Cited by 817Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AISentiment Analysis and Opinion Mining

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

The Risk of Racial Bias in Hate Speech Detection

Open access

Sap, Maarten, Card, Dallas, Gabriel, Saadia, Choi, Yejin, Smith, Noah A.·2019·Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

DOI 10.18653/v1/p19-1163

Open this paper in CoChat — free account Open PDF

Abstract

We investigate how annotators' insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations.We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets.Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others.Finally, we propose dialect and race priming as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet's dialect they are significantly less likely to label the tweet as offensive.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.18653/v1/p19-1163

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Hierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 5,022
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Attention-based LSTM for Aspect-level Sentiment Classification
Wang, Y., Huang, M., zhu, X., & Zhao, L. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1058 · Cited by 2,450
The Impact of Social Media on Children, Adolescents, and Families
O'Keeffe, G. S., Clarke-Pearson, K., & Council on Communications and Media (2011) · Pediatrics · doi:10.1542/peds.2011-0054 · Cited by 1,987 · cited 1×
“Toxic language (e.g., hate speech, abusive speech, or other offensive speech) primarily targets members of minority groups and can catalyze real-life violence towards them (O’Keeffe et al., 2011; Cleland, 2014; Mozur, 2018).”
Background
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919
Internal and external motivation to respond without prejudice.
Plant, E. A., & Devine, P. G. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.75.3.811 · Cited by 1,689
A Survey on Hate Speech Detection using Natural Language Processing
Schmidt, A., & Wiegand, M. (2017) · SocialNLP@EACL · doi:10.18653/v1/w17-1101 · Cited by 1,478
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
Zhang, Z., Robinson, D., & Tepper, J. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93417-4_48 · Cited by 676

Show all 40 references

Research works printed but not linked: 2

E. Ashby Plant and Patricia G. Devine. 1998. Inter- nal and external motivation to respond without prej- udice. J. Pers. Soc. Psychol., 75(3):811-832.
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy. 2016. Hi- erarchical attention networks for document classifi- cation. In NAACL.

