Difference between revisions of "Submissions:2014/Measuring Editor Collaborativeness With Economic Modelling"
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| − | How well Editors work together has been a key question since the inception of the encyclopedia. When we also consider the twin question - how |
+ | How well Editors work together has been a key question since the inception of the encyclopedia. When we also consider the twin question - how articles improve depending on who edits them - our problem becomes similar to the Macroeconomics Field which considers the performance of Countries and their Exports. Borrowing from new developments in Macroeconomics, we reuse a technique which scores Editors by the quality of their edited-articles portfolio. Conversely we score Articles by the quality of their contributing editor portfolio. The method is a two node-type version of the Google pageRank algorithm. Then we also establish the "ground-truth" of editor investment and article development. For editors our ground-truth is "Labour Hours", which is derived from the editors contribution history. For articles our ground-truth is a mix 5 measures of articles text (citations per sentence, number of images, etc.) |
| − | We tune two variables in the |
+ | We tune two variables in the model called α and β, which determine the ”importance” of the high quality articles in an editor's portfolio, and highly invested editors in an article's contribution history. Both α and β are optimized to maximize the ranking correlations of editors (upto 0.75 corellation) and articles (upto 0.91 corellation) between the model and groundtruth metrics. We find the correllations for 12 categories on Wikipedia. By finding the optimizing values of α and β we know how characterized a category is by highly invested editors, or by highly developed articles. |
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| ⚫ | To get an intuition for the method consider these find telling extremes. The best editors in Category:Military history of the US - a category known for being very competitive - are characterized by emphasizing investment in touching many articles in the category. On the other end, the editors in Category:Sexual acts - a taboo subject where much editing could be considered perverse - are characterized by divesting in touching many articles in the category. |
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Revision as of 01:11, 21 March 2014
- Title of the submission
Measuring Editor Collaborativeness With Economic Modelling
- Themes (Proposal Themes - Community, Tech, Outreach, GLAM, Education)
Community - presents a way to characterise editors.
- Type of submission (Presentation Types - Panel, Workshop, Presentation, etc)
Presentation
- Author of the submission
Max Klein
- E-mail address
isalix@gmail.com
- Username
- US state or country of origin
California
- Affiliation, if any (organization, company etc.)
- Personal homepage or blog
[[1]]
- Abstract (at least 300 words to describe your proposal)
How well Editors work together has been a key question since the inception of the encyclopedia. When we also consider the twin question - how articles improve depending on who edits them - our problem becomes similar to the Macroeconomics Field which considers the performance of Countries and their Exports. Borrowing from new developments in Macroeconomics, we reuse a technique which scores Editors by the quality of their edited-articles portfolio. Conversely we score Articles by the quality of their contributing editor portfolio. The method is a two node-type version of the Google pageRank algorithm. Then we also establish the "ground-truth" of editor investment and article development. For editors our ground-truth is "Labour Hours", which is derived from the editors contribution history. For articles our ground-truth is a mix 5 measures of articles text (citations per sentence, number of images, etc.)
We tune two variables in the model called α and β, which determine the ”importance” of the high quality articles in an editor's portfolio, and highly invested editors in an article's contribution history. Both α and β are optimized to maximize the ranking correlations of editors (upto 0.75 corellation) and articles (upto 0.91 corellation) between the model and groundtruth metrics. We find the correllations for 12 categories on Wikipedia. By finding the optimizing values of α and β we know how characterized a category is by highly invested editors, or by highly developed articles.
To get an intuition for the method consider these find telling extremes. The best editors in Category:Military history of the US - a category known for being very competitive - are characterized by emphasizing investment in touching many articles in the category. On the other end, the editors in Category:Sexual acts - a taboo subject where much editing could be considered perverse - are characterized by divesting in touching many articles in the category.
- Length of presentation/talk (see Presentation Types for lengths of different presentation types)
- 75 Minutes
Preferred 30 mins to fit into a thematic session, but could talk longer.
- Will you attend WikiConference USA if your submission is not accepted?
Yes, I if receive a travel scholarship as well.
- Slides or further information (optional)
- Special request as to time of presentations
Interested attendees
If you are interested in attending this session, please sign with your username below. This will help reviewers to decide which sessions are of high interest. Sign with four tildes. (~~~~).
- Add your username here.