This is awesome work! Deleting underscores and using language detection
sound like great approaches to take :). What's 14####, etc?
One of the worries I have here is the fact that the cirrus logs very
deliberately don't (yet) give us enough information to identify readers.
Are those buildup queries or the most common queries automata? We simply
don't know :/.
(Don't worry, Erik and I have been brainstorming on ways to get more
On 24 July 2015 at 17:16, Trey Jones <tjones(a)wikimedia.org> wrote:
I got access to some logs and I've been slogging through the data. In
particular, I've partially analyzed a sample of 100K zero-result full_text
searches against enwiki, over the course of about an hour (2015-07-23
07:51:29 to 2015-07-23 08:55:42). My results and opinions are below.
*TL;DR Summary: If these patterns hold for another sample (and across
languages), we should be able to get some decent mileage out of these
* - find sources of weird patterns and either ignore them, or contact the
source and redirect them to a more appropriate destination*
* - use language or character set detection to redirect queries to other
* - filter the term "quot" from queries*
* - filter 14###########: from the front of queries*
* - replace _ with space in queries*
All of this is somewhat rough, and exact numbers aren't guaranteed. Also
the categories may overlap. I also intend to look for these same patterns
from another sample from a different day and make sure they are more
general and not just temporary idiosyncrasies. I also plan to look through
other language wikis (i.e., Spanish and French to start) to see if there
are cross-linguistic patterns like these.
I think we have to some how come to terms with the fact that some queries
don't deserve results, and maybe figure out the source of such
"illegitimate" queries and filter them. (I'd really like to be able to
track down the referrer, if there is one, for a lot of the weirder queries.)
- 248 Dounload feer game
- all via web... and Google can't find it. That's just weird.
Some other categories of queries are below. The numbers are "<total
queries> / <unique queries>". Since this is a 100K sample of zero-result
queries, and zero-results are about 25% of all results, each 1,000 of total
queries here represents about 0.25% of all search queries.
253 / 171 string of numbers
3610 / 2505 no Latin letters
- I see Korean, Thai, Japanese, Cyrillic, doi #s (see below), Arabic,
Hebrew, Greek, Armenian, Georgian, Devanagari, Burmese, Chinese, and some
emoji (e.g., 11 searches for 😜💗🎨❤️💋😞☀️💦).
- I also saw instances of mixed Latin / non-Latin queries
- Includes gibberish, which is hard to grep for, but easy to spot by eye
- Lots of the non-gibberish ones are clearly in other languages, and I saw
queries in other Latin-alphabet languages go by, too.
2630 / 2627 DOIs, all in quotes
3015 / 1017 have quot in them (which gets auto-corrected to "quote",
- 327 are one word: quot ... quot
- I don't know where these are coming from, but they are weird. If we
strip "quot" we would get many of these. This must be coming from some
source that is adding quotes, then escaping them as """ and then
stripping & and ;. Weird.
7155 / 6337 #:Name
- almost all are 14###########:Text
- e.g., 1436755654740:Sherlock Holmes
- These all look like Wikipedia titles!
- Two each of 0:... and 6000:...
114 / 85 actual http(s):// URLs
488 / 244 URL-like things starting with www... and ending with .com, .ru,
211 / 132 other searches starting with “www.”
1085 / 1083 article searches in this format: ('"<TITLE>"',
2457 / 2060 TV episodes (based on the presence of "S#E#"—that's season #,
8419 / 7523 AND boolean searches
703 / 701 OR boolean searches
- Many of these look auto-generated, esp in the aggregate.
- For example: there are 498 / 249 "House_of_Gurieli" AND ... queries
6310 / 5742 queries with _ in them
- only 934 / 790 if we skip the 14###########:Text and boolean AND queries
Other things I noticed:
- lots of queries for books, articles, movies, tv, mp3s, and porn (in
- lots of "building up" searches (and these are all marked full_text), for
achevments of h
achevments of he
achevments of hell
achevments of helle
achevments of hellen
achevments of hellen k
achevments of hellen k
achevments of hellen kell
achevments of hellen kelle
achevments of hellen keller
- reasonable-looking ~ queries don't work:
intitle:George~ intitle:Washin~ gives 0 results
intitle:Washington intitle:George gives 279 results
Finally, I did see a bunch of typos, but I didn't try to quantify them
because I was digging into all of these other interesting patterns.
Have a good weekend.
Software Engineer, Discovery
Wikimedia-search mailing list