With anything like this, I would love to look at the raw data to get an intuitive feel for the phenomenon.
For example, the word "surpass" was used 1.47 times per million in the pre-2022 dataset and 3.53 times per million in the post-2022 dataset. That's 16 occurrences in 10.92M words and 41 occurrences in 11.63M words, respectively. That's a low enough number that I could just read through every occurrence and see how it feels. In this case I can't because the authors very understandably couldn't publish the whole dataset for copyright reasons. And replicating the analysis from scratch is a bit too much to do just for curiosity's sake. :)
I often find drilling to the raw data like this to be useful. It can't prove anything, but it can help formulate a bunch of alternative explanations, and then I can start to think how could I possibly tell which of the explanations is the best.
What are the competing explanations here? Perhaps the overall usage rate has increased. Or maybe there was just one or few guests who really like that word. Or perhaps a topic was discussed where it would naturally come up more. Or maybe some of these podcasts are not quite as unscripted, and ChatGPT was directly responsible for the increase. These are some alternative explanations I could think of without seeing the raw data, but there could easily be more alternative explanations that would immediately come to mind upon seeing the raw data.
For example, the word "surpass" was used 1.47 times per million in the pre-2022 dataset and 3.53 times per million in the post-2022 dataset. That's 16 occurrences in 10.92M words and 41 occurrences in 11.63M words, respectively. That's a low enough number that I could just read through every occurrence and see how it feels. In this case I can't because the authors very understandably couldn't publish the whole dataset for copyright reasons. And replicating the analysis from scratch is a bit too much to do just for curiosity's sake. :)
I often find drilling to the raw data like this to be useful. It can't prove anything, but it can help formulate a bunch of alternative explanations, and then I can start to think how could I possibly tell which of the explanations is the best.
What are the competing explanations here? Perhaps the overall usage rate has increased. Or maybe there was just one or few guests who really like that word. Or perhaps a topic was discussed where it would naturally come up more. Or maybe some of these podcasts are not quite as unscripted, and ChatGPT was directly responsible for the increase. These are some alternative explanations I could think of without seeing the raw data, but there could easily be more alternative explanations that would immediately come to mind upon seeing the raw data.