Hey folks,
This event will be starting in 15 minutes. See you there!
-Aaron
On Wed, Dec 17, 2014 at 1:49 PM, Dario Taraborelli <
dtaraborelli(a)wikimedia.org> wrote:
This month’s Research showcase will be held tomorrow, *Thursday, Dec.
18th at 3PM PST (2300 UTC)*. As usual, the event will be recorded and
publicly streamed on YouTube (link
<https://www.youtube.com/watch?v=xPO8XhmeUAU>) We’ll hold a discussion
and take questions from the Wikimedia Research IRC channel (
#wikimedia-research
<http://webchat.freenode.net/?channels=wikimedia-research> on freenode).
Looking forward to seeing you there.
Dario
——
This month:
*Mobile Madness: The Changing Face of Wikimedia Readers*By *Oliver Keyes*
<https://www.mediawiki.org/wiki/User:Ironholds>A dive into the data we
have around readership that investigates the rising popularity of the
mobile web, countries and projects that are racing ahead of the pack, and
what changes in user behaviour we can expect to see as mobile grows.
*Global Disease Monitoring and Forecasting with Wikipedia*By *Reid
Priedhorsky <http://www.lanl.gov/expertise/profiles/view/reid-priedhorsky>* (Los
Alamos National Laboratory)Infectious disease is a leading threat to
public health, economic stability, and other key social structures. Efforts
to mitigate these impacts depend on accurate and timely monitoring to
measure the risk and progress of disease. Traditional, biologically-focused
monitoring techniques are accurate but costly and slow; in response, new
techniques based on social internet data, such as social media and search
queries, are emerging. These efforts are promising, but important
challenges in the areas of scientific peer review, breadth of diseases and
countries, and forecasting hamper their operational usefulness. We examine
a freely available, open data source for this use: access logs from the
online encyclopedia Wikipedia. Using linear models, language as a proxy for
location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically,
our proof-of-concept yields models with r² up to 0.92, forecasting value up
to the 28 days tested, and several pairs of models similar enough to
suggest that transferring models from one location to another without
re-training is feasible. Based on these preliminary results, we close with
a research agenda designed to overcome these challenges and produce a
disease monitoring and forecasting system that is significantly more
effective, robust, and globally comprehensive than the current state of the
art.
_______________________________________________
Wiki-research-l mailing list
Wiki-research-l(a)lists.wikimedia.org
https://lists.wikimedia.org/mailman/listinfo/wiki-research-l