Hi,
Christian is working on page view definitions mostly.
So I gladly defer to him for further comments.
One comment on anonymized search stats:
This has been tried before, also at WMF, and was not a very good idea.
Even with extensive filtering some very privacy sensitive data were still exposed (think passwords).
Those password data shouldn’t have landed in the search box, but anything that can be input wrongly, will be.
As for de-anonimization, and how disastrous this can be:
https://en.wikipedia.org/wiki/AOL_search_data_leak
101 Dumbest Moments in Business
57. AOL, Part 2
In an "attempt to reach out to the academic community with new research tools," AOL releases the search queries of 657,000 users.
Though AOL insists that the data contains no personally identifiable information, the New York Times and other news outlets promptly identify a number of specific users, including searcher No. 4417749, soon-to-be-ex-AOL-subscriber Thelma Arnold of Lilburn, Ga., whose queries include "womens underwear" and "dog that urinates on everything."
The gaffe leads to the resignation of AOL's chief technology officer and a half-billion-dollar class-action lawsuit.
Erik Zachte
From: analytics-bounces@lists.wikimedia.org [mailto:analytics-bounces@lists.wikimedia.org] On Behalf Of Dario Taraborelli
Sent: Saturday, December 14, 2013 1:04
To: A mailing list for the Analytics Team at WMF and everybody who has an interest in Wikipedia and analytics.
Cc: Brent Hecht
Subject: Re: [Analytics] Wikipedia dataset to support NLP disambiguation
Shilad, Edgard,
replying here to this and related threads.
1) Analytics Engineering (primarily Christian Aistleitner and Erik Zachte) is currently working on defining how pageviews are counted and extracted from the raw request logs. This work is part of a larger effort to replace the legacy webstatscollector [1], which will produce more accurate data as well as the ability to parse requests in a more flexible way. Christian and Erik should be able to comment on whether referral and search query string extraction for inbound traffic are use cases falling within the initial scope of this project. The publication of this data is a different matter (see below).
2) Releasing anonymized internal search data is not AFAIK one of the priorities the team is currently working on. As Andrew noted in a previous thread, engineering effort aside, further releases of private data will be subject to the new privacy policy which is currently being discussed on Meta [2]. I don’t expect we’ll invest any effort into anonymizing or aggregating data for the purpose of publication until the privacy policy consultation is settled. Search is also undergoing a major overhaul [3]
3) As per Federico, a short description of all logs generated at Wikimedia (including MediaWiki logs, page request logs, search logs and EventLogging data) can be found at on Wikitech [4]
Dario
On Dec 10, 2013, at 10:09 PM, Shilad Sen <ssen@macalester.edu> wrote:
Greetings!
I'm a Professor at Macalester College in Minnesota, and I have been collaborating with Brent Hecht and many students to develop a Java framework for extracting multilingual knowledge from Wikipedia [1]. The framework is pre-alpha now, but we hope to offer a stable release in the next month.
Given a phrase (e.g. "apple"), our library must identifying articles associated with a phrase. This is a probabilistic question. How likely is the phrase "apple" to refer to the article about the fruit vs the company? This simple task (often called Wikification or disambiguation) forms the basis of many NLP algorithms.
Google and Stanford have released an awesome dataset to support this task [2]. It contains the *text* of all internet hyperlinks to Wikipedia articles. This dataset makes the problem much easier, but it has two serious deficiencies. First, it only contains links to articles in English Wikipedia. Second, it was generated once by Google, and it is unlikely Google will update it.
The WMF could create a similar dataset by publishing the most common inbound search queries for all WP pages across all language editions. This dataset would enable individuals, researchers and small companies (not just Google and Microsoft) to harness Wikipedia data for their applications.
Does this seem remotely possible? I've thought a little about engineering and privacy issues related to the dataset. Neither are trivial, but I think they are feasible, and I'd be happy to volunteer my engineering effort.
If you think the idea has legs, how do we develop a more formal proposal about the dataset?
Thanks for your feedback!
-Shilad
--
Shilad W. Sen
Assistant Professor
Mathematics, Statistics, and Computer Science Dept.
Macalester College
ssen@macalester.edu
651-696-6273
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