Sorted by citations

Items 601–700 of 808

Latent Hatred: A Benchmark for Understanding Implicit Hate SpeechPreprint
ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., Choudhury, M. D., & Yang, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05322 · Cited by 28
Detecting Cross-Geographic Biases in Toxicity Modeling on Social MediaPreprint
Ghosh, S., Baker, D., Jurgens, D., & Prabhakaran, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06999 · Cited by 2
Machines Do Not Decide Hate Speech: Machine Learning, Power, and the Intersectional ApproachPreprint
Kim, J. Y. (2021) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/chvgp · Cited by 2
The 7th Workshop on Online Abuse and Harms (WOAH)
(2023) · doi:10.18653/v1/2023.woah-1 · Cited by 2
Hate speech and offensive language detection using transfer learning approaches
Mozafari, M. (2021) · theses.fr (ABES) · doi:10.70675/9fc685efz2a9cz4fa0z9041z5001b09e8195 · Cited by 2
Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models
Weld, G., Ayton, E., Althoff, T., & Glenski, M. (2021) · Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda (NLP4IF) · doi:10.18653/v1/2021.nlp4if-1.5 · Cited by 2
Protecting marginalized communities by mitigating discrimination in toxic language detection
Faal, F., Schmitt, K., & Yu, J. Y. (2021) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas52410.2021.9629201 · Cited by 1
Bubble reachers and uncivil discourse in polarized online public sphere
Kobellarz, J. K., Broćić, M., Silver, D., & Silva, T. H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304564 · Cited by 5
fl-IRT-ing with Psychometrics to Improve NLP Bias Measurement
Bachmann, D., van der Wal, O., Chvojka, E., Zuidema, W. H., van Maanen, L., & Schulz, K. (2024) · Minds and Machines · doi:10.1007/s11023-024-09695-9 · Cited by 1
Evaluation of Gender Bias in Amharic Word Embedding Model
Zenebe, B., Gizaw, S., & Abgaz, Y. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70242-6_17 · Cited by 1
Court Case Dataset for Japanese Online Offensive Language Detection
Hisada, S., Wakamiya, S., & Aramaki, E. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1598 · Cited by 2
LEAP: LLM-Powered End-to-End Automatic Library for Processing Social Science Queries on Unstructured Data
Hu, C., Peters, A., & Kang, D. D. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3705829.3705843 · Cited by 9
Handling data scarcity through data augmentation for detecting offensive speech
Sekkate, S., Chebbi, S., Adib, A., & Jebara, S. B. (2025) · Annals of Telecommunications · doi:10.1007/s12243-025-01072-6 · Cited by 1
The Real Cancel Culture: Racial Bias and Injustice on Social Media
Painter, D. L., Bown, F., & Vielot, K. (2025) · Howard Journal of Communications · doi:10.1080/10646175.2025.2492114 · Cited by 2
Examining inferred author and textual correlates of harmful language annotation
Korre, K., Yenikent, S., Basile, A., Spallaccia, B., Franco-Salvador, M., & Barrón‐Cedeño, A. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09822-7 · Cited by 3
Large Language Model Annotation Bias in Hate Speech Detection
Okpala, E., & Cheng, L. (2025) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v19i1.35879 · Cited by 4
Classifying Hate: Legal and Ethical Evaluations of ML-Assisted Hate Crime Classification and Estimation in Sweden
Sargeant, H., Waldetoft, H., & Magnusson, M. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732016 · Cited by 3
An Innovative Speech Translation System for Accurate Idiomatic and Contextual Interpretation
Koreddi, V., Yasmin, N., Divya, N. S., & Somanath, N. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-5265-5_48 · Cited by 1
Generative artificial intelligence and natural language processing in behavior analysis
Twyman, J. S., & Layng, T. V. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-22358-7.00016-2 · Cited by 1
Projecting Culture Through Hashtag Activism: The #IAmABlackMan Challenge on Instagram
Edrington, C. L., Mortensen, T. M., & Ezenna, O. (2025) · Social Media + Society · doi:10.1177/20563051251382445 · Cited by 2
Bias Amplification in AI Models Trained on Low-Resource Data: A Sociolinguistic Audit
Udeze, N. C. (2025) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v4i12.1075 · Cited by 1
Towards a Fuzzy MCDM-Based Framework for Multi-Metric Bias Auditing in Generative AI
Ravikumar, R., Rajan, A. V., Narayan, A., & Maheshwari, P. (2025) · International Conference on Information Technology Trends (ITT) · doi:10.1109/itt69610.2025.11352932 · Cited by 1
Dual erosion of equality and remedy in algorithmic decision systems
Johari, B. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01040-6 · Cited by 3
