Joke Breakdown

Redditor [Ninkaka][] has a great [breakdown][] of a [joke by Rob O’Reilly][joke]:

> It’s all about the placement of the beats and the accompanying laughter. I haven’t seen this joke performed, but it’s pretty simple to tell how the crowd would react.

1. “I got mugged once, but I’m poor, so we were both disappointed.” *Scattered laughter. Audience expectant.*
2. “He was like, ‘You have 83 cents and a Sony Walkmen [sic]? What do you do?’ I was like ‘Oh, I’m a comedian.'” *More laughter. Comedian might do a little wry laugh to signal that there’s more.*
3. “He’s like ‘Oh, I used to be a comedian, you should try mugging.'” *One or two guys spit take in the back of the crowd.*
4. “I was like, ‘Thanks Carlos Mencia!'” *Longest laughter yet, might taper off sharply.*
5. “No I’m kidding. Carlos Mencia would never steal from another comedian.” *Brings it back and ends on a high. This is the actual joke the comedian wanted you to laugh the hardest at.*


Talking Shit by the Numbers

While working on a revision of the illustrations for the “Talking Shit in Rayne” essay, I considered for a time the use of a three-dimensional graph to represent how changes in genre interacted with changes in the interactional order. One axis was, of course, time. The second axis of the original graph was a kind of loose approximation of — okay a complete approximation of the degree of interaction occurring between the speaker and the audience.

In order not to have a fake 3D graph, of which there are plenty, I searched for a third axis of information that I could use. I fell upon the idea of trying to quantify the amount of interaction within a given text (semantic interaction, if you will) — as a way to explore the amount of interaction that a text created at the pragmatic level. My initial idea was to count the number of lines of direct discourse and then to divide by the total number of lines in a given text. That yielded the following results:

Text Direct Discourse Lines Total Lines Percentage
1.1 0 8 .55
1.2 6 11 .55
1.3 14 24 .58
1.4 12 14 .86
1.5 11 25 .44
1.6 25 34 .74
1.7 28 55 .51
1.8 41 81 .51
1.13 2 28 .07
1.14 20 52 .38
1.15 6 19 .32
1.17 8 21 .38
1.18 2 22 .09

A couple of notes first:

  • Texts 1.1 – 1.4 are toasts.
  • Texts 1.5 – 1.8 are jokes.
  • Texts 1.13-1.18 are memorates, first-person accounts of encounters with the supernatural.

A couple of things stand out here:

  • First, this kind of pattern seeking really wants more texts, and so there really isn’t enough data here.
  • Second, I don’t even have all the data for all the texts I have — I was missing some transcripts when I did this.
  • Third, despite all these qualifications, there are still some interesting patterns here:
    • It’s hard to ignore the clustering of the first three texts, all of which are toasts, with values of .55-.58.
    • It’s also hard to ignore the pair of jokes with a similar mirroring of percentages of direct discourse, despite enormous differences in length.
    • The same can be said about the cluster of memorates, 14-17.

Another thing to note is that having to count lines of direct discourse was a bit more challenging than it seemed, though I think I made reliable distinctions. Just as importantly, however, was the fact that there were a few instances of indirect discourse that gave me pause. But very few. So few that now I want to go back and examine those instances more closely to see if I can’t discern any patterns of why it occurs at all.

Semantic Bleaching

We can report what someone did, but it is not what they did. However, we can repeat what someone said, and it is, ostensibly, what they said.

A report is incongruent. A repetition is congruent.

— a handwritten note at the bottom of a log sheet for the Babineaux recording