from Hacker News

Evolution of words frequencies in porn

by mazr on 1/30/14, 8:32 AM with 48 comments

• by route66 on 1/30/14, 10:05 AM

  To speak of evolution when your time frame is 2008-2012 is somewhat far fetched. But I believe I see a reassuring trend here: http://porngram.sexualitics.org/?q=erlang%2C+clojure%2C+Ada%...

• by sushirain on 1/30/14, 12:21 PM

  1. It doesn't count word frequencies, but sub-string frequencies. Moreover, if a sub-string appears more than once-per-title, then it is counted more than once. I draw this conclusion by submitting "a,b,c". And from their paper [1]:

     our algorithm strips out dashes and catches any 
     occurrence of the query in the title, for example, 
     'blow' catches 'blowing', 'blowjobs'
  

  This explains the results of these queries: "ada,erlang", "tea,beer". As an alternative they could have used a stemmer [2].

  2. The "slow,fast" and "love,hardcore" trends illustrate an interesting trend. Perhaps towards women or mainstream viewers.

  [1] http://sexualitics.org/wp-content/uploads/2014/01/PORNSTUDIE...

  [2] http://nlp.stanford.edu/IR-book/html/htmledition/stemming-an...

• by easy_rider on 1/30/14, 10:56 AM

  In my first 2 weeks of working at an adult company (as a dev yes, it's sad) one of my tasks was to watch/scan 200+ video's and describe them.. It's true, you run out of inspiration fast. Also I could hint you: the "love" in the titles is probably explained by "love(s) to <insert profanity> ". I don't think I ever used hardcore in a title.

• by probably_wrong on 1/30/14, 12:17 PM

  Traditional professions are still on top:

  http://porngram.sexualitics.org/?q=pizza%2Cdelivery%2Cplumbe...

  I sense a business opportunity there.

• by ozh on 1/30/14, 8:44 AM

  Quite fun!

  Next: provide the porn industry a simple markov chain script to generate probabilistic porn movie titles, and save them all those incredibly tiresome brainstrom sessions they must have to create new titles :)

• by endriju on 1/30/14, 10:34 AM

  http://porngram.sexualitics.org/?q=btc%2Cusd

• by uloweb on 1/30/14, 10:35 AM

  http://porngram.sexualitics.org/?q=iphone%2Candroid

• by mapleoin on 1/30/14, 11:00 AM

  http://porngram.sexualitics.org/?q=hipster

• by edoloughlin on 1/30/14, 11:24 AM

  Strange: http://porngram.sexualitics.org/?q=tea,wine,beer,coffee

• by thinker on 1/30/14, 2:46 PM

  I wonder if we'll soon see an Upworthy for Porn: "You will never believe what this girl did to pay her rent"

• by guybrushT on 1/30/14, 5:57 PM

  Very interesting to see the dataset being made available. Whenever I want to do this kind of analysis, I always stumble at 'how to get the data?'. In their paper, it is mentioned that "We created a dedicated computer program to carry out the navigation and data collection tasks required to gather the metadata for all available videos...". I would love to see this program. More broadly, can anyone help me with best resources (pref python) where one can learn to crawl/scrape this type of information?

• by stcredzero on 1/30/14, 9:47 AM

  words frequency

  Is the title a British thing? Like maths vs. math?

• by graylights on 1/30/14, 9:37 PM

  So I did gay vs lesbian and I was confused why there was a big spike in 2010 for gay that has since dropped off. Is this an anomaly in their sampling?

  Also Obama's numbers have really dropped compared to Bush: http://porngram.sexualitics.org/?q=bush%2Cobama

• by 6d0debc071 on 1/30/14, 3:01 PM

  I suppose we may as well do the obvious ones

  http://porngram.sexualitics.org/?q=BDSM%2Ctorture%2Cpain%2Cr...

  :/

• by misnome on 1/30/14, 11:46 AM

  Kind of interesting, but really needs statistical error bounds

• by himal on 1/30/14, 11:20 AM

  http://porngram.sexualitics.org/?q=HIV%2CAIDS

• by jpswade on 1/30/14, 9:39 AM

  http://porngram.sexualitics.org/?q=cup

• by bobowzki on 1/30/14, 7:52 PM

  "sister" :-)