2020

Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective

Kiritchenko, Svetlana, Nejadgholi, Isar, Fraser, Kathleen C.

Understand

The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm.

Significant effort in Natural Language Processing (NLP) research has been devoted to addressing this problem through abusive content detection and related sub-areas, such as the detection of hate speech, toxicity, cyberbullying, etc.
Although current technologies achieve high classification performance in research studies, it has been observed that the real-life application of this technology can cause unintended harms, such as the silencing of under-represented groups.
We review a large body of NLP research on automatic abuse detection with a new focus on ethical challenges, organized around eight established ethical principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values.

Built on

Tutorial: safe and reliable machine learning
Original
Saria, S., and Subbaswamy, A. (2019) · 1904
Earlier work this paper cites.
Privacy-preserving classification of personal text messages with secure multi-party computation: An application to hate-speech detection
Original
Reich, D., Todoki, A., Dowsley, R., De Cock, M., and Nascimento, A. C. (2019) · 1906
Earlier work this paper cites.
RoBERTa: A robustly optimized BERT pretraining approach
Original
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019) · 1907
Earlier work this paper cites.
Tackling online abuse: A survey of automated abuse detection methods
Original
Mishra, P., Yannakoudakis, H., and Shutova, E. (2019) · 1908
Earlier work this paper cites.
Fairness with minimal harm: A pareto-optimal approach for healthcare
Original
Martinez, N., Bertran, M., and Sapiro, G. (2019) · 1911
Earlier work this paper cites.
Judgment under uncertainty: Heuristics and biases
Tversky, A., and Kahneman, D. (1974) · 1974
Earlier work this paper cites.
An integrative theory of intergroup conflict
Tajfel, H., and Turner, J. C. (1979) · 1979
Earlier work this paper cites.
A social identity model of deindividuation phenomena
Reicher, S. D., Spears, R., and Postmes, T. (1995) · 1995
Earlier work this paper cites.
Must we defend Nazis?: Hate speech, pornography, and the new first amendment
Delgado, R., and Stefancic, J. (1997) · 1997
Earlier work this paper cites.
Smokey: Automatic recognition of hostile messages
Spertus, E. (1997) · 1997
Earlier work this paper cites.
Conflict escalation: Dispute exacerbating elements of e-mail communication
Friedman, R. A., and Currall, S. C. (2003) · 2003
Earlier work this paper cites.
Stereoset: Measuring stereotypical bias in pretrained language models
Original
Nadeem, M., Bethke, A., and Reddy, S. (2020) · 2004
Earlier work this paper cites.
Cross-lingual zero-and few-shot hate speech detection utilising frozen transformer language models and AXEL
Original
Stappen, L., Brunn, F., and Schuller, B. (2020) · 2004
Earlier work this paper cites.
Operationalizing the legal concept of ‘incitement to hatred’ as an NLP task
Original
Zufall, F., Zhang, H., Kloppenborg, K., and Zesch, T. (2020) · 2004
Earlier work this paper cites.
Racism is a virus: Anti-Asian hate and counterhate in social media during the COVID-19 crisis
Original
Ziems, C., He, B., Soni, S., and Kumar, S. (2020) · 2005
Earlier work this paper cites.
To BAN or not to BAN: Bayesian attention networks for reliable hate speech detection
Original
Miok, K., Skrlj, B., Zaharie, D., and Robnik-Sikonja, M. (2020) · 2007
Earlier work this paper cites.
HSD shared task in VLSP campaign 2019: Hate speech detection for social good
Original
Vu, X.-S., Vu, T., Tran, M.-V., Le-Cong, T., and Nguyen, H. (2020) · 2007
Earlier work this paper cites.
The idea of justice
Sen, A. K. (2009) · 2009
Earlier work this paper cites.
Fair hate speech detection through evaluation of social group counterfactuals
Original
Davani, A. M., Omrani, A., Kennedy, B., Atari, M., Ren, X., and Dehghani, M. (2020) · 2010
Earlier work this paper cites.
Towards ethics by design in online abusive content detection
Original
Kiritchenko, S., and Nejadgholi, I. (2020) · 2010
Earlier work this paper cites.
Offensive language detection using multi-level classification
Razavi, A. H., Inkpen, D., Uritsky, S., and Matwin, S. (2010) · 2010
Earlier work this paper cites.
Recipes for safety in open-domain chatbots
Original
Xu, J., Ju, D., Li, M., Boureau, Y.-L., Weston, J., and Dinan, E. (2020) · 2010
Earlier work this paper cites.
