---
title: "The diffusion of scientific terms – tracing individuals' influence in"
description: "The authors use the open RSC 4.0 release of the Royal Society Corpus, which covers… 95.1% accuracy. 46 references, cited by 1 paper here."
authors: ["Yuri Bizzoni", "Stefania Degaetano-Ortlieb", "Katrin Menzel", "Elke Teich"]
year: 2021
venue: "Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature"
source: "https://aclanthology.org/2021.latechclfl-1.14/"
doi: "10.18653/v1/2021.latechclfl-1.14"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/bizzoni-2021-diffusion-scientific-terms-tracing-individuals/"
---
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# The diffusion of scientific terms – tracing individuals' influence in the history of science for English

**Authors:** [Yuri Bizzoni](https://md.research.elamur.ai/free/en/authors/yuri-bizzoni/), [Stefania Degaetano-Ortlieb](https://md.research.elamur.ai/free/en/authors/stefania-degaetano-ortlieb/), [Katrin Menzel](https://md.research.elamur.ai/free/en/authors/katrin-menzel/) and [Elke Teich](https://md.research.elamur.ai/free/en/authors/elke-teich/) · **Year:** 2021 · **Venue:** [Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature](https://md.research.elamur.ai/free/en/venues/latechclfl/) ([LaTeCH-CLfL 2021](https://md.research.elamur.ai/free/en/venues/latechclfl/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.latechclfl-1.14/) · [PDF](https://aclanthology.org/2021.latechclfl-1.14.pdf) · [DOI 10.18653/v1/2021.latechclfl-1.14](https://doi.org/10.18653/v1/2021.latechclfl-1.14) · [OpenAlex](https://openalex.org/W3212768531)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/bizzoni-2021-diffusion-scientific-terms-tracing-individuals/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether methods for measuring influence, usually applied to short-lived settings such as social media, can also reveal who shaped scientific vocabulary over decades. The authors use the open RSC 4.0 release of the Royal Society Corpus, which covers the society's journals from 1665 to 1869. They first rank words by their contribution to Kullback-Leibler Divergence between the 20 years after and the 20 years before a given point. This surfaces terms tied to two 18th-century breakthroughs: the discovery of oxygen and the study of planetary nebulae. Sentences containing those terms, with their authors and dates, are fed to a Hawkes Process model. That model treats each word use as an event that may trigger later uses by other authors, and a heatmap shows influence between pairs of authors. For oxygen, Priestley appears as the innovator, while the early adopter Pearson influences many peers. For nebulae, William Herschel mostly absorbs influence, and his sister Caroline spreads it widely. These patterns agree with accounts by historians of science, which suggests Hawkes Processes can help study influence over long time spans.

## Key points
- Data: RSC 4.0 (1665–1869), with 9,779 texts and about 31.95M tokens. It is annotated with TreeTagger lemmas and POS tags, reaching 95.1% accuracy on spelling normalized with VARD, and it was preprocessed to reduce OCR noise.
- Term selection: KLD of future against past usage, using 20-year windows. Two word sets were built:
  - oxygen: oxygen, phlogiston, dephlogisticated, hydrogen, and related terms;
  - nebula: nebula variants, cluster, star, spectrum, and related terms.
- Model: a self-exciting point process whose intensity sums over earlier events. A decaying kernel is expressed as weighted basis functions, with one weight per pair of authors.
- Filtering: only single-author papers were used, from authors with more than seven papers containing the selected words. Caroline Herschel did not meet this threshold and was added by hand. The nebula analysis covers 1761–1828.
- Other findings:
  - Kater strongly influences Brewster.
  - Sabine influences Christie.
  - Priestley never used the word "oxygen"; Lavoisier coined it.
- The code is publicly available on GitHub.

## Interesting ideas
- Scoring each word's share of the KLD between a "future" window and a "past" window picks out new words and old words with shifted usage, without hand-picking them.
- An event can be modeled as raising the chance of later events through a decaying kernel learned per source–target pair, which separates triggered follow-ups from background noise.
- The authors warn that cascade-shaped patterns show correlation and could come from a hidden common source rather than direct influence.
- They propose validating the detector on synthetic data with planted diffusion patterns, to learn which kinds of influence it can actually recover.
- A frequency cutoff hid an important but rarely published author, Caroline Herschel, and expert knowledge brought her back.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): Running this over incoming corpora could flag lemmas whose behaviour is drifting, so lexicon and morphology entries can be prioritized for review.
