1887
image of Stylistic variation in email
USD
Buy:$35.00 + Taxes

Abstract

Abstract

This study explores how email is partly shaped by writers’ positions within a corporate structure. This stylistic variation is measurable at scale and can be described by messages’ rhetorical organizations and orientations. The modeling was carried out on a subset of the Enron email corpus, which was processed using the dictionary-based tagger DocuScope. The results identify four stylistic variants (Trained/Technical Support, Decision-Making, Everyday Workplace Interaction, and Engaged Planning), each realizing distinctive combinations of features reflective of their communicative functions. In Trained/Technical Support emails, for example, constellations of words and phrases associated with informational production and facilitation are marshaled in fulfilling routine guidance-seeking and guidance-giving tasks. While writers’ positions motivate stylistic tendencies (e.g., members of upper-level management compose a majority of their messages in the Decision-Making style), all writers avail themselves of a variety of styles, depending on audience and purpose, suggesting that learners might benefit from developing adaptable communicative repertoires.

Loading

Article metrics loading...

/content/journals/10.1075/rs.20023.bro
2021-12-23
2022-05-18
Loading full text...

Full text loading...

References

  1. Biber, D.
    (1988) Variation across speech and writing. Cambridge: Cambridge University Press. 10.1017/CBO9780511621024
    https://doi.org/10.1017/CBO9780511621024 [Google Scholar]
  2. Biber, D., & Gray, B.
    (2010) Challenging stereotypes about academic writing: Complexity, elaboration, explicitness. Journal of English for Academic Purposes, 9(1), 2-20. 10.1016/j.jeap.2010.01.001
    https://doi.org/10.1016/j.jeap.2010.01.001 [Google Scholar]
  3. Brown, D. W., & Aull, L. L.
    (2017) Elaborated specificity versus emphatic generality: A corpus-based comparison of higher-and lower-scoring Advanced Placement exams in English. Research in the Teaching of English, 51(4), 394–417.
    [Google Scholar]
  4. Clarke, I. and Grieve, J.
    (2017) Dimensions of abusive language on Twitter. InProceedings of the First Workshop on Abusive Language Online1–10. Vancouver: ACL. 10.18653/v1/W17‑3001
    https://doi.org/10.18653/v1/W17-3001 [Google Scholar]
  5. Clarke, I., & Grieve, J.
    (2019) Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018. PlOS ONE, 14(9), e0222062. 10.1371/journal.pone.0222062
    https://doi.org/10.1371/journal.pone.0222062 [Google Scholar]
  6. Creamer, G., Rowe, R., Hershkop, S., & Stolfo, S. J.
    (2009) Segmentation and automated social hierarchy detection through email network analysis, Berlin, Heidelberg. 10.1007/978‑3‑642‑00528‑2_3
    https://doi.org/10.1007/978-3-642-00528-2_3 [Google Scholar]
  7. De Felice, R., & Garretson, G.
    (2018) Politeness at work in the Clinton email corpus: A first look at the effects of status and gender. Corpus Pragmatics, 2(3), 221–242. 10.1007/s41701‑018‑0034‑2
    https://doi.org/10.1007/s41701-018-0034-2 [Google Scholar]
  8. DeJeu, E., & Brown, D. W.
    (in preparation). Comparing disciplinary variation in student writing across national contexts. InD. W. Brown & D. Wetzel Eds. Between invention and audience: DocuScope and the rhetorical approach to corpus analysis. Amsterdam: John Benjamins Publishing Company. Manuscript in preparation.
    [Google Scholar]
  9. Egbert, J., & Biber, D.
    (2018) Do all roads lead to Rome? Modeling register variation with factor analysis and discriminant analysis. Corpus Linguistics and Linguistic Theory, 14(2), 233. 10.1515/cllt‑2016‑0016
