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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change

arXiv 1606.02821 ↗First posted 9 Jun 2016cs.CLcited by 63

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Abstract (arXiv)

Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic change. The first measure, which has been used in many previous works, analyzes global shifts in a word's distributional semantics, it is sensitive to changes due to regular processes of linguistic drift, such as the semantic generalization of promise ("I promise." -> "It promised to be exciting."). The second measure, which we develop here, focuses on local changes to a word's nearest semantic neighbors; it is more sensitive to cultural shifts, such as the change in the meaning of cell ("prison cell" -> "cell phone"). Comparing measurements made by these two methods allows researchers to determine whether changes are more cultural or linguistic in nature, a distinction that is essential for work in the digital humanities and historical linguistics.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2016.

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Citing papers per year: 2021: 17; 2022: 8; 2023: 12; 2024: 14; 2025: 4; 2026: 8.

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  3. 2021Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Mediacited by 51
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  8. 2023Fairness in Language Models Beyond English: Gaps and Challengescited by 16
  9. 2021Challenges for Computational Lexical Semantic Changecited by 15
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  11. 2021Statistically significant detection of semantic shifts using contextual word embeddingscited by 14
  12. 2022Discovering Differences in the Representation of People using Contextualized Semantic Axescited by 12
38 more
  1. 2023Towards More Human-like AI Communication: A Review of Emergent Communication Researchcited by 12
  2. 2021Three-part diachronic semantic change dataset for Russiancited by 11
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  4. 2024Robustness of Large Language Models to Perturbations in Textcited by 11
  5. 2024Graph-based Clustering for Detecting Semantic Change Across Time and Languagescited by 10
  6. 2023Substitution-based Semantic Change Detection using Contextual Embeddingscited by 9
  7. 2021Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crispcited by 7
  8. 2022NorDiaChange: Diachronic Semantic Change Dataset for Norwegiancited by 7
  9. 2024Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Typescited by 7
  10. 2021Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspaperscited by 6
  11. 2023Robust language-based mental health assessments in time and space through social mediacited by 5
  12. 2021Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018cited by 4
  13. 2021Capturing Stance Dynamics in Social Media: Open Challenges and Research Directionscited by 4
  14. 2022A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysiscited by 4
  15. 2022Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddingscited by 3
  16. 2024Presence or Absence: Are Unknown Word Usages in Dictionaries?cited by 3
  17. 2024A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applicationscited by 3
  18. 2021A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosiscited by 2
  19. 2022SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Mediacited by 2
  20. 2023Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarization on Social Media Discoursecited by 2
  21. 2023Investigating the Effects of Fairness Interventions Using Pointwise Representational Similaritycited by 2
  22. 2023Geometric instability of graph neural networks on large graphscited by 2
  23. 2025Rethinking Word Similarity: Semantic Similarity through Classification Confusioncited by 2
  24. 2025SiDiaC: Sinhala Diachronic Corpuscited by 2
  25. 2021Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Taskscited by 1
  26. 2022TransDrift: Modeling Word-Embedding Drift using Transformercited by 1
  27. 2023Semantic Change Detection for the Romanian Languagecited by 1
  28. 2024Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distancecited by 1
  29. 2024A Systematic Survey of Natural Language Processing for the Greek Languagecited by 1
  30. 2024Historical Ink: Semantic Shift Detection for 19th Century Spanishcited by 1
  31. 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspectivecited by 1
  32. 2021Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis managementcited by 0
  33. 2021DUKweb: Diachronic word representations from the UK Web Archive corpuscited by 0
  34. 2021How to Do Things without Words: Modeling Semantic Drift of Emojicited by 0
  35. 2021Diachronic Text Mining Investigation of Therapeutic Candidates for COVID-19cited by 0
  36. 2021On the Impact of Temporal Representations on Metaphor Detectioncited by 0
  37. 2023WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Datasetcited by 0
  38. 2024Revealing COVID-19's Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twittercited by 0

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