Just a reminder this will be taking place in one hour!
On Tue, Feb 14, 2017 at 2:49 PM, Sarah R <srodl...@wikimedia.org> wrote: > Hi Everyone, > > The next Research Showcase will be live-streamed this February 15, 2017 at > 11:30 AM (PST) 18:30 UTC. > > YouTube stream: https://www.youtube.com/watch?v=m6smzMppb-I > > As usual, you can join the conversation on IRC at #wikimedia-research. > And, you can watch our past research showcases here > <https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#February_2017> > . > > This month's presentations: > > Wikipedia and the Urban-Rural DivideBy *Isaac Johnson*Wikipedia articles > about places, OpenStreetMap features, and other forms of peer-produced > content have become critical sources of geographic knowledge for humans and > intelligent technologies. We explore the effectiveness of the peer > production model across the rural/urban divide, a divide that has been > shown to be an important factor in many online social systems. We find that > in Wikipedia (as well as OpenStreetMap), peer-produced content about rural > areas is of systematically lower quality, less likely to have been produced > by contributors who focus on the local area, and more likely to have been > generated by automated software agents (i.e. “bots”). We continue to > explore and codify the systemic challenges inherent to characterizing rural > phenomena through peer production as well as discuss potential solutions. > > > Wikipedia Navigation VectorsBy *Ellery Wulczyn > <https://www.mediawiki.org/wiki/User:Ewulczyn_(WMF)>*In this project, we > learned embeddings for Wikipedia articles and Wikidata > <https://www.wikidata.org/wiki/Wikidata:Main_Page> items by applying > Word2vec <https://en.wikipedia.org/wiki/Word2vec> models to a corpus of > reading sessions. Although Word2vec models were developed to learn word > embeddings from a corpus of sentences, they can be applied to any kind of > sequential data. The learned embeddings have the property that items with > similar neighbors in the training corpus have similar representations (as > measured by the cosine similarity > <https://en.wikipedia.org/wiki/Cosine_similarity>, for example). > Consequently, applying Wor2vec to reading sessions results in article > embeddings, where articles that tend to be read in close succession have > similar representations. Since people usually generate sequences of > semantically related articles while reading, these embeddings also capture > semantic similarity between articles. > > -- > Sarah R. Rodlund > Senior Project Coordinator-Product & Technology, Wikimedia Foundation > srodl...@wikimedia.org > -- Sarah R. Rodlund Senior Project Coordinator-Product & Technology, Wikimedia Foundation srodl...@wikimedia.org
_______________________________________________ Wiki-research-l mailing list Wiki-research-l@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