- **I2** (world): This kind of excitation scoring could rank which earlier mention or event in a long narrative most plausibly prompts a later one, as an explainable signal for event ordering.
- **I3** (learn): When several witness parsers agree on silver data, the agreement may reflect a shared bias, so negative controls should check whether agreement survives with independent witnesses.
- **I4** (learn): Self-play could generate texts with known, planted relations and measure whether parsing and frame extraction recover them, which gives a ground-truth check.
- **I5** (answer): Thresholds that prune rare entities or readings should be overridable by curated lists, so key but infrequent items in fables or tests are not silently dropped.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Saarland University](https://md.research.elamur.ai/free/en/universities/saarland-university/) — University, Saarbrücken, Germany · 149 works · 18,349 citations

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🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works

## Cite

Cite as: Yuri Bizzoni, Stefania Degaetano-Ortlieb, Katrin Menzel and Elke Teich. 2021. The diffusion of scientific terms – tracing individuals' influence in the history of science for English. Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{bizzoni2021diffusion,
  title = {{The diffusion of scientific terms – tracing individuals' influence in the history of science for English}},
  author = {Yuri Bizzoni, Stefania Degaetano-Ortlieb, Katrin Menzel and Elke Teich},
  booktitle = {Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
  year = {2021},
  doi = {10.18653/v1/2021.latechclfl-1.14},
  url = {https://doi.org/10.18653/v1/2021.latechclfl-1.14}
}
```

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- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/9/)

## References

46 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Emmanuel Bacry, Iacopo Mastromatteo, and Jean-François Muzy. 2015. Hawkes processes in finance. Market Microstructure and Liquidity, 1(01):1550005. [↗](https://doi.org/10.1142/s2382626615500057)
2. Alistair Baron and Paul Rayson. 2008. [VARD 2: A tool for dealing with spelling variation in historical corpora](https://md.research.elamur.ai/free/en/papers/baron-2008-vard2-tool-dealing-spelling-variation/). In Proceedings of the Postgraduate Conference in Corpus Linguistics, Birmingham, UK.
3. Douglas Biber and Bethany Gray. 2016. Grammatical complexity in academic English: Linguistic change in writing. Studies in English Language. Cambridge University Press, Cambridge, UK. [find](https://scholar.google.com/scholar?q=Douglas+Biber+and+Bethany+Gray%2E+2016%2E+Grammatical+complexity+in+academic+English%3A+Linguistic+change+in+writing%2E+Studies+in+English+Language%2E+Cambridge+University+Press%2C+Cambridge%2C+UK%2E)
4. Yuri Bizzoni, Stefania Degaetano-Ortlieb, Peter Fankhauser, and Elke Teich. 2020. Linguistic variation and change in 250 years of English scientific writing: A data-driven approach. Frontiers in Artificial Intelligence, 3:73. [↗](https://doi.org/10.3389/frai.2020.00073)
5. Anna Bonnet, Miguel Herrera, and Maxime Sangnier. 2021. Maximum likelihood estimation for Hawkes processes with self-excitation or inhibition. arXiv preprint arXiv:2103.05299. [↗](https://arxiv.org/abs/2103.05299)
6. Wen-Hao Chiang, Xueying Liu, and George Mohler. 2021. Hawkes process modeling of covid-19 with mobility leading indicators and spatial covariates. International Journal of Forecasting, online print. [↗](https://doi.org/10.1016/j.ijforecast.2021.07.001)
7. Noel G. Coley. 2003. George Pearson MD, FRS (1751-1828): The greatest chemist in England? Notes and Records of the Royal Society of London, 57(2):161– 175. [↗](https://doi.org/10.1098/rsnr.2003.0203)
