Upcoming research newsletter: new papers open for review
We’re preparing for the May 2017 research newsletter and looking for contributors. Please take a look at:
https://etherpad.wikimedia.org/p/WRN201705 and add your name next to any paper you are interested in covering. Our target publication date is Friday August 4 UTC although actual publication might take place several days later. As usual, short notes and one-paragraph reviews are most welcome.
Highlights from this month:
• “A wound that has been festering since 2007”: The Burma/Myanmar naming controversy and the problem of rarely challenged assumptions on Wikipedia
• An algorithm designed to expand Wikipedia in all languages
• A productive clash of perspectives? The interplay between articles’ and authors’ perspectives and their impact on Wikipedia edits in a controversial domain
• Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach
• Automatic Classification of Wikipedia Articles by Using Convolutional Neural Network
• Conclusion: So, what is the gender breakdown of heads of government?
• Connecting the sum of all human knowledge, one edit at a time
• Cumulative Experience and Recent Behavior and their Relation to Content Quality on Wikipedia
• Cyberfeminism on Wikipedia: Visibility and deliberation in feminist Wikiprojects
• Do wikipedia science articles reflect on state-of-the-art research?
• Embracing Wikipedia as a teaching and learning tool benefits health professional schools and the populations they serve
• Estimating the Quality of Articles in Russian Wikipedia Using the Logical-Linguistic Model of Fact Extraction
• How Does Knowledge Come By?
• Information Has Value: A View from Three Institutions
• Nation image and its dynamic changes in Wikipedia
• Projects Wikisource and Wikibooks as information resource
• Student perceptions of writing with Wikipedia in Australian higher education
• Wikipedia Controversial Articles by Size of Controversy Section
• Wikipedia Matters
• Wikipedia Vandal Early Detection: from User Behavior to User Embedding by Marry Trame - network, memory, deep learning
If you have any question about the format or process feel free to get in touch off-list.