{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T07:12:22Z","timestamp":1782803542857,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P024394\/1 and EP\/R033501\/1"],"award-info":[{"award-number":["EP\/P024394\/1 and EP\/R033501\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,3]]},"DOI":"10.1145\/3442188.3445875","type":"proceedings-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T01:45:48Z","timestamp":1614217548000},"page":"116-128","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Differential Tweetment"],"prefix":"10.1145","author":[{"given":"Ari","family":"Ball-Burack","sequence":"first","affiliation":[{"name":"Compliant &amp; Accountable Systems Group University of Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle Seng Ah","family":"Lee","sequence":"additional","affiliation":[{"name":"Compliant &amp; Accountable Systems Group University of Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Cobbe","sequence":"additional","affiliation":[{"name":"Compliant &amp; Accountable Systems Group University of Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jatinder","family":"Singh","sequence":"additional","affiliation":[{"name":"Compliant &amp; Accountable Systems Group University of Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"The cost of dichotomising continuous variables. BMJ 332, 7549","author":"Altman Douglas G","year":"2006","unstructured":"Douglas G Altman and Patrick Royston. 2006. The cost of dichotomising continuous variables. BMJ 332, 7549 (2006), 1080."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054223"},{"key":"e_1_3_2_1_3_1","volume-title":"John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang.","author":"Bellamy Rachel K. E.","year":"2018","unstructured":"Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. https:\/\/arxiv.org\/abs\/1810.01943"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the Conference on Fairness, Accountability, and Transparency in Machine Learning (FAT\/ML","author":"Beutel Alex","year":"2017","unstructured":"Alex Beutel, Jilin Chen, Zhe Zhao, and Ed Chi. 2017. Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations. In Proceedings of the Conference on Fairness, Accountability, and Transparency in Machine Learning (FAT\/ML 2017)."},{"key":"e_1_3_2_1_5_1","volume-title":"Forthcoming","author":"Bloch-Wehba Hannah","year":"2019","unstructured":"Hannah Bloch-Wehba. 2019. Automation in Moderation. Cornell International Law Journal, Forthcoming (2019)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1120"},{"key":"e_1_3_2_1_7_1","volume-title":"Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States. Palgrave Communications 2","author":"Bok\u00e1nyi Eszter","year":"2016","unstructured":"Eszter Bok\u00e1nyi, D\u00e1niel Kondor, L\u00e1szl\u00f3 Dobos, Tam\u00e1s Seb\u0151k, J\u00f3zsef St\u00e9ger, Istv\u00e1n Csabai, and G\u00e1bor Vattay. 2016. Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States. Palgrave Communications 2 (2016)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157584"},{"key":"e_1_3_2_1_9_1","volume-title":"Content or context moderation","author":"Caplan Robyn","year":"2018","unstructured":"Robyn Caplan. 2018. Content or context moderation. Data & Society Research Institute (2018)."},{"key":"e_1_3_2_1_10_1","volume-title":"Algorithmic Censorship by Social Platforms: Power and Resistance. Philosophy & Technology","author":"Cobbe Jennifer","year":"2020","unstructured":"Jennifer Cobbe. 2020. Algorithmic Censorship by Social Platforms: Power and Resistance. Philosophy & Technology (2020)."},{"key":"e_1_3_2_1_11_1","volume-title":"Regulating Recommending: Motivations, Considerations, and Principles. European Journal of Law and Technology, 10(3)","author":"Cobbe Jennifer","year":"2019","unstructured":"Jennifer Cobbe and Jatinder Singh. 2019. Regulating Recommending: Motivations, Considerations, and Principles. European Journal of Law and Technology, 10(3) (2019)."},{"key":"e_1_3_2_1_12_1","volume-title":"The Valorization of Surveillance: Towards a Political Economy of Facebook. Democratic Communiqu\u00e9 22","author":"Cohen Nicole","year":"2011","unstructured":"Nicole Cohen. 2011. The Valorization of Surveillance: Towards a Political Economy of Facebook. Democratic Communiqu\u00e9 22 (2011)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2016.0048"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-3504"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315851600"},{"key":"e_1_3_2_1_17_1","volume-title":"Measuring and Mitigating Unintended Bias in Text Classification. In AAAI\/ACM Conference on AI, Ethics, and Society. Association for the Advancement of Artificial Intelligence.","author":"Dixon Lucas","year":"2018","unstructured":"Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018. Measuring and Mitigating Unintended Bias in Text Classification. In AAAI\/ACM Conference on AI, Ethics, and Society. Association for the Advancement of Artificial Intelligence."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_19_1","unstructured":"Elizabeth Dwoskin Jeanne Whalen and Regine Cabato. 