8. Alicia Cork, Richard Everson, Mark Levine, and Miriam Koschate. 2020. Using computational techniques to study social influence online. Group Processes & Intergroup Relations, 23(6):808–826. [↗](https://doi.org/10.1177/1368430220937354)
9. José Da Fonseca and Riadh Zaatour. 2014. Hawkes process: Fast calibration, application to trade clustering, and diffusive limit. Journal of Futures Markets, 34(6):548–579. [↗](https://doi.org/10.1002/fut.21644)
10. Hridoy Sankar Dutta, Vishal Raj Dutta, Aditya Adhikary, and Tanmoy Chakraborty. 2020. Hawkes-Eye: Detecting fake retweeters using Hawkes process and topic modeling. IEEE Transactions on Information Forensics and Security, 15:2667–2678. [find](https://scholar.google.com/scholar?q=Hridoy+Sankar+Dutta%2C+Vishal+Raj+Dutta%2C+Aditya+Adhikary%2C+and+Tanmoy+Chakraborty%2E+2020%2E+Hawkes%2DEye%3A+Detecting+fake+retweeters+using+Hawkes+process+and+topic+modeling%2E+IEEE+Transactions+on+Information+Fo)
11. Jacob Eisenstein, Brendan O’Connor, Noah A. Smith, and Eric P. Xing. 2014. Diffusion of lexical change in social media. PLOS ONE, 9(11):1–13. [↗](https://doi.org/10.1371/journal.pone.0113114)
12. Juan V. Escobar. 2020. A Hawkes process model for the propagation of COVID-19: Simple analytical results. Europhysics Letters (EPL), 131(6):68005. [↗](https://doi.org/10.1209/0295-5075/131/68005)
13. Stefan Fischer, Jörg Knappen, Katrin Menzel, and Elke Teich. 2020. The Royal Society Corpus 6.0. Providing 300+ years of scientific writing for humanistic study. In Proceedings of the the 12th Language Resources and Evaluation Conference (LREC), Marseille, France. [find](https://scholar.google.com/scholar?q=Stefan+Fischer%2C+J%C3%B6rg+Knappen%2C+Katrin+Menzel%2C+and+Elke+Teich%2E+2020%2E+The+Royal+Society+Corpus+6%2E0%2E+Providing+300%2B+years+of+scientific+writing+for+humanistic+study%2E+In+Proceedings+of+the+the+12th+Languag)
14. Aileen Fyfe, Flaminio Squazzoni, Didier Torny, and Pierpaolo Dondio. 2020. Managing the growth of peer review at the Royal Society journals, 1865-1965. Science, Technology, and Human Values, 45(3):405–429. [↗](https://doi.org/10.1177/0162243919862868)
15. Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna Wallach, Fernando Diaz, and Jacob Eisenstein. 2016. The social dynamics of language change in online networks. In International Conference on Social Informatics, pages 41–57. Springer. [↗](https://doi.org/10.48550/arxiv.1609.02075)
16. Fangjian Guo, Charles Blundell, Hanna Wallach, and Katherine Heller. 2015. The Bayesian Echo Chamber: Modeling social influence via linguistic accommodation. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, volume 38 of Proceedings of Machine Learning Research, pages 315–323, San Diego, California, USA. PMLR. [↗](https://doi.org/10.48550/arxiv.1411.2674)
17. M.A.K. Halliday. 1988. On the language of physical science. In Mohsen Ghadessy, editor, Registers of Written English: Situational Factors and Linguistic Features, pages 162–177. Pinter, London. [find](https://scholar.google.com/scholar?q=M%2EA%2EK%2E+Halliday%2E+1988%2E+On+the+language+of+physical+science%2E+In+Mohsen+Ghadessy%2C+editor%2C+Registers+of+Written+English%3A+Situational+Factors+and+Linguistic+Features%2C+pages+162%E2%80%93177%2E+Pinter%2C+London%2E)
18. Alan G Hawkes. 1971. Point spectra of some mutually exciting point processes. Journal of the Royal Statistical Society: Series B (Methodological), 33(3):438– 443. [↗](https://doi.org/10.1111/j.2517-6161.1971.tb01530.x)
19. Andrew Daw, Antonio Castellanos, Galit B Yom-Tov, Jamol Pender, and Leor Gruendlinger. 2020. The co-production of service: Modeling service times in contact centers using Hawkes processes. arXiv preprint arXiv:2004.07861. [↗](https://arxiv.org/abs/2004.07861)
20. Stefania Degaetano-Ortlieb and Elke Teich. 2018. Using relative entropy for detection and analysis of periods of diachronic linguistic change. In Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 22– 33, Santa Fe, New Mexico. Association for Computational Linguistics. [↗](https://doi.org/10.22028/d291-37928)
