Difference between revisions of "Submissions:2014/Measuring Editor Collaborativeness With Economic Modelling"
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;Abstract ''(at least 300 words to describe your proposal)'': |
;Abstract ''(at least 300 words to describe your proposal)'': |
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| − | Even though Wikipedia is a vanguard of collaboration, Wikipedians still remain stuck with the old-thinking individual user performance metrics - most notably “Edit Count”. The search, for describing a group of editors has started through the Foundation's WikiMetrics cohort notion |
+ | Even though Wikipedia is a vanguard of collaboration, Wikipedians still remain stuck with the old-thinking individual user performance metrics - most notably “Edit Count”. The search, for describing a group of editors has started through the Foundation's WikiMetrics cohort notion <ref>Wikimetrics. [http://metrics.wmflabs.org/] </ref>, hasn't answered “how” a set of users works together. |
A recent stream of research in Macroeconomics has shown simple techniques for predicting GDP with very little information <ref>Hidalgo, Hausmann, The Building Blocks of Economic Complexity [http://chidalgo.com/Papers/HidalgoHausmann_PNAS_2009.pdf] </ref> <ref>Caldarelli et al. Firm Grounds. [https://www.ncbi.nlm.nih.gov/pubmed/23094044] </ref>, which can be translated into the wiki realm. Using only the data of which countries export which products (not even how much of each product), one can quickly predict GDP rankings. Here we repurpose the algorithm, so that Editors are countries, articles are products, and GDP is “Labour Hours” (an edit count derivative <ref>Halfaker, Geiger, Using Edit Sessions [http://www-users.cs.umn.edu/~halfak/publications/Using_Edit_Sessions_to_Measure_Participation_in_Wikipedia/geiger13using-preprint.pdf]</ref>) . |
A recent stream of research in Macroeconomics has shown simple techniques for predicting GDP with very little information <ref>Hidalgo, Hausmann, The Building Blocks of Economic Complexity [http://chidalgo.com/Papers/HidalgoHausmann_PNAS_2009.pdf] </ref> <ref>Caldarelli et al. Firm Grounds. [https://www.ncbi.nlm.nih.gov/pubmed/23094044] </ref>, which can be translated into the wiki realm. Using only the data of which countries export which products (not even how much of each product), one can quickly predict GDP rankings. Here we repurpose the algorithm, so that Editors are countries, articles are products, and GDP is “Labour Hours” (an edit count derivative <ref>Halfaker, Geiger, Using Edit Sessions [http://www-users.cs.umn.edu/~halfak/publications/Using_Edit_Sessions_to_Measure_Participation_in_Wikipedia/geiger13using-preprint.pdf]</ref>) . |
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Revision as of 20:51, 27 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
- Abstract (at least 300 words to describe your proposal)
Even though Wikipedia is a vanguard of collaboration, Wikipedians still remain stuck with the old-thinking individual user performance metrics - most notably “Edit Count”. The search, for describing a group of editors has started through the Foundation's WikiMetrics cohort notion [1], hasn't answered “how” a set of users works together.
A recent stream of research in Macroeconomics has shown simple techniques for predicting GDP with very little information [2] [3], which can be translated into the wiki realm. Using only the data of which countries export which products (not even how much of each product), one can quickly predict GDP rankings. Here we repurpose the algorithm, so that Editors are countries, articles are products, and GDP is “Labour Hours” (an edit count derivative [4]) .
This method comes only from considering a matrix of which editors have touched which articles – producing an entirely new perspective on Wikipedia (see Figure below). The simplicity of this model can help us to quickly and easily determine which areas are more likely to be hostile and power-user dominated, and which are more egaliterean an collaborative.
Incredibly, this borrowed method works even better for Wikis than Economies. Where the maximum achievable correlation in Macroeconomics is about 0.42, we can achieve correlations of upto 0.91. But the real innovation comes from, two factors which are tweaked to optimize the model. These two variables are: the importance of the high quality articles in an editor's contribution portfolio, and conversely the “importance” of highly invested editors in an article's contribution history. These variables can range independently, and tell us about “collaborativeness”.
Collaborativeness is determined from the edit patterns of the most highly invested editors. Do they edit many articles? And how well developed are the articles they edit? Consider these 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 not touching many articles in the category, more collaborative.
References
- 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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