The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Demographic Dialectal Variation in Social Media: A Case Study of African-American English
Open accessAbstract
Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language.We conduct a case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter.We propose a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and we verify that this language follows well-known AAE linguistic phenomena.In addition, we analyze the quality of existing language identification and dependency parsing tools on AAE-like text, demonstrating that they perform poorly on such text compared to text associated with white speakers.We also provide an ensemble classifier for language identification which eliminates this disparity and release a new corpus of tweets containing AAE-like language.
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In the citing paper
“Such variations were also observed across racial or ethnic groups”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Blodgett et al., 2016
In the citing paper
“these biases raise additional concerns as they affect several popular tasks, such as user classification, trending topics detection, language identification, or content filtering”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Blodgett et al., 2016
In the citing paper
“by focusing on African-American English dialect, Blodgett et al. (2016) show racial disparity when the textual content produced by users vary from the mainstream or standard languages.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Blodgett et al., 2016
In the citing paper
“Gaps between the purpose and the assumptions under which these datasets and models are built and those under which they are being used can lead to performance disparities, affecting research validity and producing misleading results (e.g., Blodgett et al., 2016; Buolamwini and Gebru, 2018).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Blodgett et al., 2016
Citations
Cites
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008) · Political Analysis · doi:10.1093/pan/mpn018 · Cited by 542 · cited 2×
“This model has broadly similar goals as nonlatent, log-linear generative models of text that condition on document-level covariates (Monroe et al., 2008; Eisenstein et al., 2011a; Taddy, 2013).”
MethodThe social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329
Toward a Description of African American Vernacular English Dialect Regions Using “Black Twitter”
Jones, T. (2015) · American Speech · doi:10.1215/00031283-3442117 · Cited by 144 · cited 5×
“, 2016) and sociolinguistic (Stewart, 2014; Eisenstein, 2015; Jones, 2015) research.”
BackgroundMethodInfluential
Cited by
Sorted by citations
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Sagawa, S., Koh, P. W., Hashimoto, T., & Liang, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08731 · Cited by 1,178
“However, models that are highly accurate on average can still consistently fail on rare and atypical examples (Hovy & Sgaard, 2015; Blodgett et al., 2016; Tatman, 2017; Hashimoto et al., 2018; Duchi et al., 2019).”
BackgroundA Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
“Type Datasets/Benchmarks Multi-Task MMLU [242], SuperGLUE [3], BIG-bench [243], GLUE [244], BBH [243], CUGE [245], ZeroCLUE [246], FewCLUE [247], Blended Skill Talk [248], HELM [249], KLUE-STS [250] Language Understanding CoQA [251], WiC [252], Wikitext103 [253], PG19 [254], LCQMC [255], QQP [256], WinoGender [257], C…”
Gorwa, R., Binns, R., & Katzenbach, C. (2020) · Big Data & Society · doi:10.1177/2053951719897945 · Cited by 1,097
“…used for recommendation, ranking, or blocking, may be more or less favourable to content associated with gender, race and other protected categories (Blodgett et al., 2016; Ekstrand et al, 2018; Zehlike et al., 2017), and thus entrench forms of representational harm against such groups (Barocas et…”
BackgroundLanguage (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050
“Other representational harms Hovy and Søgaard (2015); Blodgett et al. (2016); Bolukbasi et al. (2016b); Hovy and Spruit (2016); Blodgett and O’Connor (2017); Larson (2017); Schnoebelen (2017); Blodgett et al. (2018); Curry and Rieser (2018); Díaz et al. (2018); Dixon et al. (2018); Kiritchenko and…”
BackgroundSelf-citation
Metadata
- Authors
- Blodgett, S. L., Green, L., & O'Connor, B.
- Affiliations
- University of Massachusetts Amherst (US)
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 1119-1130
- OpenAlex
- W2508354372
- Cited by
- 291
- References
- 41
- Citations by year
- 2026: 18 · 2025: 18 · 2024: 20 · 2023: 39 · 2022: 52 · 2021: 61 · 2020: 35 · 2019: 18 · 2018: 15 · 2017: 15
- FWCI
- 8.62
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › Linguistics and Language › Linguistic Variation and Morphology
- Keywords
- African American English, dialect variation, social media language, Twitter, language identification, dependency parsing, ensemble classifiers
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