Intermediaries and hate speech: Fostering digital citizenship for our information age
Citron, D. K., and Norton, H. (2011) · 2011
Earlier work this paper cites.
Speech and harm: Controversies over free speech
Maitra, I., and McGowan, M. K. (2012) · 2012
Earlier work this paper cites.
Interpretability and explainability: A machine learning zoo mini-tour
Original
Marcinkevičs, R., and Vogt, J. E. (2020) · 2012
Earlier work this paper cites.
The harm in hate speech
Waldron, J. (2012) · 2012
Earlier work this paper cites.
Improving cyberbullying detection with user context
Dadvar, M., Trieschnigg, D., Ordelman, R., and de Jong, F. (2013) · 2013
Earlier work this paper cites.
Analyzing and predicting viral tweets
Jenders, M., Kasneci, G., and Naumann, F. (2013) · 2013
Earlier work this paper cites.
Dehumanization and infrahumanization
Haslam, N., and Loughnan, S. (2014) · 2014
Earlier work this paper cites.
The role of prevent in countering online extremism
Saltman, E. M., and Russell, J. (2014) · 2014
Earlier work this paper cites.
Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: a meta-analysis
Van Geel, M., Vedder, P., and Tanilon, J. (2014) · 2014
Earlier work this paper cites.
Hate speech detection with comment embeddings
Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., and Bhamidipati, N. (2015) · 2015
Earlier work this paper cites.
Countering online hate speech
Gagliardone, I., Gal, D., Alves, T., and Martinez, G. (2015) · 2015
Earlier work this paper cites.
A lexicon-based approach for hate speech detection
Gitari, N. D., Zuping, Z., Damien, H., and Long, J. (2015) · 2015
Earlier work this paper cites.
Analyzing labeled cyberbullying incidents on the Instagram social network
Hosseinmardi, H., Mattson, S. A., Rafiq, R. I., Han, R., Lv, Q., and Mishra, S. (2015) · 2015
Earlier work this paper cites.
This is why we can’t have nice things: Mapping the relationship between online trolling and mainstream culture
Phillips, W. (2015) · 2015
Earlier work this paper cites.
Histories of hating
Shepherd, T., Harvey, A., Jordan, T., Srauy, S., and Miltner, K. (2015) · 2015
Earlier work this paper cites.
Counterspeech on Twitter: A field study
Benesch, S., Ruths, D., Dillon, K. P., Saleem, H. M., and Wright, L. (2016) · 2016
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., and Kalai, A. T. (2016) · 2016
Earlier work this paper cites.
Evidencing the harms of hate speech
Gelber, K., and McNamara, L. (2016) · 2016
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Original
Konečnỳ, J., McMahan, H. B., Ramage, D., and Richtárik, P. (2016) · 2016
Earlier work this paper cites.
Abusive language detection in online user content
Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., and Chang, Y. (2016) · 2016
Earlier work this paper cites.
“Why should I trust you?” Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
Earlier work this paper cites.
Governing hate speech by means of counterspeech on Facebook
Schieb, C., and Preuss, M. (2016) · 2016
Earlier work this paper cites.
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016) · 2016
Earlier work this paper cites.
Engineering safety in machine learning
Varshney, K. R. (2016) · 2016
Earlier work this paper cites.
Are you a racist or am I seeing things? Annotator influence on hate speech detection on Twitter
Waseem, Z. (2016) · 2016
Earlier work this paper cites.
Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter
Waseem, Z., and Hovy, D. (2016) · 2016
Earlier work this paper cites.
Classification and its consequences for online harassment: Design insights from HeartMob
Blackwell, L., Dimond, J., Schoenebeck, S., and Lampe, C. (2017) · 2017
Earlier work this paper cites.
Of, for, and by the people: the legal lacuna of synthetic persons
Bryson, J. J., Diamantis, M. E., and Grant, T. D. (2017) · 2017
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., and Narayanan, A. (2017) · 2017
Earlier work this paper cites.
Censoring hate speech in US social media content: Understanding the users perspective
Carlson, C. (2017) · 2017
Earlier work this paper cites.
Automated hate speech detection and the problem of offensive language
Davidson, T., Warmsley, D., Macy, M., and Weber, I. (2017) · 2017
Earlier work this paper cites.
Online Harassment 2017
Duggan, M. (2017) · 2017
Earlier work this paper cites.
Legal framework, dataset and annotation schema for socially unacceptable online discourse practices in Slovene
Fišer, D., Erjavec, T., and Ljubešić, N. (2017) · 2017
Earlier work this paper cites.
Using convolutional neural networks to classify hate-speech
Gambäck, B., and Sikdar, U. K. (2017) · 2017
Earlier work this paper cites.
Detecting online hate speech using context aware models
Gao, L., and Huang, R. (2017) · 2017
Earlier work this paper cites.
A large labeled corpus for online harassment research
Golbeck, J., Ashktorab, Z., Banjo, R. O., Berlinger, A., Bhagwan, S., Buntain, C., Cheakalos, P., Geller, A. A., Gnanasekaran, R. K., Gunasekaran, R. R., et al. (2017) · 2017
Earlier work this paper cites.
Deceiving Google’s Perspective API built for detecting toxic comments
Original
Hosseini, H., Kannan, S., Zhang, B., and Poovendran, R. (2017) · 2017
Earlier work this paper cites.
Hate speech in public discourse: A pessimistic defense of counterspeech
Lepoutre, M. (2017) · 2017
Earlier work this paper cites.
Abusive language detection on Arabic social media
Mubarak, H., Darwish, K., and Magdy, W. (2017) · 2017
Earlier work this paper cites.
Perspective API documentation - score normalization and feedback
PerspectiveAPI (2017) · 2017
Earlier work this paper cites.
Extreme speech online: An anthropological critique of hate speech debates
Pohjonen, M., and Udupa, S. (2017) · 2017
Earlier work this paper cites.
Hate speech annotation: Analysis of an Italian Twitter corpus
Poletto, F., Stranisci, M., Sanguinetti, M., Patti, V., and Bosco, C. (2017) · 2017
Earlier work this paper cites.
A survey on hate speech detection using natural language processing
Schmidt, A., and Wiegand, M. (2017) · 2017
Earlier work this paper cites.
Beyond law: Protecting cyber victims through engineering and design
Vincent, N. A., and Jane, E. A. (2017) · 2017
Earlier work this paper cites.
Understanding abuse: A typology of abusive language detection subtasks
Waseem, Z., Davidson, T., Warmsley, D., and Weber, I. (2017) · 2017
Earlier work this paper cites.
Ex machina: Personal attacks seen at scale
Wulczyn, E., Thain, N., and Dixon, L. (2017) · 2017
Earlier work this paper cites.
New York Times: Using AI to host better conversations
Adams, C. (2018) · 2018
Earlier work this paper cites.
The human cost of online content moderation
Arsht, A., and Etcovitch, D. (2018) · 2018
Earlier work this paper cites.
Data statements for natural language processing: Toward mitigating system bias and enabling better science
Bender, E. M., and Friedman, B. (2018) · 2018
Earlier work this paper cites.
Overview of the EVALITA 2018 hate speech detection task
Bosco, C., Felice, D., Poletto, F., Sanguinetti, M., and Maurizio, T. (2018) · 2018
Earlier work this paper cites.
From editorial obligation to procedural accountability: policy approaches to online content in the era of information intermediaries
Bunting, M. (2018) · 2018
Earlier work this paper cites.
The Internet’s hidden rules: An empirical study of Reddit norm violations at micro, meso, and macro scales
Chandrasekharan, E., Samory, M., Jhaver, S., Charvat, H., Bruckman, A., Lampe, C., Eisenstein, J., and Gilbert, E. (2018) · 2018
Earlier work this paper cites.
Measuring and mitigating unintended bias in text classification
Dixon, L., Li, J., Sorensen, J., Thain, N., and Vasserman, L. (2018) · 2018
Earlier work this paper cites.