2019. Content moderators at YouTube Facebook and Twitter see the worst of the web - and suffer silently. The Washington Post. https:\/\/www.washingtonpost.com\/technology\/2019\/07\/25\/social-media-companies-are-outsourcing-their-dirty-work-philippines-generation-workers-is-paying-price\/."},{"key":"e_1_3_2_1_20_1","volume-title":"Media & Sport","author":"UK Department for Digital, Culture","year":"2018","unstructured":"UK Department for Digital, Culture, Media & Sport. 2018. Data Ethics Framework. Government Guideline."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v12i1.14991"},{"key":"e_1_3_2_1_22_1","unstructured":"Aditya Gaydhani Vikrant Doma Shrikant Kendre and Laxmi Bhagwat. 2018. Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach. arXiv:1809.08651 [cs.CL]"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3091478.3091509"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems -","volume":"2","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (NIPS '14). MIT Press, 2672--2680."},{"key":"e_1_3_2_1_25_1","volume-title":"Algorithmic content moderation: Technical and political challenges in the automation of platform governance","author":"Gorwa Robert","year":"2020","unstructured":"Robert Gorwa, Reuben Binns, and Christian Katzenbach. 2020. Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society 7, 1 (2020)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157469"},{"key":"e_1_3_2_1_27_1","unstructured":"High-Level Expert Group on AI. 2019. Ethics guidelines for trustworthy AI. Report. European Commission."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 591--598","author":"Hovy Dirk","unstructured":"Dirk Hovy and Shannon L. Spruit. 2016. The Social Impact of Natural Language Processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 591--598."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.809401"},{"key":"e_1_3_2_1_30_1","volume-title":"Informal proceedings of the 19th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn'10","author":"Kamiran F.","year":"2010","unstructured":"F. Kamiran and T.G.K. Calders. 2010. Classification with no discrimination by preferential sampling. In Informal proceedings of the 19th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn'10, Leuven, Belgium, May 27-28, 2010). 1--6."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"e_1_3_2_1_33_1","volume-title":"Inherent Trade-Offs in the Fair Determination of Risk Scores. In 8th Innovations in Theoretical Computer Science Conference, ITCS 2017","volume":"23","author":"Kleinberg Jon M.","year":"2017","unstructured":"Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2017. Inherent Trade-Offs in the Fair Determination of Risk Scores. In 8th Innovations in Theoretical Computer Science Conference, ITCS 2017, January 9-11, 2017, Berkeley, CA, USA (LIPIcs, Vol. 67). Schloss Dagstuhl - Leibniz-Zentrum f\u00fcr Informatik, 43:1-43:23."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5113"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00980"},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR","author":"Louizos Christos","year":"2016","unstructured":"Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. 2016. The Variational Fair Autoencoder. In Proceedings of the 4th International Conference on Learning Representations (ICLR 2016)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0221152"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"4391","author":"Mary Jeremie","year":"2019","unstructured":"Jeremie Mary, Cl\u00e9ment Calauz\u00e8nes, and Noureddine El Karoui. 2019. Fairness-Aware Learning for Continuous Attributes and Treatments. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97). PMLR, 4382--4391."},{"key":"e_1_3_2_1_39_1","unstructured":"Louise Matsakis and Paris Martineau. 2020. Coronavirus Disrupts Social Media's First Line of Defense. Wired. https:\/\/www.wired.com\/story\/coronavirus-social-media-automated-content-moderation\/."},{"key":"e_1_3_2_1_40_1","unstructured":"Ninareh Mehrabi Fred Morstatter Nripsuta Saxena Kristina Lerman and Aram Galstyan. 2019. A Survey on Bias and Fairness in Machine Learning. arXiv:1908.09635 [cs.LG]"},{"key":"e_1_3_2_1_41_1","volume-title":"Media & Sport","author":"UK Home Office and UK Department for Digital, Culture","year":"2020","unstructured":"UK Home Office and UK Department for Digital, Culture, Media & Sport. 2020. Online Harms White Paper. Government Guideline."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1302"},{"key":"e_1_3_2_1_43_1","unstructured":"Frank Pasquale. 2019. The Second Wave of Algorithmic Accountability. LPE Blog. https:\/\/lpeblog.org\/2019\/11\/25\/the-second-wave-of-algorithmic-accountability\/."},{"key":"e_1_3_2_1_44_1","unstructured":"Georgios Pitsilis Heri Ramampiaro and Helge Langseth. 2018. Detecting Offensive Language in Tweets Using Deep Learning. (2018)."},{"key":"e_1_3_2_1_45_1","unstructured":"Julia Powles and Helen Nissenbaum. 2018. The Seductive Diversion of 'Solving' Bias in Artificial Intelligence. Medium OneZero. https:\/\/onezero.medium.com\/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53."