    https://doi.org/10.1515/cllt-2016-0016 [Google Scholar]
  10. Gilbert, E.
    (2012) Phrases that signal workplace hierarchy. Paper presented at theProceedings of the ACM 2012 Conference on Computer Supported Cooperative Work. 10.1145/2145204.2145359
    https://doi.org/10.1145/2145204.2145359 [Google Scholar]
  11. Gray, B.
    (2013) More than discipline: Uncovering multi-dimensional patterns of variation in academic research articles. Corpora, 8(2), 153–181. 10.3366/cor.2013.0039
    https://doi.org/10.3366/cor.2013.0039 [Google Scholar]
  12. Hardy, J. A., & Römer, U.
    (2013) Revealing disciplinary variation in student writing: A multi-dimensional analysis of the Michigan Corpus of Upper-Level Student Papers (MICUSP). Corpora, 8(2), 183–207. 10.3366/cor.2013.0040
    https://doi.org/10.3366/cor.2013.0040 [Google Scholar]
  13. Jonsson, E.
    (2015) Conversational writing: A multidimensional study of synchronous and supersynchronous computer-mediated communication. New York: Peter Lang International Academic Publishers.
    [Google Scholar]
  14. Kaufer, D., Geisler, C., Vlachos, P., & Ishizaki, S.
    (2006) Mining textual knowledge for writing education and research: The docuscope project. InL. Van Waes, M. Leijten, & C. M. Neuwirth (Eds.), Writing and digital media (pp.115–130). Oxford: Elsevier.
    [Google Scholar]
  15. Kaufer, D. S., Ishizaki, S., Butler, B. S., & Collins, J.
    (2004) The power of words: Unveiling the speaker and writer’s hidden craft. Mahwah, N.J.: Lawrence Erlbaum. 10.4324/9781410609748
    https://doi.org/10.4324/9781410609748 [Google Scholar]
  16. Marcellino, W. M.
    (2014) Talk like a marine: USMC linguistic acculturation and civil–military argument. Discourse Studies, 16(3), 385–405. 10.1177/1461445613508895
    https://doi.org/10.1177/1461445613508895 [Google Scholar]
  17. McLachlan, G. J.
    (2004) Discriminant analysis and statistical pattern recognition. Hoboken, N.J.: Wiley & Sons.
    [Google Scholar]
  18. Parry-Giles, S. J., & Kaufer, D. S.
    (2017) Memories of Lincoln and the splintering of American political thought. University Park, PA: The Pennsylvania State University Press.
    [Google Scholar]
  19. Quirk, R., Greenbaum, S., Leech, G., & Svartvik, J.
    (1985) A comprehensive grammar of the English language. London: Pearson Longman.
    [Google Scholar]
  20. Shetty, J., & Adibi, J.
    (2004) The Enron email dataset database schema and brief statistical report. Information sciences institute technical report, University of Southern California, 4(1), 120–128.
    [Google Scholar]
  21. Taguchi, N., Kaufer, D., Gomez-Laich, P., & Zhao, H.
    (2017) A corpus linguistics analysis of on-line peer commentary. Pragmatics and language learning, 14, 357–170.
    [Google Scholar]
  22. Tootalian, J.
    (2017) “To corrupt a man in the midst of a verse”: Ben Jonson and the prose of the world. Ben Jonson Journal, 24(1), 46–72. 10.3366/bjj.2017.0179
    https://doi.org/10.3366/bjj.2017.0179 [Google Scholar]
  23. Tsur, O., Littman, A., & Rappoport, A.
    (2013) Efficient clustering of short messages into general domains. Paper presented at theSeventh International AAAI Conference on Weblogs and Social Media.
    [Google Scholar]
  24. Zhao, H., & Kaufer, D.
    (2013) DocuScope for genre analysis: Potential for assessing pragmatic functions. Technology in interlanguage pragmatics research and teaching, 235–260. 10.1075/lllt.36.12zha
    https://doi.org/10.1075/lllt.36.12zha [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/rs.20023.bro
Loading
/content/journals/10.1075/rs.20023.bro
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error