I read the summary of the VE study, and I have a question. Anecdotally, I am hearing from multiple sources that new editors *who attend workshops or editathons in person* prefer VE over wikitext for ease of use. Do we have any data specifically about the productivity and longevity of this population of users when they are introduced to to Wikipedia editing on VE instead of wikitext?
Thanks!
Pine
LeilaA friendly reminder that this is happening in 23 min. :-)Best,
YouTube stream: https://www.youtube.com/watch?v=vGyrVg_qKSM
IRC: #wikimedia-researchOn Mon, Jul 27, 2015 at 2:47 PM, Leila Zia <leila@wikimedia.org> wrote:Hi everyone,
The next Research showcase will be live-streamed this Wednesday, July 29 at 11.30 PT. The streaming link will be posted on the lists a few minutes before the showcase starts (sorry, we haven't been able to solve this, yet. :-() and as usual, you can join the conversation on IRC at #wikimedia-research.
We look forward to seeing you!
Leila
This month:By Aaron Halfaker
- VisualEditor's effect on newly registered users
It's been nearly two years since we ran an initial study of VisualEditor's effect on newly registered editors. While most of the results of this study were positive (e.g. workload on Wikipedians did not increase), we still saw a significant decrease in the newcomer productivity. In the meantime, the Editing team has made substantial improvements to performance and functionality. In this presentation, I'll report on the results of a new experiment designed to test the effects of enabling this improved VisualEditor software for newly registered users by default. I'll show what we learned from the experiment and discuss some results have opened larger questions about what, exactly, is difficult about being a newcomer to English Wikipedia.
Wikipedia knowledge graph with DeepDive
By Juhana Kangaspunta and Thomas Palomares (10-week student project)
Despite the tremendous amount of information present on Wikipedia, only a very little amount is structured. Most of the information is embedded in text and extracting it is a non-trivial challenge. In this project, we try to populate Wikidata, a structured component of Wikipedia, using DeepDive tool to extract relations embedded in the text. We finally extracted more than 140,000 relations with more than 90% average precision. We will present DeepDive and the data that we use for this project, we explain the relations we focused on so far and explain the implementation and pipeline, including our model, features and extractors. Finally, we detail our results with a thorough precision and recall analysis.
_______________________________________________
Wiki-research-l mailing list
Wiki-research-l@lists.wikimedia.org
https://lists.wikimedia.org/mailman/listinfo/wiki-research-l