The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
The Social Impact of Natural Language Processing
Open accessAbstract
Medical sciences have long since established an ethics code for experiments, to minimize the risk of harm to subjects.Natural language processing (NLP) used to involve mostly anonymous corpora, with the goal of enriching linguistic analysis, and was therefore unlikely to raise ethical concerns.As NLP becomes increasingly wide-spread and uses more data from social media, however, the situation has changed: the outcome of NLP experiments and applications can now have a direct effect on individual users' lives.Until now, the discourse on this topic in the field has not followed the technological development, while public discourse was often focused on exaggerated dangers.This position paper tries to take back the initiative and start a discussion.We identify a number of social implications of NLP and discuss their ethical significance, as well as ways to address them.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Thus, data, tools, and inferences obtained for one purpose can be used for another purpose (Hovy and Spruit, 2016; Benton et al., 2017)”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Hovy and Spruit, 2016
Citations
Cites
Principles of Biomedical Ethics
(2012) · Thieves of Virtue · doi:10.7551/mitpress/9079.003.0009 · Cited by 13,453
Availability: A heuristic for judging frequency and probability
Tversky, A., & Kahneman, D. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90033-9 · Cited by 9,984 · cited 1×
“Such topic overexposure may lead to a psychological effect called availability heuristic (Tversky and Kahneman, 1973): if people can recall a certain event, or have knowledge about specific things, they infer it must be more important.”
BackgroundReproduction in Education, Society and Culture
Erben, M., Bourdieu, P., & Passeron, J.-C. (1979) · British Journal of Sociology · doi:10.2307/589547 · Cited by 8,141
Indexical order and the dialectics of sociolinguistic life
Silverstein, M. C. (2003) · Language & Communication · doi:10.1016/s0271-5309(03)00013-2 · Cited by 3,737
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.025 · Cited by 2,349
The Normative Structure of Science
Merton, R. Κ. (1973) · Medical Entomology and Zoology · Cited by 1,625
Models of ecological rationality: The recognition heuristic.
Goldstein, D. G., & Gigerenzer, G. (2002) · Psychological Review · doi:10.1037/0033-295x.109.1.75 · Cited by 1,352
Voice, Footing, Enregisterment
Agha, A. (2005) · Journal of Linguistic Anthropology · doi:10.1525/jlin.2005.15.1.38 · Cited by 1,326
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008
Amazon Mechanical Turk: Gold Mine or Coal Mine?
Fort, K., Adda, G., & Cohen, K. B. (2011) · Computational Linguistics · doi:10.1162/coli_a_00057 · Cited by 772
Research Priorities for Robust and Beneficial Artificial Intelligence
Russell, S., Dewey, D., & Tegmark, M. (2015) · AI Magazine · doi:10.1609/aimag.v36i4.2577 · Cited by 637
Mosteller, F., & Wallace, D. L. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500849 · Cited by 466
Censorship and deletion practices in Chinese social media
Bamman, D., O'Connor, B., & Smith, N. A. (2012) · First Monday · doi:10.5210/fm.v17i3.3943 · Cited by 295
Inducing multilingual POS taggers and NP bracketers via robust projection across aligned corpora
Yarowsky, D., & Ngai, G. (2001) · doi:10.3115/1073336.1073362 · Cited by 244
Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections
Das, D., & Petrov, S. (2011) · Cited by 241
An analysis of the user occupational class through Twitter content
Preoțiuc-Pietro, D., Lampos, V., & Αλέτρας, Ν. (2015) · doi:10.3115/v1/p15-1169 · Cited by 224
Studying user income through language, behaviour and affect in social media
Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138717 · Cited by 219
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media
Volkova, S., Wilson, T., & Yarowsky, D. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1187 · Cited by 191
Personality Traits on Twitter—or—How to Get 1,500 Personality Tests in a Week
Plank, B., & Hovy, D. (2015) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w15-2913 · Cited by 178
Self-citationAuthor Age Prediction from Text using Linear Regression
Nguyen, D., A. Smith, N., & Rosé, C. P. (2018) · Figshare · doi:10.1184/r1/6473069 · Cited by 178
McKeown, K. R., & Rosenthal, S. (2011) · doi:10.7916/d8w384nb · Cited by 173
What's in a Name? Using First Names as Features for Gender Inference in Twitter
Liu, W. J., & Ruths, D. A. (2013) · National Conference on Artificial Intelligence · Cited by 165
Inferring Latent User Properties from Texts Published in Social Media
Volkova, S., Bachrach, Y., Armstrong, M., & Sharma, V. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9271 · Cited by 151
Demographic Factors Improve Classification Performance
Hovy, D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1073 · Cited by 148
Self-citationPrecautions Against What? The Availability Heuristic and Cross-Cultural Risk PerceptionsPreprint
Sunstein, C. R. (2004) · SSRN Electronic Journal · doi:10.2139/ssrn.578303 · Cited by 129
The Moral Character of Cryptographic WorkPreprint
Rogaway, P. (2016) · IACR Cryptology ePrint Archive · Cited by 127
Inferring User Political Preferences from Streaming Communications
Volkova, S., Coppersmith, G., & Durme, B. V. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1018 · Cited by 119
A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns
Tsur, O., Calacci, D., & Lazer, D. M. J. (2015) · doi:10.3115/v1/p15-1157 · Cited by 108
Tagging Performance Correlates with Author Age