Modeling the Adoption of GenAI for Counter Speech: A Hybrid SEM-ANN Approach Uncovering the Interplay of AI Attributes and User Cognitive-Affective Load
Gan, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2637834 · Cited by 1
Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs
Pihulski, D., & Kocoń, J. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00261 · Cited by 1
A Comparative Survey of Social Bias in Text and Image Generation: Gaps, Directions and Compliance with the EU AI Act
Rad, A.-C., & Lemnaru, C. (2025) · International Conference on Computational Photography · doi:10.1109/iccp68926.2025.11427185 · Cited by 1
Technological transitions in artificial intelligence-driven cyberbullying mitigation on social media: a systematic review
Akwa, G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01054-x · Cited by 1
Attitudes, Imagined Roles, and Governance Boundaries for AI in Decentralized Social Media
Zhang, Z., Zhao, J., Wang, G., Viswanathan, S., Ross, T., Johnston, S., Liu, D., Noh, H., Kleek, M. G. V., & Shadbolt, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790295 · Cited by 2
Harnessing blockchain for emotion identification: A novel edge-compatible privacy-preserving framework
Hasan, M. S., Ahmed, A., Jabe, M. M. N., & Pirzado, F. A. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00560-6 · Cited by 1
Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models
Erol, A., Padhi, T., Saha, A., Aktas, M. E., & Kursuncu, U. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3821418 · Cited by 4
The algorithmic other: Dehumanization, infrastructure, and racial capitalism in the age of AI
Love, J. W. (2026) · Big Data & Society · doi:10.1177/20539517261458315 · Cited by 1
DGDS: Reliability-Gated Neuro-Symbolic Learning for Roman Urdu Hate Speech Detection
Wang, R. L., Peng, M., Liu, J., & Huang, X. (2026) · Applied Sciences · doi:10.3390/app16157394 · Cited by 1
Detecting Text-Bullying on Twitter Using Machine Learning Algorithms
Amer, A. Y. A., & Siddiqui, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88378-2_17 · Cited by 1
Protect the Filter Bubbles
Dames, P. (2021) · UF Journal of Undergraduate Research · doi:10.32473/ufjur.v23i.128414 · Cited by 1
Leveraging external resources for offensive content detection in social media
Kovács, G., Alonso, P., Saini, R., & Liwicki, M. (2022) · AI Communications · doi:10.3233/aic-210138 · Cited by 1
Computational Analysis of Online Hate Content using Cognitive -AI
Chetty, N., Alathur, S., Andrews, D., & Kumar, V. (2021) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/icccs51487.2021.9776339 · Cited by 1
Moderators
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.003.0002 · Cited by 1
KI-Verfahren für die Hate Speech Erkennung: Die Gestaltung von Ressourcen für das maschinelle Lernen und ihre Zuverlässigkeit
Mandl, T. (2023) · Springer eBooks · doi:10.1007/978-3-662-65964-9_6 · Cited by 1
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages
Zhang, C., Doan, K., Liao, Q., & Abdul-Mageed, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.160 · Cited by 6
Hate Speech Detection and Bias in Supervised Text Classification
Davidson, T. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.16 · Cited by 1
NoHateBrazil: A Brazilian Portuguese Text Offensiveness Analysis System
Vargas, F., Carvalho, I., Schmeisser-Nieto, W. S., Benevenuto, F., & Pardo, T. A. S. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_125 · Cited by 1
What is my privacy score? Measuring users’ privacy on social networking websites
Srivastava, A. K., & Mishra, R. (2024) · Electronic Commerce Research · doi:10.1007/s10660-023-09796-0 · Cited by 1
MAGNA
de Felipe, H., & (ed)., M. Á. M. (2022) · Editorial Comares eBooks · doi:10.55323/edc.2022.38 · Cited by 2
Social and Ethical Norms in Annotation Task Design
Amironesei, R., & Díaz, M. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3374639 · Cited by 1
Advancing Equality: Harnessing Generative AI to Combat Systemic Racism
Gabriel, S., Han, J. X., Liu, E., Puri, I., So, W., Christia, F., Dahleh, M., D’Ignazio, C., Ghassemi, M., Hosoi, P., et al. (2024) · doi:10.21428/e4baedd9.7dc53bbf · Cited by 1
Self-citation
EnronSR: A Benchmark for Evaluating AI-Generated Email Replies
Shay, M., Davidson, R., & Grinberg, N. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31448 · Cited by 3
Hate and Perceived Threats on the Resettlement of Afghan Refugees in Portugal
Veiga, J. P., & de Oliveira, L. G. (2024) · Societies · doi:10.3390/soc14070103 · Cited by 1
A Path Analysis Model of Responsible Usage of AI and Its Effectiveness on the Quality of Education: Perspective from India
Chanda, N., Chauhan, V., & Nath, N. J. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-72410-7_1 · Cited by 1
Challenges in Hate Speech Identification
