I have seen some fascinating statistics about what HTML markup is actually in use on the web.
There is Web Authoring Statistics from Google in 2005, and MAMA from Opera in 2008.
Have there been any similar studies more recently than this?
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I have seen some fascinating statistics about what HTML markup is actually in use on the web. There is Web Authoring Statistics from Google in 2005, and MAMA from Opera in 2008. Have there been any similar studies more recently than this? |
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This dates from January 2011, but isn't that widespread: http://try.powermapper.com/demo/statsversions.aspx |
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I recently needed to come up with some HTML element statistics. I downloaded a 600k page web corpus from 2009 and got the statistics below for number of uses of the tags in that corpus capped at 1000. a 39923975 td 29707781 br 17266040 div 15644761 tr 15340012 img 13539744 option 9319944 li 8531300 span 7573226 table 5999791 font 5329688 b 4419975 p 3762731 input 3413040 script 3368258 strong 1855448 meta 1809099 link 1262896 ul 1246231 hr 712094 form 703914 dd 521143 i 503215 center 483503 h2 477122 title 446862 body 445094 head 436058 html 435397 h3 400073 th 393534 em 363613 dt 363085 label 342376 h1 318542 select 311449 style 292716 tbody 275069 nobr 251825 small 242097 noscript 227474 u 227338 area 221226 param 204798 h4 162877 dl 142403 iframe 126827 o 86064 sup 72331 h5 64141 fieldset 55447 textarea 54044 object 53226 embed 51864 cite 47406 scr 47050 tt 45748 big 44210 optgroup 43329 blockquote 42869 base 42147 map 41401 col 40191 wbr 36995 legend 32738 d 29939 ol 28879 thead 27083 spacer 26848 pre 26118 h6 24766 s 24510 button 23417 code 21190 rdf 20960 abbr 20356 acronym 20186 w 16818 noindex 14038 dfn 12974 marquee 11350 v 11165 strike 10522 address 9725 description 9058 sc 8677 caption 8633 st1 8374 colgroup 8191 item 8090 pubdate 7160 layer 6156 sub 5634 ins 5433 category 5106 guid 4936 document 4678 del 4639 frame 4548 dc 4323 image 4306 var 3729 variable 3292 tfoot 3282 x 3120 xs 2963 zeroboard 2870 js 2825 ilayer 2823 frameset 2738 media 2669 rx 2658 author 2581 c 2563 h 2227 ifr 2213 xml 2134 m 2007 csobj 1972 n 1948 set 1872 l 1870 permits 1848 samp 1767 menuitem 1738 requires 1732 noframes 1714 z 1686 this 1655 q 1638 t 1629 f 1622 content 1612 license 1533 left 1501 srch 1488 kbd 1482 sfels 1466 hs 1438 para 1434 comments 1394 changeimages 1393 list 1362 actinic 1358 csaction 1352 skype 1338 myarr 1337 index 1283 blip 1279 scri 1262 mlp 1205 e 1199 url 1199 basefont 1167 channel 1153 if 1152 u1 1151 g 1137 xsl 1137 literal 1108 rss 1105 itemtemplate 1053 j 1036 blink 1022 len 1018 id 1001 align 1000 Here are selected tags from HTML5 and others that are less frequent. bdo 425 quote 123 time 61 mark 23 bdi 5 MethodologyI didn't try anything particularly fancy. I just took the whole corpus (2.4GB gzipped) and ran it through perl
where the perl script does
I then aggregated the output using a python script.
CaveatsThis script uses a very simple heuristic to look for tags, but will be confused by a '<' followed by a word in some contexts:
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<header>,<footer>and<nav>were chosen based on the most popular class names found in that Google study. I'm not aware of anything more recent than Opera's 2008 study though. – John Catterfeld Mar 31 '11 at 14:04