I'm particularly interested in this area. Is there a team of researchers within the organization that analyzes these numbers and external influences on search stats? For my research, I define external influences as nation-state sponsored censorship during high tension periods, which are usually short periods of time (a day or few hours). Of corse, I'm operating under the assumption that many people search for political candidates or figures on Wikipedia during a) election cycles and/or b) times of conflict. Any other researchers here focused on geopolitical conditions and its effects on digital search behavior and its stats?
Thank you, Tilman, for sharing the link to Intervention Analysis + Time Series. Feel free to connect with me at [email protected]. Ria On Thu, Sep 17, 2015 at 10:30 AM, Tilman Bayer <[email protected]> wrote: > This app is really cool. I wonder if beside future predictions, it > could be modified to support another use case: Assessing the impact of > past events and software changes on our pageviews. > > As many of us are aware, the Wikimedia movement has been struggling > for a long time to understand the effects of our work (and of outside > events) on our readership. And while WMF engineering teams are getting > better about doing, say, A/B tests, it's often not possible to provide > a controlled environment for such experiments. > > There's an established statistical technique aimed at such situations, > called "Intervention Analysis", see e.g. [1]. It requires modeling the > time series (here: monthly pageviews) with an ARIMA model just like it > has been done in the app. One then basically does a backdated forecast > from the time of the intervention, and uses the difference between > that forecast and the actual development to model the effect of the > intervention. I've been wondering recently if this has ever been used > for Wikipedia pageviews; yesterday while attending Morten's research > showcase talk about their "misalignment" paper I noticed that that > paper has indeed been applying it (to views of individual articles, > where it may be easier to isolate effects).[2] Is anyone aware of > other examples? > > Would it be possible to modify the app to support such backdated > forecasts, as a first step, and also for calculating their difference > to the actual development? > > [1] https://onlinecourses.science.psu.edu/stat510/node/76 > [2] > http://www-users.cs.umn.edu/~morten/publications/icwsm2015-popularity-quality-misalignment.pdf > (p.8) > > On Tue, Sep 15, 2015 at 5:28 PM, Dario Taraborelli > <[email protected]> wrote: > > > > An updated version of a pageview forecasting application written by > Ellery (Research & Data team) has just been released: > > > > https://ewulczyn.shinyapps.io/pageview_forecasting > > https://twitter.com/WikiResearch/status/643942154549592064 > > > > The data is refreshed monthly and it includes breakdowns by country and > platform. > > > > Dario > > > > > > > > Dario Taraborelli Head of Research, Wikimedia Foundation > > wikimediafoundation.org • nitens.org • @readermeter > > > > > > _______________________________________________ > > Analytics mailing list > > [email protected] > > https://lists.wikimedia.org/mailman/listinfo/analytics > > > > > > -- > Tilman Bayer > Senior Analyst > Wikimedia Foundation > IRC (Freenode): HaeB > > _______________________________________________ > Analytics mailing list > [email protected] > https://lists.wikimedia.org/mailman/listinfo/analytics > -- Ria Baldevia Twitter.com/RiaBaldevia
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