Saha, P., Das, M., & Mukherjee, A. (2025) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-86595-4_4 · Cited by 1
Assessing the Level of Toxicity Against Distinct Groups in Bangla Social Media Comments: A Comprehensive Investigation
Moin, M. B., Debnath, P., Rifa, U. A., & Anis, R. B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1758-6_46 · Cited by 3
Digitalisierung und Demokratie
Herzog, L. M. (2020) · Franz Steiner Verlag eBooks · doi:10.25162/9783515128278 · Cited by 1
Measuring the Effects of Bias in Training Data for Literary Classification
Bagga, S., & Piper, A. (2020) · Cited by 1
Recognition of Hate or Offensive Tweets in the Online Communities
Machová, K., Suchanic, D., & Krešňáková, V. (2020) · International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta51985.2020.9379227 · Cited by 2
A felügyelt gépi tanulás kihívásai a szociológiai alkalmazásokban
Németh, R. (2021) · Metszetek · doi:10.18392/metsz/2021/3/3 · Cited by 1
Overview of NLPCC 2022 Shared Task 7: Fine-Grained Dialogue Social Bias Measurement
Zhou, J., Mi, F., Meng, H. M. L., & Deng, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17189-5_31 · Cited by 1
"To Target or Not to Target": Identification and Analysis of Abusive Text Using Ensemble of ClassifiersPreprint
Verma, G., Chhaya, N., & Vinay, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03256 · Cited by 1
Social Media Driven Public Health Informatics: Applications in Regulatory Science
Zhan, Y. (2019) · UA Campus Repository (The University of Arizona) · Cited by 1
Automatic Detection of Nastiness and Early Signs of Cyberbullying Incidents on Social Media
Samghabadi, N. S. (2020) · Cited by 1
Cross-geographic Bias Detection in Toxicity Modeling.Preprint
Ghosh, S., Baker, D., Jurgens, D., & Prabhakaran, V. (2021) · arXiv (Cornell University) · Cited by 1
Liberdade de expressão nas redes sociais: o problema jurídico da remoção de conteúdo pelas plataformas
Nitrini, R. V. (2020) · doi:10.11606/t.2.2020.tde-22032021-171558 · Cited by 1
Improving Counterfactual Generation for Fair Hate Speech DetectionPreprint
Davani, A. M., Omrani, A., Kennedy, B., Atari, M., Ren, X., & Dehghani, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.01721 · Cited by 2
Independent Ethical Assessment of Text Classification Models: A Hate Speech Detection Case StudyPreprint
Singh, A., Chen, J., Zhang, L., Rasekh, A., Golbin, I., & Rao, A. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07627 · Cited by 2
Learning Neural Models for Natural Language Processing in the Face of Distributional ShiftPreprint
Michel, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01558 · Cited by 2
Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing
(2022) · doi:10.18653/v1/2022.hcinlp-1 · Cited by 1
On the definition of toxicity in NLPPreprint
Berezin, S., Farahbakhsh, R., & Crespi, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.02357 · Cited by 1
Diversity, equity, and inclusivity in observational ambulatory assessment: Recommendations from two decades of Electronically Activated Recorder (EAR) researchPreprint
Kaplan, D. M., Tidwell, C. A., & Mehl, M. R. (2023) · doi:10.31234/osf.io/rnfps · Cited by 1
The Manager in the Machine: Organizational Priorities Influence AI Programmer’s Ability to Design Fair ModelsPreprint
Osborne, M. R., Cratsley, M. J., Massey, M., & Rice, V. (2024) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/tc5vq · Cited by 1
Est-ce que Vous Compute?
Falbo, A., & LaCroix, T. (2022) · Feminist Philosophy Quarterly · doi:10.5206/fpq/2022.3/4.14264 · Cited by 13
A Critical Audit of Accuracy and Demographic Biases within Toxicity Detection Tools
Jiang, J. (2020)
(Mis)Measuring People's Attitudes from Social Media
Sen, I. (2020) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3406865.3418363
Abusive Advertising: Scrutinizing socially relevant algorithms in a black box analysis to examine their impact on vulnerable patient groups in the health sectorPreprint
Reber, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02018
An Improve Framework for hate speech detection using Machine Learning Approach
Palimote, J., & Gaage, F. (2021) · IJARCCE · doi:10.17148/ijarcce.2021.10332
A Dataset and Baselines for Multilingual Reply SuggestionPreprint
Zhang, M., Wang, W., Deb, B., Zheng, G., Shokouhi, M., & Awadallah, A. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02017
A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators
Thakur, G. M., Caspersen, J. R., Herrmannová, D., Eaton, B., & Burdette, J. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.169
Opinion Prediction with User FingerprintingPreprint
Tumarada, K., Zhang, Y., Yang, F., Dragut, E. C., Gnawali, O. D., & Mukherjee, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.00270
#ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in PoliticsPreprint
Comer, J., Work, S., Mathewson, K. W., Cuthbertson, L., & Machin, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00116 · Cited by 1
An Empirical Investigation of Learning from Biased Toxicity LabelsPreprint
Nanda, N., Uesato, J., & Gowal, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01577
EXIST 2021: Inducing Bias in Deep Learning Models.
Garrido-Muñoz, I., Montejo‐Ráez, A., & Santiago, F. J. M. (2021) · IberLEF@SEPLN
Adversarial Scrubbing of Demographic Information for Text ClassificationPreprint
Chowdhury, S. B. R., Ghosh, S., Li, Y., Oliva, J. B., Srivastava, S., & Chaturvedi, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08613 · Cited by 4
Peer Review #1 of "Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled (v0.2)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.859v0.2/reviews/1
Peer Review #4 of "Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled (v0.1)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.859v0.1/reviews/4
Peer Review #3 of "Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled (v0.2)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.859v0.2/reviews/3
Peer Review #2 of "Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled (v0.1)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.859v0.1/reviews/2
Peer Review #1 of "Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled (v0.1)"
(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.859v0.1/reviews/1
Digitalisierung und Demokratie
Mechnich, J., & Kallinich, D. (2019) · Journal für politische Bildung · doi:10.46499/1254.1679
Peer Review #1 of "Towards generalisable hate speech detection: a review on obstacles and solutions (v0.2)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.598v0.2/reviews/1
Peer Review #1 of "Towards generalisable hate speech detection: a review on obstacles and solutions (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.598v0.1/reviews/1
Buntovništvo i nemogućnost komunikacijske društvene svijesti
Terzić, D. (2020) · In medias res · doi:10.46640/imr
Semantic Search over Wikipedia Documents Meaning of Queries Based Pre-Trained Language Model
Tharun, P., & TJPRC (2021) · International journal of computer science engineering and information technology research · doi:10.24247/ijcseitrdec20212
Peer Review #2 of "Towards generalisable hate speech detection: a review on obstacles and solutions (v0.1)"
(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.598v0.1/reviews/2
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection
Mosquera, A. (2022) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2022.semeval-1.66
Detecting Deception, Partisan, and Social Biases
Junquera, J. J. S. (2022) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/185784
Toward Analyzing the Diversity of Extractive Summaries
Hudson, A. D. (2022) · doi:10.15760/etd.7950
Copyright Page
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.002.0003
Conclusion
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.003.0006
Imposters and Tricksters
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.003.0003
Lurkers
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.003.0005
Internet Research Ethics for Philosophers: Privacy, Positionality, and Power
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.005.0001
Introduction
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.003.0001 · Cited by 1
Dedication
Frost‐Arnold, K. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780190089184.002.0004

Authors: Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A.
Affiliations: University of Washington (US)
Carnegie Mellon University (US)
Allen Institute for Artificial Intelligence (US)
Corresponding author: Sap, Maarten
ORCID: Sap, Maarten
Card, Dallas
Gabriel, Saadia
Choi, Yejin
Smith, Noah A.
Venue: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Publisher: Association for Computational Linguistics
Published: 2019
Pages: 1668-1678
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/p19-1163
OpenAlex: W2949678053
Cited by: 817
References: 40
Citations by year: 2026: 59 · 2025: 96 · 2024: 86 · 2023: 139 · 2022: 142 · 2021: 178 · 2020: 105 · 2019: 12
FWCI: 40.99
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
Funders: National Science Foundation (1714566)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords: hate speech detection, racial bias, African American English, dialects, annotation bias, hate speech datasets, priming, minority groups

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The Risk of Racial Bias in Hate Speech Detection

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks