Article published In:
Register and social media
Edited by Isobelle Clarke and Jack Grieve
[Register Studies 4:2] 2022
► pp. 263287
References (32)
References
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020). The Pushshift Reddit Dataset. Proceedings of the International AAAI Conference on Web and Social Media, 14 (1), 830–839. DOI logoGoogle Scholar
Berber Sardinha, T., & Veirano Pinto, M. (2014). Multi-dimensional analysis, 25 years on: A tribute to Douglas Biber. Philadelphia: John Benjamins. DOI logoGoogle Scholar
Biber, D. (1988). Variation across speech and writing. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
(1994). An analytical framework for register studies. In D. Biber & E. Finegan (Eds.), Sociolinguistic perspectives on register (pp. 31–56). New York: Oxford University Press.Google Scholar
Biber, D., & Conrad, S. (2001). Introduction: Multi-dimensional analysis and the study of register variation. In S. Conrad & D. Biber (Eds.), Variation in English: Multi-dimensional studies (pp. 3–12). Harlow: Pearson Education.Google Scholar
(2009). Register, genre, and style. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Biber, D., Csomay, E., Jones, J. K., & Keck, C. (2004). A corpus linguistic investigation of vocabulary-based discourse units in university registers. In U. Connor & T. A. Upton (Eds.), Applied Corpus Linguistics: A Multidimensional Perspective (pp. 53–72). Rodopi.Google Scholar
Biber, D., & Egbert, J. (2016). Register variation on the searchable web: A multi-dimensional analysis. Journal of English Linguistics, 44 (2), 95–137. DOI logoGoogle Scholar
(2018). Register variation online. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Biber, D., Egbert, J., & Davies, M. (2015). Exploring the composition of the searchable web: A corpus-based taxonomy of web registers. Corpora, 10 (1), 11–45. DOI logoGoogle Scholar
Biber, D., Egbert, J., & Keller, D. (2020). Reconceptualizing register in a continuous situational space. Corpus Linguistics and Linguistic Theory, 16 (3), 581–616. DOI logoGoogle Scholar
Biber, D., & Gray, B. (2013). Being specific about historical change: The influence of sub-register. The Journal of English Linguistics, 41 1, 104–134. DOI logoGoogle Scholar
Biber, D., & Kurjian, J. (2007). Towards a taxonomy of web registers and text types: A multi-dimensional analysis. In M. Hundt, N. