Accessible Unlicensed Requires Authentication Published by De Gruyter Mouton May 11, 2021

“It’s a Whole Vibe”: testing evaluations of grammatical and ungrammatical AAE on Twitter

Nicole Holliday ORCID logo and Marie Tano ORCID logo
From the journal Linguistics Vanguard

Abstract

This study focuses on listener perceptions of African American English (AAE) on Twitter, examining both grammatical and ungrammatical usages, as well as how these perceptions may be affected by the race of the speaker and rater. We conducted an experimental survey designed to address the following questions: 1. Does avatar race affect perception of the grammaticality of AAE? 2. Are differences between grammatical and ungrammatical AAE discernible to naive raters of different races? 3. How are various social attributes evaluated for avatars of different races and different linguistic varieties? Results indicate that participants generally do not downgrade avatars who use grammatical AAE on ratings of grammaticality or personal characteristics. However, participants of all races disprefer ungrammatical uses of AAE, with black raters being especially sensitive to ungrammatical AAE. These findings have implications for sociolinguistics in that they demonstrate that participants across racial backgrounds may differentiate grammatical versus ungrammatical AAE online, and that contrary to expectations based on previous literature, AAE is not universally downgraded in these contexts. However, results also indicate that the use of AAE still negatively impacts listeners’ perceptions of speakers as educated, demonstrating that some widespread biases against AAE-speakers persist in an online context.


Corresponding author: Nicole Holliday, Linguistics, University of Pennsylvania, Philadelphia, PA, USA, E-mail

Appendix A: Stimuli tweets

  1. 1.

    Selected tweets for white UAAE avatar

  1. 2.

    Selected tweets for black GAAE avatar

  1. 3.

    Selected tweets for black MUSE avatar

  1. 4.

    Selected tweets for white GAAE avatar

  1. 5.

    Selected tweets for white MUSE avatar

  1. 6.

    Selected tweets for black UAAE avatar

Appendix B: Statistical summary

1a. Personality trait ratings regression model.

Educated Friendly Funny Rude
(Intercept) 6.13 (0.27)*** 4.84 (0.26)*** 4.39 (0.29)*** 1.52 (0.21)***
Participant Race = Black 0.55 (0.36) −0.12 (0.35) 0.86 (0.40)* −0.32 (0.29)
Avatar grammar = GAAE −0.40 (0.37) 0.21 (0.36) −0.57 (0.42) −0.13 (0.30)
Avatar grammar = UAAE −2.12 (0.37)*** −1.66 (0.36)*** −0.73 (0.42) 0.57 (0.30)
Avatar Race = White −1.61 (0.37)*** −1.64 (0.36)*** −1.41 (0.42)*** 0.53 (0.30)
ParticipantRace = Black*Grammar = GAAE 1.22 (0.51)* −0.10 (0.50) −0.14 (0.57) −0.20 (0.42)
ParticipantRace = Black*Grammar = UAAE −1.69 (0.51)** −1.29 (0.50)* −2.27 (0.57)*** 0.02 (0.42)
ParticipantRace = Black*AvatarRace = White −0.64 (0.51) −1.15 (0.50)* −1.48 (0.57)** −0.05 (0.42)
Grammar = GAEE*AvatarRace = White 0.46 (0.53) 0.13 (0.52) 0.41 (0.59) −0.27 (0.43)
Grammar = UAEE*AvatarRace = White 0.64 (0.53) 0.99 (0.52) 0.31 (0.59) 0.08 (0.43)
ParticipantRace = Black* Grammar = GAEE*Avatar_Race = White −0.44 (0.73) 0.69 (0.71) 1.10 (0.81) 0.42 (0.59)
ParticipantRace = Black* Grammar = UAEE*Avatar_Race = White 1.38 (0.73) 1.66 (0.71)* 2.04 (0.81)* −0.03 (0.59)
R 2 0.26 0.20 0.12 0.05
Adj. R 2 0.26 0.19 0.11 0.04
Num. obs. 1,152 1,152 1,152 1,152
RMSE 2.51 2.44 2.79 2.03

  1. p < 0.001***, p < 0.01**, p < 0.05*.

1b. Personality trait model ANOVAs.

Term df Educated Friendly Funny Rude
Participant race 1 2.5 28.1*** 1.0 7.9**
Avatar variety 2 129.7*** 58.0*** 22.7*** 18.5***
Avatar race 1 91.6*** 103.6*** 72.7*** 18.3***
Participant race* Avatar variety 2 15.2*** 2.0 9.2*** 0.0
Participant race* Avatar race 1 1.2 1.6 1.7 0.1
Avatar Variety*Avatar race 2 8.3*** 15.1*** 6.4*** 0.1
Participant race* Avatar Variety*Avatar race 2 3.4* 2.8* 3.2* 0.4
Residuals 1,140

  1. p < 0.001***, p < 0.01**, p < 0.05*.

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Received: 2020-06-12
Accepted: 2021-01-18
Published Online: 2021-05-11

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