Re: [Wiki-research-l] Wikimedia Research Showcase -- Thurs. Dec 18th: mobile readership; disease monitoring with Wikipedia

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