Hovy, D., & Søgaard, A. (2015) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p15-2079 · Cited by 106
Self-citationThe Language Demographics of Amazon Mechanical Turk
Pavlick, E., Post, M., Irvine, A., Kachaev, D., & Callison-Burch, C. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00167 · Cited by 96
Distributed Representations of Geographically Situated Language
Bamman, D., Dyer, C., & Smith, N. A. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2134 · Cited by 93
Cross-lingual syntactic variation over age and gender
Johannsen, A., Hovy, D., & Søgaard, A. (2015) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k15-1011 · Cited by 89
Self-citationInverted indexing for cross-lingual NLP
Søgaard, A., Agić, Ż., Alonso, H. M., Plank, B., Bohnet, B., & Johannsen, A. (2015) · doi:10.3115/v1/p15-1165 · Cited by 79
Identifying fake Amazon reviews as learning from crowds
Fornaciari, T., & Poesio, M. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1030 · Cited by 78
If all you have is a bit of the Bible: Learning POS taggers for truly low-resource languages
Agić, Ż., Hovy, D., & Søgaard, A. (2015) · doi:10.3115/v1/p15-2044 · Cited by 75
Self-citationKeystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays
Banerjee, R., Feng, S., Kang, J. R. W., & Choi, Y. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1155 · Cited by 70
Challenges of studying and processing dialects in social media
Jørgensen, A., Hovy, D., & Søgaard, A. (2015) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w15-4302 · Cited by 64
Self-citationEmpirical Evaluation of Profile Characteristics for Gender Classification on Twitter
Alowibdi, J. S., Buy, U. A., & Yu, P. S. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.74 · Cited by 62
Hovy, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2057 · Cited by 47
Self-citationGender Matters: Feminist Linguistic Analysis
Mills, S. (2011) · SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) · Cited by 37
Staking out the unclear ethical terrain of online social experiments
Puschmann, C., & Bozdag, E. (2014) · Internet Policy Review · doi:10.14763/2014.4.338 · Cited by 35
Identifying Real or Fake Articles: Towards better Language Modeling
Badaskar, S., Agarwal, S., & Arora, S. (2008) · Cited by 29
Staying on Topic: An Indicator of Power in Political Debates
Prabhakaran, V., Arora, A., & Rambow, O. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1157 · Cited by 23
Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency
Wallach, H. (2014) · Cited by 21
Written Dialog and Social Power: Manifestations of Different Types of Power in Dialog Behavior
Prabhakaran, V., & Rambow, O. (2013) · International Joint Conference on Natural Language Processing · Cited by 20
Resolving Entity Morphs in Censored Data
Huang, H., Wen, Z., Yu, D., Ji, H., Sun, Y., Han, J., & He, L. (2013) · Cited by 19
A bibliometric analysis of privacy and ethics in IEEE Security and Privacy
Tse, J., Schrader, D. E., Ghosh, D. P., Liao, T., & Lundie, D. C. (2015) · Ethics and Information Technology · doi:10.1007/s10676-015-9369-6 · Cited by 12
Evaluating corpora documentation with regards to the Ethics and Big Data Charter
Couillault, A., Fort, K., Adda, G., & de Mazancourt, H. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2puvxu8b9txh · Cited by 9
Exploring Language Variation Across Europe - A Web-based Tool for Computational Sociolinguistics
Hovy, D., & Johannsen, A. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3u7q5rot3xrv · Cited by 5
Self-citationThe Language of Power and its Cultural Influence
Bracewell, D. B., & Tomlinson, M. T. (2012) · International Conference on Computational Linguistics · Cited by 5
Ethical and legal issues in corpus construction
McEnery, T. (2002) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2mhgu8jb7py3 · Cited by 1
(2012)
Cited by
Sorted by citations
Language (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
“A.2 Full categorization: Motivations Allocational harms Hovy and Spruit (2016); Caliskan et al. (2017); Madnani et al. (2017); Dixon et al. (2018); Kiritchenko and Mohammad (2018); Shen et al. (2018); Zhao et al. (2018b); Bhaskaran and Bhallamudi (2019); Bordia and Bowman (2019); Brunet et al.…”
BackgroundInfluentialBender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854
“, 2017) and papers (Hovy and Spruit, 2016) in NLP, as well as workshops in related fields, notably the FATML series (http://www.”
BackgroundInfluentialSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“Thus, data, tools, and inferences obtained for one purpose can be used for another purpose (Hovy and Spruit, 2016; Benton et al., 2017); yet, the risks associated with secondary uses may not be well understood.”
BackgroundTaxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
“Choices on model architecture may also have an impact, as it has been proposed that the architecture of current LMs and particularly tokenisation is well-suited to English, but not morphologically more complex languages [14, 79, 162].”
BackgroundMeasuring and Mitigating Unintended Bias in Text Classification
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674
Metadata
- Authors
- Hovy, D., & Spruit, S. L.
- Affiliations
- University of Copenhagen (DK)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2016
- Pages
- 591-598
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2511234952
- Cited by
- 329
- References
- 53
- Citations by year
- 2026: 31 · 2025: 39 · 2024: 18 · 2023: 47 · 2022: 45 · 2021: 58 · 2020: 40 · 2019: 16 · 2018: 17 · 2017: 15 · 2016: 3
- FWCI
- 71.55
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- social media data, ethical risks, public discourse on AI
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
