In a ten-year-old LLOG post ("Gender and tags" 5/9/2004), I cited "the complexity of findings about language and gender, where published claims sometimes contradict one another, and where the various things that 'everybody knows' are not always confirmed by experiment", and warned that
This happens in every area of rational inquiry, but it's especially common in cases where generalizations are associated with strong feelings. In this case, we're talking about the nature of men and women as biological and social categories, and the way individual men and women interact in both private and public spheres. There aren't many topics that generate stronger feelings than this one.
Strong feelings tend to generate contradictory research for two obvious reasons. First, systematic observation sometimes fails to confirm evocative anecdotes, which may be evocative because they resonate with stereotypes rather than because they genuinely confirm experience. Second, even systematic observation can be misleading, if you don't make the right observational distinctions or don't control for the context in an appropriate way. When the emotional stakes are high, people should in principle be especially careful not to overinterpret or overgeneralize their findings, but in practice, the opposite is often true.
I've recently posted several times on sex differences in filled-pause usage: "Fillers: Autism, gender, and age" 7/30/2014; "More on UM and UH" 8/3/2014; "UM UH 3"8/4/2014. This morning's post will try to put this issue into the context of other statistical tendencies in gendered word usage, and to point out the wide range of possible explanations for the differences.
[As in the UM~UH posts, my sources are the Switchboard dataset (collected in 1990-91) and the Fisher dataset (collected in 2003). I've relied on cases where the sex assigned to a speaker by auditors listening to a recorded conversation agreed with the sex given by that nominal speaker when he or she signed up for the study. Most of the small number of disagreements between these indicators are examples where someone other than the designated subject answered the phone and participated in the call. This yields 520 speakers for Switchboard and 10,401 speakers for Fisher.]
And in order to quantify the gender association of individual words, I've used the "weighted log-odds-ratio, informative dirichlet prior" algorithm [Monroe et al., "Fightin Words", Political Analysis 2008] previously discussed in "Obama's favored (and disfavored) SOTU words" 1/29/2014.
Without further ado, here are the 25 most female-associated textual tokens in the Fisher corpus, according to that metric (note of course that a different metric would give a somewhat different list). The four columns are the "word", the frequency per million words for males, the frequency per million words for females, and the weighted log-odds:
It's pretty clear why the women in this collection would use husband, kids, children, daughter, son, home more than the men do. It's also pretty clear why they use gosh 4.4 times as often, and goodness 5.6 times as often — but the obvious explanation is a different one.
It's less clear why women should laugh 60% more often than men do — are women on average happier, or more overtly sociable? Or do men feel constrained not to express positive emotions?
Does the greater female propensity to use mhm, yes, and uh-uh reflect an overall larger proportion of so-called "back-channel" responses? Or just a different choice of back-channel words, compared to (say) the more male-associated yeah, no shit, etc.?
And why in the world should women use and 16% more frequently than men?
Here are the 25 most male-associated lexical tokens in the Fisher corpus, again according to the specified metric:
Again, there's an obvious reason for men to use words like wife more than women do. But it's less clear what the explanation for you and the should be.
The (( and )) tokens are the start and end of regions that transcribers were unsure of or found completely unintelligible — thus male speakers in this collection were unintellible 11% more often than female speakers. The hyphens at the end of i- and th- represent "false starts", words that were cut off and replaced by a self-correction — overall, male speakers in this collection had 45% more false starts than female speakers did (7815 per MW vs. 5478 per MW). This suggests somewhat greater disfluency overall, which would be consistent with uh being a symptom of disfluency in a way that um isn't.
But the same question comes up here as in respect to the different in laughter: Does the apparent sex difference in markers of disfluency really reflect a difference in underlying capabilities, or does it indicate a difference in self-presentation?
We can pose an analogous question again with respect to the well-known gender difference in taboo word usage, strikingly evident in the word clouds for female vs. male Facebook posts in H. Andrew Schwartz et al., "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach", PLoS ONE 2013.
Presumably this reflects a gender difference in how people are socialized to express the emotions and attitudes underlying taboo-word usage, not an intrinsic difference (whether genetic or learned) in how men and women feel and react. Many people will be more open to intrinsic-difference explanations for gendered variation in frequency of laughter or of disfluency markers. But I believe that we should keep an open mind in all of these cases.
Some relevant earlier LLOG posts: