Difference between revisions of "From Mdma to Lady Gaga : Expertise and Contribution Behavior of Editing Communities on Wikipedia"
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| + | | title = From Mdma to Lady Gaga : Expertise and Contribution Behavior of Editing Communities on Wikipedia | ||
| + | | date = 2016 | ||
| + | | authors = [[Louis J. Dijkstra]]<br />[[Lisa J. Krieg]] | ||
| + | | doi = 10.1016/j.procs.2016.11.013 | ||
| + | | link = http://www.sciencedirect.com/science/article/pii/S1877050916326801 | ||
| + | }} | ||
'''From Mdma to Lady Gaga : Expertise and Contribution Behavior of Editing Communities on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Louis J. Dijkstra]] and [[Lisa J. Krieg]]. | '''From Mdma to Lady Gaga : Expertise and Contribution Behavior of Editing Communities on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Louis J. Dijkstra]] and [[Lisa J. Krieg]]. | ||
== Overview == | == Overview == | ||
In this paper authors present a methodology for gaining a better understanding of the contribution behavior, interests and expertise of communities of [[Wikipedia]] users. Starting from a list of core articles and their main editors, authors identify which other articles (outside of the initial list) they contributed to ‘significantly’. The ordering is based on (empirical) Bayesian estimates of the contribution probabilities for each of the articles. By constructing a co-contribution network, authors can identify the general themes the community expresses exceptional interest (or disinterest) in. In order to show what type of insights one might gain from employing the proposed method, authors use the editors that contributed to the articles on designer drugs as a case study. Authors find that the users in this community contribute significantly to articles on pharmaceuticals, popular party drugs, chemistry, mental illnesses, diseases, medicine and cell biology. | In this paper authors present a methodology for gaining a better understanding of the contribution behavior, interests and expertise of communities of [[Wikipedia]] users. Starting from a list of core articles and their main editors, authors identify which other articles (outside of the initial list) they contributed to ‘significantly’. The ordering is based on (empirical) Bayesian estimates of the contribution probabilities for each of the articles. By constructing a co-contribution network, authors can identify the general themes the community expresses exceptional interest (or disinterest) in. In order to show what type of insights one might gain from employing the proposed method, authors use the editors that contributed to the articles on designer drugs as a case study. Authors find that the users in this community contribute significantly to articles on pharmaceuticals, popular party drugs, chemistry, mental illnesses, diseases, medicine and cell biology. | ||
Revision as of 00:55, 5 January 2021
Authors | Louis J. Dijkstra Lisa J. Krieg |
|---|---|
Publication date | 2016 |
DOI | 10.1016/j.procs.2016.11.013 |
Links | Original |
From Mdma to Lady Gaga : Expertise and Contribution Behavior of Editing Communities on Wikipedia - scientific work related to Wikipedia quality published in 2016, written by Louis J. Dijkstra and Lisa J. Krieg.
Overview
In this paper authors present a methodology for gaining a better understanding of the contribution behavior, interests and expertise of communities of Wikipedia users. Starting from a list of core articles and their main editors, authors identify which other articles (outside of the initial list) they contributed to ‘significantly’. The ordering is based on (empirical) Bayesian estimates of the contribution probabilities for each of the articles. By constructing a co-contribution network, authors can identify the general themes the community expresses exceptional interest (or disinterest) in. In order to show what type of insights one might gain from employing the proposed method, authors use the editors that contributed to the articles on designer drugs as a case study. Authors find that the users in this community contribute significantly to articles on pharmaceuticals, popular party drugs, chemistry, mental illnesses, diseases, medicine and cell biology.
