Hey James,

When we first started looking at zero results rate (ZRR), it was an easy metric to calculate, and it was surprisingly high. We still look at ZRR because it is so easy to measure, and anything that improves it is probably a net positive (note the big dip when the new completion suggester was deployed!!), but we have more complex metrics that we prefer. There's user engagement/augmented clickthroughs, which combines clicks and dwell time and other user activity. We also use historical click data in a metric that improves when we move clicked-on results higher in the results list, which we use with the Relevance Forge.

And I didn't mean to give the impression that most zero-results queries are gibberish, though many, many are. And that was something we didn't really know a year ago. There are also non-gibberish results that correctly get zero results, like most DOI and many media player queries. We also see a lot of non-notable (not-yet-notable?) public figures (local bands, online artists, youtube musicians), and sometimes just random names.

The discussion in response to Dan's original comment in Phab mentions some approaches to reduce the risk of automatically releasing private info, but I still take an absolute stand against unreviewed release. If I can get a few hundred people to click on a link like this, I can get any message I want on that list. (Curious? Did you click?) The message could be less anonymous and much more obnoxious, obviously.

50 character limits won't stop emails and phone numbers from making the list (which invites spam and cranks). Those can be filtered, but not perfectly.

I've only looked at these top lists by day in the past, but on that time scale the top results are usually under 1000 count (and that includes IP duplicates), so the list of queries with 100 IPs might also be very small.

As I said, I'm happy to do the data slogging to try this in a better fashion if this task is prioritized, and I'd be happy to be wrong about the quality of the results, but I'm still not hopeful.

—Trey

Trey Jones
Software Engineer, Discovery
Wikimedia Foundation



On Fri, Jul 15, 2016 at 11:44 AM, James Heilman <jmh649@gmail.com> wrote:
Hey Trey

Thanks for the in depth discussion. So if the terms people are using that result in "zero search results" are typically gibberish why do we care if 30% of our searches result in "zero search results"? A big deal was made about this a while ago.

If one was just to look at those search terms that more than 100 IPs searched for would that not remove the concerns about anonymity? One could also limit the length of the searches displaced to 50 characters. And just provide the first 100 with an initial human review to make sure we are not miss anything.

James

On Fri, Jul 15, 2016 at 9:31 AM, Trey Jones <tjones@wikimedia.org> wrote:

Pine, thanks for the forward. Regulars on the Discovery list may know me, but James probably does not. I've manually reviewed tens of thousands of generally poorly performing queries (fewer than 3 results) and skimmed hundreds of thousands more from many of the top 20 Wikipedias—and to a lesser extent other projects—over the year I've been at the WMF and in Discovery. You can see my list of write ups here.

So I want to say that this is an awesome idea—which is why many people have thought of it. It was apparently one of the first ideas the Discovery department had when they formed (see Dan's notes linked below). It was also one of the first ideas I had when I joined Discovery a few months later.

Dan Garry's notes on T8373 and the following discussion pretty much quash the idea of automated extraction and publication from a privacy perspective. People not only divulge their own personal information, they also divulge other people's personal information. One example: some guy outside the U.S. was methodically searching long lists of real addresses in Las Vegas. I will second Dan's comments in the T8373 discussion; all kinds of personal data end up in search queries. A dump of search queries was provided in September 2012, but had to be withdrawn over privacy concerns. 

Another concern for auto-published data: never underestimate the power of random groups of bored people on the internet. 4chan decided to arrange Time Magazine poll results so the first letter spelled out a weird message. It would be easy for 4chan, Reddit, and other communities to get any message they want on that list if they happened to notice that it existed. See also Boaty McBoatface and Mountain Dew "Diabeetus" (which is not at all the worst thing on that list). We don't want to have to try to defend against that.

In my experience, the quality of what's actually there isn't that great. One of my first tasks when I joined Discovery was to look at daily lists of top 100 zero-results queries that had been gathered automatically. I was excited by this same idea. The top 100 zero-results query list was a wasteland. (Minimal notes on some of what I found are here.) We could make it better by focusing on human-ish searchers, using basic bot-exclusion techniques, ignoring duplicates from the same IP, and such, but I don't think it would help. And while Wikipedia is not for children, there could be an annoying amount of explicit adult material on the list, too. We would probably find some interesting spellings of Facebook and WhatsApp, though.

If we're really excited about this, I could imagine using better techniques to pull zero-results queries and see if anything good is in there, but we'd have to commit to some sort of review before we publish it. For example, Discernatron data, after consulting with legal, is reviewed independently by two people, who then have to reconcile any discrepancies, before being made public. So I think we'd need an ongoing commitment to have at least two people under NDA who would review any list before publication. 500-600 queries takes a couple hours per person (we’ve done that for the Discernatron), so the top 100 would probably be less than an hour. I'd even be willing to help with the review (as I am for Discernatron) if we found there was something useful in there—but I'm not terribly hopeful. We'd also need more people to efficiently and effectively review queries for other languages if we wanted to extend this beyond English Wikipedia.

Finally, if this is important enough and the task gets prioritized, I'd be willing to dive back in and go through the process once and pull out the top zero-results queries, this time with basic bot exclusion and IP deduplication—which we didn't do early on because we didn't realize what a mess the data was. We could process a week or a month of data and categorize the top 100 to 500 results in terms of personal info, junk, porn, and whatever other categories we want or that bubble up from the data, and perhaps publish the non-personal-info part of the list as an example, either to persuade ourselves that this is worth pursuing, or as a clearer counter to future calls to do so.

—Trey

Trey Jones
Software Engineer, Discovery
Wikimedia Foundation


On Fri, Jul 15, 2016 at 10:09 AM, Pine W <wiki.pine@gmail.com> wrote:

Forwarding

Pine

---------- Forwarded message ----------
From: "James Heilman" <jmh649@gmail.com>
Date: Jul 15, 2016 06:33
Subject: [Wikimedia-l] Improving search (sort of)
To: "Wikimedia Mailing List" <wikimedia-l@lists.wikimedia.org>
Cc:

A while ago I requested a list of the "most frequently searched for terms
for which no Wikipedia articles are returned". This would allow the
community to than create redirect or new pages as appropriate and help
address the "zero results rate" of about 30%.

While we are still waiting for this data I have recently come across a list
of the most frequently clicked on redlinks on En WP produced by Andrew West
https://en.wikipedia.org/wiki/User:West.andrew.g/Popular_redlinks Many of
these can be reasonably addressed with a redirect as the issue is often
capitals.

Do anyone know where things are at with respect to producing the list of
most search for terms that return nothing?

--
James Heilman
MD, CCFP-EM, Wikipedian

The Wikipedia Open Textbook of Medicine
www.opentextbookofmedicine.com
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--
James Heilman
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The Wikipedia Open Textbook of Medicine
www.opentextbookofmedicine.com