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Deep learning models for multilingual hate speech detection
Aluru, S. S., Mathew, B., Saha, P., and Mukherjee, A. (2020) · 2020
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A novel methodology for developing automatic harassment classifiers for Twitter
Arora, I., Guo, J., Levitan, S. I., McGregor, S., and Hirschberg, J. (2020) · 2020
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Annotating for hate speech: The MaNeCo corpus and some input from critical discourse analysis
Assimakopoulos, S., Vella Muskat, R., van der Plas, L., and Gatt, A. (2020) · 2020
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Machine learning techniques for hate speech classification of Twitter data: State-of-the-art, future challenges and research directions
Ayo, F. E., Folorunso, O., Ibharalu, F. T., and Osinuga, I. A. (2020) · 2020
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A unified taxonomy of harmful content
Banko, M., MacKeen, B., and Ray, L. (2020) · 2020
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Unmasking contextual stereotypes: Measuring and mitigating BERT’s gender bias
Bartl, M., Nissim, M., and Gatt, A. (2020) · 2020
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Language (technology) is power: A critical survey of “bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., and Wallach, H. (2020) · 2020
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WAC: A corpus of Wikipedia conversations for online abuse detection
Cecillon, N., Labatut, V., Dufour, R., and Linares, G. (2020) · 2020
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Multi-dimensional gender bias classification
Dinan, E., Fan, A., Wu, L., Weston, J., Kiela, D., and Williams, A. (2020) · 2020
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Towards transparency by design for artificial intelligence
Felzmann, H., Fosch-Villaronga, E., Lutz, C., and Tamò-Larrieux, A. (2020) · 2020
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Unsupervised discovery of implicit gender bias
Field, A., and Tsvetkov, Y. (2020) · 2020
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Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., and Srikumar, M. (2020) · 2020
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Toxic, hateful, offensive or abusive? What are we really classifying? An empirical analysis of hate speech datasets
Fortuna, P., Soler, J., and Wanner, L. (2020) · 2020
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., and Smith, N. A. (2020) · 2020
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Cyberbullying detection with fairness constraints
Gencoglu, O. (2020) · 2020
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Algorithmic content moderation: Technical and political challenges in the automation of platform governance
Gorwa, R., Binns, R., and Katzenbach, C. (2020) · 2020
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Why attention is not explanation: Surgical intervention and causal reasoning about neural models
Grimsley, C., Mayfield, E., and Bursten, J. R. (2020) · 2020
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Transformers and data augmentation for aggressiveness detection in Mexican Spanish
Guzman-Silverio, M., Balderas-Paredes, A., and López-Monroy, A. (2020) · 2020
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Gaming algorithmic hate-speech detection: Stakes, parties, and moves
Haapoja, J., Laaksonen, S.-M., and Lampinen, A. (2020) · 2020
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The political power of platforms: How current attempts to regulate misinformation amplify opinion power
Helberger, N. (2020) · 2020
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Cyberbullying fact sheet: Identification, Prevention, and Response
Hinduja, S., and Patchin, J. W. (2020) · 2020
Closest in time.
Multilingual Twitter corpus and baselines for evaluating demographic bias in hate speech recognition
Huang, X., Xing, L., Dernoncourt, F., and Paul, M. (2020) · 2020
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Lawmakers drill down on how facebook and twitter moderate content
Isaac, M., and Browning, K. (2020) · 2020
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What makes discrimination morally wrong? A harm-based view reconsidered
Ishida, S. (2020) · 2020
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The state and fate of linguistic diversity and inclusion in the NLP world
Joshi, P., Santy, S., Budhiraja, A., Bali, K., and Choudhury, M. (2020) · 2020
Closest in time.
Systematic attack surface reduction for deployed sentiment analysis models
Kalin, J., Noever, D., and Dozier, G. (2020) · 2020
Closest in time.
Toward situated interventions for algorithmic equity: lessons from the field
Katell, M., Young, M., Dailey, D., Herman, B., Guetler, V., Tam, A., Bintz, C., Raz, D., and Krafft, P. M. (2020) · 2020
Closest in time.
The hateful memes challenge: Detecting hate speech in multimodal memes
Kiela, D., Firooz, H., Mohan, A., Goswami, V., Singh, A., Ringshia, P., and Testuggine, D. (2020) · 2020
Closest in time.
Report: Facebook makes 300,000 content moderation mistakes every day
Koetsier, J. (2020) · 2020
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Weight poisoning attacks on pre-trained models
Kurita, K., Michel, P., and Neubig, G. (2020) · 2020
Closest in time.
ALBERT: A lite BERT for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2020) · 2020
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Explainable AI approach towards toxic comment classification
Mahajan, A., Shah, D., and Jafar, G. (2020) · 2020
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Predicting the outbreak of conflict in online discussions using emotion-based features
Marcinowski, M., and Ławrynowicz, A. (2020) · 2020
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Swe2: Subword enriched and significant word emphasized framework for hate speech detection
Mou, G., Ye, P., and Lee, K. (2020) · 2020
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Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nangia, N., Vania, C., Bhalerao, R., and Bowman, S. R. (2020) · 2020
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A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on Twitter
Naseem, U., Razzak, I., and Eklund, P. W. (2020) · 2020
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Nejadgholi, I., and Kiritchenko, S. (2020) · 2020
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Comparative evaluation of label agnostic selection bias in multilingual hate speech datasets
Ousidhoum, N., Song, Y., and Yeung, D.-Y. (2020) · 2020
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Toxicity detection: Does context really matter?
