, but person will have to assign label. Or, giant tag cloud, people click on what they're interested in, program kicks out the closes topic filter. That may be too confusing. The way it's laid out on Social Actions is very clear. Nothing to it but to do it -- feed data into LDA, tell it to generate 80 topics. None of this will save you time upfront.

 

Thinking of the data coming into a prism, distributed into streams of different color (issues). Question: creating that initial prism functionality to build off of, and build communities of action takers.

 

Social Actions has brainstormed about Action Packs sponsorship. We can't really move on that great idea until the quality of the actions coming through these packs is improved. We have a feeling this conversation is a starting point for a business model of sorts for pursuing its mission without compromising on any front.  

 

Resources and Links

 

 

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