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Is “Huh?” a Universal Word? Conversational Infrastructure and the Convergent Evolution of Linguistic Items

Overview of attention for article published in PLoS ONE, November 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#25 of 130,131)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
71 news outlets
blogs
29 blogs
twitter
796 tweeters
facebook
92 Facebook pages
wikipedia
2 Wikipedia pages
googleplus
56 Google+ users
reddit
8 Redditors
video
1 video uploader

Citations

dimensions_citation
47 Dimensions

Readers on

mendeley
166 Mendeley
citeulike
2 CiteULike
Title
Is “Huh?” a Universal Word? Conversational Infrastructure and the Convergent Evolution of Linguistic Items
Published in
PLoS ONE, November 2013
DOI 10.1371/journal.pone.0078273
Pubmed ID
Authors

Mark Dingemanse, Francisco Torreira, N. J. Enfield, Johan J. Bolhuis

Abstract

A word like Huh?--used as a repair initiator when, for example, one has not clearly heard what someone just said--is found in roughly the same form and function in spoken languages across the globe. We investigate it in naturally occurring conversations in ten languages and present evidence and arguments for two distinct claims: that Huh? is universal, and that it is a word. In support of the first, we show that the similarities in form and function of this interjection across languages are much greater than expected by chance. In support of the second claim we show that it is a lexical, conventionalised form that has to be learnt, unlike grunts or emotional cries. We discuss possible reasons for the cross-linguistic similarity and propose an account in terms of convergent evolution. Huh? is a universal word not because it is innate but because it is shaped by selective pressures in an interactional environment that all languages share: that of other-initiated repair. Our proposal enhances evolutionary models of language change by suggesting that conversational infrastructure can drive the convergent cultural evolution of linguistic items.

Twitter Demographics

The data shown below were collected from the profiles of 796 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 3%
United States 5 3%
France 3 2%
Switzerland 2 1%
Japan 2 1%
Germany 1 <1%
Italy 1 <1%
South Africa 1 <1%
Brazil 1 <1%
Other 5 3%
Unknown 140 84%

Demographic breakdown

Readers by professional status Count As %
Student > Master 34 20%
Researcher 33 20%
Student > Ph. D. Student 32 19%
Student > Bachelor 12 7%
Other 12 7%
Other 43 26%
Readers by discipline Count As %
Linguistics 42 25%
Agricultural and Biological Sciences 23 14%
Psychology 22 13%
Computer Science 12 7%
Social Sciences 12 7%
Other 55 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1516. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 September 2018.
All research outputs
#1,084
of 11,817,084 outputs
Outputs from PLoS ONE
#25
of 130,131 outputs
Outputs of similar age
#15
of 162,306 outputs
Outputs of similar age from PLoS ONE
#2
of 4,155 outputs
Altmetric has tracked 11,817,084 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 130,131 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 162,306 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 4,155 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.