Pavlopoulos, J., Sorensen, J., Dixon, L., Thain, N., and Androutsopoulos, I. (2020) · 2020
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Prabhakaran, V., Waseem, Z., Akiwowo, S., and Vidgen, B. (2020) · 2020
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Solidarity and community engagement in global health research
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Mitigating unfair bias in ML models with the mindiff framework
Prost, F. (2020) · 2020
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Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., and Barnes, P. (2020) · 2020
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Investigating sampling bias in abusive language detection
Razo, D., and Kübler, S. (2020) · 2020
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Bagging BERT models for robust aggression identification
Risch, J., and Krestel, R. (2020) · 2020
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Aggression and misogyny detection using BERT: A multi-task approach
Safi Samghabadi, N., Patwa, P., PYKL, S., Mukherjee, P., Das, A., and Solorio, T. (2020) · 2020
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Approaches to automated detection of cyberbullying: A survey
Salawu, S., He, Y., and Lumsden, J. (2020) · 2020
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Developing an online hate classifier for multiple social media platforms
Salminen, J., Hopf, M., Chowdhury, S. A., Jung, S.-g., Almerekhi, H., and Jansen, B. J. (2020) · 2020
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Social bias frames: Reasoning about social and power implications of language
Sap, M., Gabriel, S., Qin, L., Jurafsky, D., Smith, N. A., and Choi, Y. (2020) · 2020
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What happened when humans stopped managing social media content
Scott, M., and Kayali, L. (2020) · 2020
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Predictive biases in natural language processing models: A conceptual framework and overview
Shah, D. S., Schwartz, H. A., and Hovy, D. (2020) · 2020
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Sigurbergsson, G. I., and Derczynski, L. (2020) · 2020
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Generating counter narratives against online hate speech: Data and strategies
Tekiroğlu, S. S., Chung, Y.-L., and Guerini, M. (2020) · 2020
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Detecting ‘dirt and ‘toxicity: Rethinking content moderation as pollution behaviour
Thylstrup, N., and Waseem, Z. (2020) · 2020
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Towards a friendly online community: An unsupervised style transfer framework for profanity redaction
Tran, M., Zhang, Y., and Soleymani, M. (2020) · 2020
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Dispute resolution and content moderation: Fair, accountable, independent, transparent, and effective
Tworek, H., and Tenove, C. (2020) · 2020
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Quarantining online hate speech: technical and ethical perspectives
Ullmann, S., and Tomalin, M. (2020) · 2020
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A multi-platform dataset for detecting cyberbullying in social media
Van Bruwaene, D., Huang, Q., and Inkpen, D. (2020) · 2020
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On mismatched detection and safe, trustworthy machine learning
Varshney, K. R. (2020) · 2020
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A human-centered agenda for intelligible machine learning
Vaughan, J. W., and Wallach, H. (2020) · 2020
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Directions in abusive language training data, a systematic review: Garbage in, garbage out
Vidgen, B., and Derczynski, L. (2020) · 2020
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Investigating annotator bias with a graph-based approach
Wich, M., Al Kuwatly, H., and Groh, G. (2020) · 2020
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UHH-LT at SemEval-2020 task 12: Fine-tuning of pre-trained transformer networks for offensive language detection
Wiedemann, G., Yimam, S. M., and Biemann, C. (2020) · 2020
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Towards hate speech detection at large via deep generative modeling
Wullach, T., Adler, A., and Minkov, E. M. (2020) · 2020
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SemEval-2020 task 12: Multilingual offensive language identification in social media (OffensEval 2020)
Zampieri, M., Nakov, P., Rosenthal, S., Atanasova, P., Karadzhov, G., Mubarak, H., Derczynski, L., Pitenis, Z., and Çöltekin, Ç. (2020) · 2020
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Understanding and countering stereotypes: A computational approach to the stereotype content model
Fraser, K. C., Nejadgholi, I., and Kiritchenko, S. (2021) · 2021
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On transferability of bias mitigation effects in language model fine-tuning
Jin, X., Barbieri, F., Kennedy, B., Davani, A. M., Neves, L., and Ren, X. (2021) · 2021
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HateCheck: Functional tests for hate speech detection models
Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H., and Pierrehumbert, J. (2021) · 2021
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Introducing CAD: the contextual abuse dataset
Vidgen, B., Nguyen, D., Margetts, H., Rossini, P., and Tromble, R. (2021) · 2021
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Implicitly abusive language – what does it actually look like and why are we not getting there?
Wiegand, M., Ruppenhofer, J., and Eder, E. (2021) · 2021
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Challenges in automated debiasing for toxic language detection
Zhou, X., Sap, M., Swayamdipta, S., Choi, Y., and Smith, N. (2021) · 2021
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