When the two genders are speaking to each other, they try to meet in the middle: “Males use uh about 14 percent less often when talking with a female rather than a male, and females use uh about about 20 percent more often when talking with a male rather than a female,” Liberman writes. (There’s not nearly as much accommodation with “um.”)
What Liberman found, essentially, was that young men speak like old women: “The rate of ‘um’ usage for the younger men is almost the same as the rate of ‘um’ usage for the older women.”
This reminds me of that Twitter study a while back by Tyler Schnoebelen and others, showing that gender-associated speech also has network effects. A summary of the effect, from Ben Zimmer:
They found that even though you can categorize certain words as having a higher male or female probability, it’s easy to find large swaths of Twitter users who go against these trends. By grouping people by their style of usage, they could find, for example, a cluster of authors that is 72 percent male but nonetheless favors the nonstandard spellings that are supposedly a hallmark of “female” language.
Digging deeper, the researchers looked at the social networks that people create on Twitter, making connections by “following” and replying to other users. When you take these networks into account, the gender picture gets even more complex. It turns out that the statistical outliers (men who use language that’s associated with women, and vice versa) are more likely to have networks skewing to the other gender. A man who favors emoticons is more likely to have a high proportion of women in his network. And a woman who frequently mentions the names of sports teams likely has a lot of male friends. The takeaway from Schnoebelen’s presentation is that a simple binary model of gender isn’t sufficient in understanding the welter of language styles in the Twittersphere—or, by implication, in everyday life.
So I’m wondering if people with a lot of male or female friends would pattern like their network with respect to “uh” and “um” as well. It might be harder to do a study of this on Twitter though, because we tend to use disfluencies a lot more rarely and consciously in text than in speech, so it’s not clear that any of the trends would necessarily be the same. And I doubt that network friend gender ratio was recorded for the participants in the corpus that Language Log is using, alas.