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

  • Nicole Holliday ORCID logo EMAIL logo and Marie Tano ORCID logo
Published/Copyright: May 11, 2021
Linguistics Vanguard
From the journal Linguistics Vanguard Volume 7 Issue 1

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.

Keywords: African American English; language ideologies; online language; perception; sociolinguistics
Corresponding author: Nicole Holliday, Linguistics, University of Pennsylvania, Philadelphia, PA, USA, E-mail

Appendix A: Stimuli tweets

  1. Selected tweets for white UAAE avatar

  1. Selected tweets for black GAAE avatar

  1. Selected tweets for black MUSE avatar

  1. Selected tweets for white GAAE avatar

  1. Selected tweets for white MUSE avatar

  1. 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

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/lingvan-2020-0095
Keywords for this article
African American English; language ideologies; online language; perception; sociolinguistics

Articles in the same Issue

  1. Editorial Note
  2. Editorial note
  3. Phonetics & Phonology
  4. Fast Track: fast (nearly) automatic formant-tracking using Praat
  5. Acoustic investigation of anticipatory vowel nasalization in a Caribbean and a non-Caribbean dialect of Spanish
  6. Evidence against a link between learning phonotactics and learning phonological alternations
  7. The extent and degree of utterance-final word lengthening in spontaneous speech from 10 languages
  8. Morphology & Syntax
  9. Brand names as multimodal constructions
  10. NP-internal structure and the distribution of adjectives in Mə̀dʉ́mbὰ
  11. A quantitative investigation of the ellipsis of English relativizers
  12. Positional dependency in Murrinhpatha: expanding the typology of non-canonical morphotactics
  13. Semantics & Pragmatics
  14. Multifactorial Information Management (MIM): summing up the emerging alternative to Information Structure
  15. Language Documentation & Typology
  16. Current trends in grammar writing
  17. Psycholinguistics & Neurolinguistics
  18. Experimental filler design influences error correction rates in a word restoration paradigm
  19. Phonological and morphological roles modulate the perception of consonant variants
  20. Language Acquisition and Language Learning
  21. Sounds like a dynamic system: a unifying approach to Language
  22. Sociolinguistics and Anthropological Linguistics
  23. Using hidden Markov models to find discrete targets in continuous sociophonetic data
  24. “It’s a Whole Vibe”: testing evaluations of grammatical and ungrammatical AAE on Twitter
  25. The sociolinguistics of /l/ in Manchester
  26. Computational & Corpus Linguistics
  27. An empirical study on the contribution of formal and semantic features to the grammatical gender of nouns
  28. A computational construction grammar approach to semantic frame extraction
  29. The “negative end” of change in grammar: terminology, concepts and causes
  30. In order that – a data-driven study of symptoms and causes of obsolescence
  31. Cognitive Linguistics
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  33. Iconicity ratings really do measure iconicity, and they open a new window onto the nature of language
  34. Repetition in Mandarin-speaking children’s dialogs: its distribution and structural dimensions
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