[HN Gopher] A data analysis of speeches at the Oscars
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       A data analysis of speeches at the Oscars
        
       Author : PourquoiPas
       Score  : 99 points
       Date   : 2025-02-27 12:23 UTC (10 hours ago)
        
 (HTM) web link (stephenfollows.com)
 (TXT) w3m dump (stephenfollows.com)
        
       | ks2048 wrote:
       | Cool. As mentioned at the end, the oscars has site,
       | 
       | https://aaspeechesdb.oscars.org/
       | 
       | "This database contains more than 1,500 transcripts of onstage
       | acceptance speeches given by Academy Award winners and
       | acceptors."
        
       | ngriffiths wrote:
       | Very cool! I think mentions per word could be a good metric for
       | some of these, otherwise a main takeaway is just "everyone crams
       | in more stuff now."
        
       | abound wrote:
       | > I have added more details in the notes at the end of the
       | article to explain how I found God, but for now, just have faith
       | that I did.
        
         | noddleah wrote:
         | > God cannot give them their next job - Steven Spielberg can
        
       | 6stringmerc wrote:
       | Great dive into the nature of the speeches and some interesting
       | tidbits.
       | 
       | Counting the instances of the word "amazing" would be a fun
       | follow up. That was our drinking game cue word. We inevitably
       | stopped at some point because...poisoning became likely.
        
       | lblume wrote:
       | How exactly was the data evaluated? I would assume that manually
       | checking every speech would be too labor-intensive?
        
         | ikanreed wrote:
         | Not really? It's a lot of work, a multi-week project, but
         | reading a couple hundred word speech can be done in 5 minutes,
         | following a checklist in hand, probably 10 minutes. Times 12
         | categories, and 80 years of history, that's a lot of time 160
         | hours, a working month. A lot of effort but humanely doable.
        
           | lblume wrote:
           | Ok, obviously it's _doable_, but is it worth it? Using LLMs
           | for this purpose would have been significantly cheaper,
           | easier and with the right configuration just as reliable.
           | Once the setup works, you could extend the analysis to all
           | kinds of other interesting branches without having to look at
           | a single speech by hand.
           | 
           | I would even go so far as to say that _not_ using LLMs for
           | this task would be fairly odd, unless I'm missing something
           | or the author really enjoys a month of manually classifying
           | documents to write an interesting and well-written but not
           | exceedingly outstanding article.
        
           | LeifCarrotson wrote:
           | That's true, but assumes you have the checklist of what data
           | to analyze in hand when you start out. If you only decide
           | after the fact which familial relationships have interesting
           | trends, you'd have to start over again. It seems more
           | reasonable to start by transcribing everything to text,
           | annotating that text, and then running a lot of scripting to
           | automatically query that data.
        
       | cruelmathlord wrote:
       | this is a very interesting project. I like when technology is
       | used to analyze cultural or political events
        
       | ryanmcbride wrote:
       | >In 2010, the Academy sought to combat this verbosity with a new
       | 45-second rule. In response, some winners sped through their
       | acknowledgements, while others used humour or emotion to buy
       | extra time before the music signalled them off. Occasionally, the
       | orchestra was ignored entirely, with speeches like Adrien Brody's
       | 2003 win for The Pianist running well over the limit.
       | 
       | Brody so clairvoyant that he can ignore limits that don't even
       | exist yet.
        
       | mewse-hn wrote:
       | This was an enjoyable article but the conclusion where he finds
       | the most thanked woman in oscar speeches and gets a response from
       | her puts it over the top. Amazing.
        
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       (page generated 2025-02-27 23:00 UTC)