},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics (COLING). Association for Computational Linguistics, 1534--1545","author":"Preo\u0163iuc-Pietro Daniel","year":"2018","unstructured":"Daniel Preo\u0163iuc-Pietro and Lyle Ungar. 2018. User-Level Race and Ethnicity Predictors from Twitter Text. In Proceedings of the 27th International Conference on Computational Linguistics (COLING). Association for Computational Linguistics, 1534--1545."},{"key":"e_1_3_2_1_47_1","volume-title":"Solidarity with U.S. protesters: People around the world march and speak out against racism","author":"Press The Associated","unstructured":"The Associated Press. 2020. Solidarity with U.S. protesters: People around the world march and speak out against racism. Canadian Broadcasting Corporation. https:\/\/www.cbc.ca\/news\/world\/protests-world-floyd-1.5595135."},{"key":"e_1_3_2_1_48_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1668--1678","author":"Sap Maarten","unstructured":"Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1668--1678."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/647967.741626"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-1101"},{"key":"e_1_3_2_1_51_1","volume-title":"Predatory Inclusion and Education Debt: Rethinking the Racial Wealth Gap. Social Currents 4, 3","author":"Seamster Louise","year":"2017","unstructured":"Louise Seamster and Rapha\u00ebl Charron-Ch\u00e9nier. 2017. Predatory Inclusion and Education Debt: Rethinking the Racial Wealth Gap. Social Currents 4, 3 (2017)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Deven Shah H. Andrew Schwartz and Dirk Hovy. 2019. Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview. arXiv:1912.11078 [cs.CL]","DOI":"10.18653\/v1\/2020.acl-main.468"},{"key":"e_1_3_2_1_53_1","unstructured":"H. Suresh and J. Guttag. 2019. A Framework for Understanding Unintended Consequences of Machine Learning. ArXiv abs\/1901.10002 (2019)."},{"key":"e_1_3_2_1_54_1","unstructured":"Twitter. [n.d.]. Our range of enforcement options. https:\/\/help.twitter.com\/en\/rules-and-policies\/enforcement-options."},{"key":"e_1_3_2_1_55_1","unstructured":"Twitter. [n.d.]. The Twitter Rules. https:\/\/help.twitter.com\/en\/rules-and-policies\/twitter-rules."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Ameya Vaidya Feng Mai and Yue Ning. 2019. Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection. arXiv:1909.09758 [cs.AI]","DOI":"10.1609\/icwsm.v14i1.7334"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3194770.3194776"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","unstructured":"Bertie Vidgen and Leon Derczynski. 2020. Directions in Abusive Language Training Data: Garbage In Garbage Out. arXiv:2004.01670 [cs.CL]","DOI":"10.1371\/journal.pone.0243300"},{"key":"e_1_3_2_1_59_1","volume-title":"Proceedings of the Conference on Fairness, Accountability, and Transparency in Machine Learning (FAT\/ML","author":"Wadsworth Christina","year":"2018","unstructured":"Christina Wadsworth, Francesca Vera, and Chris Piech. 2018. Achieving fairness through adversarial learning: an application to recidivism prediction. In Proceedings of the Conference on Fairness, Accountability, and Transparency in Machine Learning (FAT\/ML 2018)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-5618"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-3012"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-2013"},{"key":"e_1_3_2_1_64_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Wiegand Michael","year":"2019","unstructured":"Michael Wiegand, Josef Ruppenhofer, and Thomas Kleinbauer. 2019. Detection of abusive language: the problem of biased datasets. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 602--608."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398556"},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the 30th International Conference on International Conference on Machine Learning -","volume":"28","author":"Zemel Richard","year":"2013","unstructured":"Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork. 2013. Learning Fair Representations. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML '13). JMLR.org, III-325-III-333."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2003"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1177\/1095796018819461"}],"event":{"name":"FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency","location":"Virtual Event Canada","acronym":"FAccT '21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445875","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:56Z","timestamp":1750193336000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445875"}},"subtitle":["Mitigating Racial Dialect Bias in Harmful Tweet Detection"],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":68,"alternative-id":["10.1145\/3442188.3445875","10.1145\/3442188"],"URL":"https:\/\/doi.org\/10.1145\/3442188.3445875","relation":{},"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"2021-03-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}