What's most likely is that this IS a factor for a portion of our more recent data, but not enough to affect the main finding here (across a range of words and decades). Tyvm for the interest in this!!

Cool article that's relevant: www.newyorker.com/magazine/200...

Very valid q! It's likely a confound for some of the more recent data, but not most. (i) lots of the "speeches" are in fact shorter replies and remarks; (ii) the professionalization of speech-writing evolved over the 20th century, but we see no change in speakers' adoption behavior over time.

These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)

Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)

Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)

We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)

Our new paper in #PNAS (bit.ly/4fcWfma) presents a surprising finding—when words change meaning, older speakers rapidly adopt the new usage; inter-generational differences are often minor.

w/ Michelle Yang, ‪@sivareddyg.bsky.social‬ , @msonderegger.bsky.social‬ and @dallascard.bsky.social‬👇(1/12)

These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)

Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)

Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)

We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)