Difference between revisions of "Submissions:2014/Measuring Editor Collaborativeness With Economic Modelling"
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| + | In our performance-driven world we care deeply about quantifying our contributions to Wikis, and yet we remain addicted the ''Edit Count'' metric despite all its shortcomings. Smarter metrics have been proposed such as: counting hours spent editing, and the survival rate of a users contributed text. We investigated a method from Macroeconomics which considers the “exports” of a User - their contributed-to article portfolio. An unforeseen consequence was found in the results which suggest using better metrics than measuring individual performance, but rather editor collaborativeness. |
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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 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.) |
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| + | First let us show that the question of ''ranking user performance, based on article portfolio'', rests on answering the twin question ''ranking article quality, based on contributor performances''. It is possible to solve these two questions simultaneously, and the solution is similar to Google PageRank algorithm. The way this works is we consider the user-article “matrix” of a Category (see Figure below of ''Category:Feminist Writers''). Then we produce the editor and article rankings, and compare them to two ground-truth rankings. 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.) |
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| − | 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. |
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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| + | The correlation between our produced rankings and the ground-truth rankings rely on two factors in our model, termed α and β. These determine the ''importance'' of the high quality articles in an editor's portfolio, and conversely highly invested editors in an article's contribution history. |
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| + | When both α and β are optimized to maximize the ranking correlations we find correlations between 0.46 and 0.91 between the model and groundtruth metrics. (see Table below). 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. Taken together we can talk about the collaborativeness of a Category, which would best feature highly divested editors, but highly developed articles. |
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Revision as of 20:08, 26 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)
In our performance-driven world we care deeply about quantifying our contributions to Wikis, and yet we remain addicted the Edit Count metric despite all its shortcomings. Smarter metrics have been proposed such as: counting hours spent editing, and the survival rate of a users contributed text. We investigated a method from Macroeconomics which considers the “exports” of a User - their contributed-to article portfolio. An unforeseen consequence was found in the results which suggest using better metrics than measuring individual performance, but rather editor collaborativeness.
First let us show that the question of ranking user performance, based on article portfolio, rests on answering the twin question ranking article quality, based on contributor performances. It is possible to solve these two questions simultaneously, and the solution is similar to Google PageRank algorithm. The way this works is we consider the user-article “matrix” of a Category (see Figure below of Category:Feminist Writers). Then we produce the editor and article rankings, and compare them to two ground-truth rankings. 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.)
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.
The correlation between our produced rankings and the ground-truth rankings rely on two factors in our model, termed α and β. These determine the importance of the high quality articles in an editor's portfolio, and conversely highly invested editors in an article's contribution history. When both α and β are optimized to maximize the ranking correlations we find correlations between 0.46 and 0.91 between the model and groundtruth metrics. (see Table below). 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. Taken together we can talk about the collaborativeness of a Category, which would best feature highly divested editors, but highly developed articles.
- 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
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- Add your username here.