---
title: "Finding Microaggressions in the Wild: A Case for Locating — paper by Breitfeller et al. (2019)"
description: "The work offers the first corpus, typology and model for this elusive form of offensive language, extending abuse… 45 references, cited by 76 papers here."
authors: ["Luke Breitfeller", "Emily Ahn", "David Jurgens", "Yulia Tsvetkov"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1176/"
doi: "10.18653/v1/d19-1176"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/breitfeller-2019-finding-microaggressions-wild-case-locating/"
---
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# Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts

**Authors:** Luke Breitfeller, Emily Ahn, [David Jurgens](https://md.research.elamur.ai/free/en/authors/david-jurgens/) and [Yulia Tsvetkov](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/) · **Year:** 2019 · **Venue:** [Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-1176/) · [PDF](https://aclanthology.org/D19-1176.pdf) · [DOI 10.18653/v1/d19-1176](https://doi.org/10.18653/v1/d19-1176) · [OpenAlex](https://openalex.org/W2970395295)

**Citations:** 112 citations (OpenAlex, 2026-10-14) · [76](https://md.research.elamur.ai/free/en/papers/breitfeller-2019-finding-microaggressions-wild-case-locating/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Microaggressions are subtle expressions of bias against marginalized groups. Toxicity detectors miss them, and sentiment tools may even score them as positive. Computational study has been held back by the lack of real-world data and clear annotation criteria. The authors address this with three contributions.

First, they collected SelfMA, a set of self-reported incidents from microaggressions.com. From it, they derived a four-theme typology: Attributive, Institutionalized, Teaming and Othering. Three annotators reached moderate agreement on this scheme, and a linear SVM could tell the themes apart.

Second, they built an active-learning crowdsourcing pipeline to find gender-based microaggressions in Reddit comments. Instead of asking workers to judge intent, it uses two proxies: perceived offensiveness and the gap in offensiveness ratings between annotator genders. Both signals raised the rate of surfacing microaggressions, and each found different kinds.

Third, they showed that self-reported and naturally occurring microaggressions differ substantially in type. The work offers the first corpus, typology and model for this elusive form of offensive language, extending abuse detection beyond overt hate speech.

## Key points
- SelfMA contains 2,934 posts from the Tumblr site microaggressions.com, covering gender (1,314), race (1,278), sexuality (461) and religion (88), among other categories.
- Three annotators triple-labeled 200 posts: 183 had majority agreement and 17 were adjudicated. Another 1,100 posts were singly annotated, giving 1,300 posts in total. Twenty-six purely overt aggressions were removed.
- Theme classification used one-versus-rest linear SVMs trained on 1,000 posts and tested on 300. Features included word unigrams/bigrams (8,853 features), 2015 LIWC categories, sentiment lexicons and a 40-topic LDA model.
- The Reddit study targets gender-based microaggressions, the largest category in SelfMA. Candidates are selected using classifiers for predicted offensiveness and predicted gender-based rating discrepancy, and these surface posts that offensiveness alone would miss.
- All data and code are released publicly (https://bit.ly/2lVv3BG), and the study was IRB-approved.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 365 works · 158,048 citations
- [University of Michigan](https://md.research.elamur.ai/free/en/universities/university-of-michigan/) — University, Ann Arbor, United States · 36 works · 12,181 citations

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## Cite

Cite as: Luke Breitfeller, Emily Ahn, David Jurgens and Yulia Tsvetkov. 2019. Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{breitfeller2019finding,
  title = {{Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts}},
  author = {Luke Breitfeller, Emily Ahn, David Jurgens and Yulia Tsvetkov},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1176},
  url = {https://doi.org/10.18653/v1/d19-1176}
}
```

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43. Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017. Understanding Abuse: A Typology of Abusive Language Detection Subtasks. pages 78–84. [our card](https://md.research.elamur.ai/free/en/papers/waseem-2017-understanding-abuse-typology-abusive-language/)
44. Gloria Wong, Annie O Derthick, EJR David, Anne Saw, and Sumie Okazaki. 2014. The what, the why, and the how: A review of racial microaggressions research in psychology. Race and social problems, 6(2):181–200. [find](https://scholar.google.com/scholar?q=Gloria+Wong%2C+Annie+O+Derthick%2C+EJR+David%2C+Anne+Saw%2C+and+Sumie+Okazaki%2E+2014%2E+The+what%2C+the+why%2C+and+the+how%3A+A+review+of+racial+microaggressions+research+in+psychology%2E+Race+and+social+problems%2C+6%282%29%3A)
45. Wei Xu, Alan Ritter, and Ralph Grishman. 2013. Gathering and generating paraphrases from twitter with application to normalization. In Proceedings of the [find](https://scholar.google.com/scholar?q=Wei+Xu%2C+Alan+Ritter%2C+and+Ralph+Grishman%2E+2013%2E+Gathering+and+generating+paraphrases+from+twitter+with+application+to+normalization%2E+In+Proceedings+of+the)

*Updated 24 October 2026*