21. Stefania Degaetano-Ortlieb and Elke Teich. 2019. Toward an optimal code for communication: The case of scientific English. Corpus Linguistics and Linguistic Theory, 0(0):1–33. Online print. [↗](https://doi.org/10.1515/cllt-2018-0088)
22. Alan G. Hawkes. 1973. Cluster models for earthquakes-regional comparisons. Bull. Int. Stat. Inst., 45(3):454–461. [find](https://scholar.google.com/scholar?q=Alan+G%2E+Hawkes%2E+1973%2E+Cluster+models+for+earthquakes%2Dregional+comparisons%2E+Bull%2E+Int%2E+Stat%2E+Inst%2E%2C+45%283%29%3A454%E2%80%93461%2E)
23. Alan G Hawkes. 2018. Hawkes processes and their applications to finance: a review. Quantitative Finance, 18(2):193–198. [↗](https://doi.org/10.1080/14697688.2017.1403131)
24. Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, and Yan Liu. 2015. Hawkestopic: A joint model for network inference and topic modeling from text-based cascades. In Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 871–880, Lille, France. PMLR. [find](https://scholar.google.com/scholar?q=Xinran+He%2C+Theodoros+Rekatsinas%2C+James+Foulds%2C+Lise+Getoor%2C+and+Yan+Liu%2E+2015%2E+Hawkestopic%3A+A+joint+model+for+network+inference+and+topic+modeling+from+text%2Dbased+cascades%2E+In+Proceedings+of+the+32nd+)
25. Michael Hoskin. 1979. Goodricke, Pigott and the quest for variable stars. Journal for the History of Astronomy, 10:23–24. [↗](https://doi.org/10.1177/002182867901000103)
26. Hannah Kermes, Stefania Degaetano-Ortlieb, Ashraf Khamis, Jörg Knappen, and Elke Teich. 2016. The Royal Society Corpus: From uncharted data to corpus. In Proceedings of the 10th LREC, Portorož, Slovenia. [find](https://scholar.google.com/scholar?q=Hannah+Kermes%2C+Stefania+Degaetano%2DOrtlieb%2C+Ashraf+Khamis%2C+J%C3%B6rg+Knappen%2C+and+Elke+Teich%2E+2016%2E+The+Royal+Society+Corpus%3A+From+uncharted+data+to+corpus%2E+In+Proceedings+of+the+10th+LREC%2C+Portoro%C5%BE%2C+Sloven)
27. Solomon Kullback and Richard A. Leibler. 1951. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79–86. [↗](https://doi.org/10.1214/aoms/1177729694)
28. Hui Li and Sourav S Bhowmick. 2020. Brunch: branching structure inference of hybrid multivariate Hawkes processes with application to social media. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 553–566. Springer. [find](https://scholar.google.com/scholar?q=Hui+Li+and+Sourav+S+Bhowmick%2E+2020%2E+Brunch%3A+branching+structure+inference+of+hybrid+multivariate+Hawkes+processes+with+application+to+social+media%2E+In+Pacific%2DAsia+Conference+on+Knowledge+Discovery+an)
29. Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, and Hongyuan Zha. 2014. Identifying and labeling search tasks via query-based Hawkes processes. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, page 731–740, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Liangda+Li%2C+Hongbo+Deng%2C+Anlei+Dong%2C+Yi+Chang%2C+and+Hongyuan+Zha%2E+2014%2E+Identifying+and+labeling+search+tasks+via+query%2Dbased+Hawkes+processes%2E+In+Proceedings+of+the+20th+ACM+SIGKDD+International+Confe)
30. Scott Linderman and Ryan Adams. 2014. Discovering latent network structure in point process data. In Proceedings of the 31st International Conference on Machine Learning, pages 1413–1421. PLMR. [↗](https://doi.org/10.48550/arxiv.1402.0914)
31. Michal Lukasik, PK Srijith, Duy Vu, Kalina Bontcheva, Arkaitz Zubiaga, and Trevor Cohn. 2016. Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 393–398. [find](https://scholar.google.com/scholar?q=Michal+Lukasik%2C+PK+Srijith%2C+Duy+Vu%2C+Kalina+Bontcheva%2C+Arkaitz+Zubiaga%2C+and+Trevor+Cohn%2E+2016%2E+Hawkes+processes+for+continuous+time+sequence+classification%3A+an+application+to+rumour+stance+classificati)
32. Lee T. MacDonald. 2015. Making kew observatory: the Royal Society, the British Association and the politics of early Victorian science. The British Journal for the History of Science, 48(3):409–433. [↗](https://doi.org/10.1017/s0007087415000023)
