Statistically Significant Detection of Linguistic Change
Abstract
We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time.
Integrity signals
Registry
Cites
Statistics
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?
Existing efforts to address inference in embeddings are typically data intensive and computationally intensive.
“Although there have been efforts to address inference in embeddings (see, e.g, Kulkarni et al. 2015;Lauretig 2019), they are typically data intensive and computationally intensive.”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Kulkarni et al. 2015
Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Temporal Analysis of Language through Neural Language Models
Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., & Petrov, S. (2014) · Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science · doi:10.3115/v1/w14-2517 · Cited by 305 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720347115 · Cited by 1,065 · see citing works
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585 · see citing works
Using natural language processing to analyse text data in behavioural science
Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140 · see citing works
Altszyler, E., Ribeiro, S., Sigman, M., & Fernández Slezak, D. (2017) · Consciousness and Cognition · doi:10.1016/j.concog.2017.09.004 · Cited by 128 · see citing works
Embedding Regression: Models for Context-Specific Description and Inference
RODRIGUEZ, P. L., SPIRLING, A., & STEWART, B. M. (2023) · American Political Science Review · doi:10.1017/s0003055422001228 · Cited by 70 · see citing works
“Although there have been efforts to address inference in embeddings (see, e.g, Kulkarni et al. 2015; Lauretig 2019), they are typically data intensive and computationally intensive.”
BackgroundSimple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora
Gonen, H., Jawahar, G., Seddah, D., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.51 · Cited by 59 · see citing works
- Authors
- Kulkarni, V., Al-Rfou, R., Perozzi, B., & Skiena, S.
- Affiliations
- Stony Brook University (US)
- Stony Brook University, Stony Brook, NY, USA
- Venue
- Proceedings of the 24th International Conference on World Wide Web
- Published
- 2015-05-18
- Pages
- 625-635
- OpenAlex
- W1570098300
- Cited by
- 390
- References
- 2
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 32 · 2023: 30 · 2022: 33 · 2021: 55 · 2020: 60 · 2019: 65 · 2018: 27 · 2017: 30 · 2016: 28 · 2015: 8
- FWCI
- 17.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Funders
- NSF (DBI-1355990, IIS-1017181)
- Google Faculty Research Award
- Renaissance Technologies Fellowship
- Institute for Computational Science at Stony Brook University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- language change, change point detection, word meaning shift, internet language, word co-occurrence, distributional semantics, word embeddings, time series analysis, meta-analysis, semantic drift
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