33. Katrin Menzel, Jörg Knappen, and Elke Teich. 2021. Generating linguistically relevant metadata for the Royal Society Corpus. Challenges in Combining Structured and Unstructured Data in Corpus Development - Special Issue of Research in Corpus Linguistics, Asociación Española de Lingüística de Corpus (AELINCO), 9(1):1–18. [↗](https://doi.org/10.32714/ricl.09.01.02)
34. Marcello Rambaldi, Paris Pennesi, and Fabrizio Lillo. 2015. Modeling foreign exchange market activity around macroeconomic news: Hawkes-process approach. Physical Review E, 91(1):012819. [↗](https://doi.org/10.1103/physreve.91.012819)
35. Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, and Lexing Xie. 2017. Hawkes processes for events in social media, page 191–218. Association for Computing Machinery and Morgan & Claypool. [find](https://scholar.google.com/scholar?q=Marian%2DAndrei+Rizoiu%2C+Young+Lee%2C+Swapnil+Mishra%2C+and+Lexing+Xie%2E+2017%2E+Hawkes+processes+for+events+in+social+media%2C+page+191%E2%80%93218%2E+Association+for+Computing+Machinery+and+Morgan+%26+Claypool%2E)
36. Everette M. Rogers. 2010. Diffusion of Innovation. The Free Press, New York. [find](https://scholar.google.com/scholar?q=Everette+M%2E+Rogers%2E+2010%2E+Diffusion+of+Innovation%2E+The+Free+Press%2C+New+York%2E)
37. Camilla Mørk Røstvik and Aileen Fyfe. 2018. Ladies, gentlemen, and scientific publication at the Royal Society, 1945–1990. Open Library of Humanities, 4(1):1–40. [↗](https://doi.org/10.16995/olh.265)
38. Sandeep Soni, Lauren F. Klein, and Jacob Eisenstein. 2021. Abolitionist networks: Modeling language change in nineteenth-century activist newspapers. Journal of Cultural Analytics, 6(1). [↗](https://doi.org/10.22148/001c.18841)
39. Sandeep Soni, Shawn Ling Ramirez, and Jacob Eisenstein. 2019. Detecting social influence in event cascades by comparing discriminative rankers. In The 2019 ACM SIGKDD Workshop on Causal Discovery, pages 78–99. PMLR. [↗](https://doi.org/10.48550/arxiv.1802.06138)
40. Elke Teich, Peter Fankhauser, Stefania Degaetano-Ortlieb, and Yuri Bizzoni. 2021. Less is more/more diverse: On the communicative utility of linguistic conventionalization. Frontiers in Communication, 5:142. [↗](https://doi.org/10.3389/fcomm.2020.620275)
41. Emily Winterburn. 2015. Learned modesty and the first lady’s comet: A commentary on Caroline Herschel (1787) ’An account of a new comet’. Philosophical Transactions of the Royal Society A, 373(2039):20140210. [↗](https://doi.org/10.1098/rsta.2014.0210)
42. Steve Y Yang, Anqi Liu, Jing Chen, and Alan Hawkes. 2018. Applications of a multivariate Hawkes process to joint modeling of sentiment and market return events. Quantitative Finance, 18(2):295–310. [↗](https://doi.org/10.1080/14697688.2017.1403156)
43. Wei Zhang, Fan Bu, Derek Owens-Oas, Katherine Heller, and Xiaojin Zhu. 2018. Learning root source with marked multivariate Hawkes processes. arXiv preprint arXiv:1809.03648. [↗](https://arxiv.org/abs/1809.03648)
44. George Mohler, Jeremy Carter, and Rajeev Raje. 2018. Improving social harm indices with a modulated Hawkes process. International Journal of Forecasting, 34(3):431–439. [↗](https://doi.org/10.1016/j.ijforecast.2018.01.006)
45. Mark E.J. Newman. 2001. Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(016131). [find](https://scholar.google.com/scholar?q=Mark+E%2EJ%2E+Newman%2E+2001%2E+Scientific+collaboration+networks%2E+I%2E+Network+construction+and+fundamental+results%2E+Physical+Review+E%2C+64%28016131%29%2E)
46. Dong Nguyen, A. Seza Doğruöz, Carolyn P. Rosé, and Franciska De Jong. 2016. [Computational sociolinguistics: A survey](https://md.research.elamur.ai/free/en/papers/nguyen-2016-computational-sociolinguistics-survey/). Computational Linguistics, 42(3):537–593. [↗](https://doi.org/10.1162/coli_a_00258)

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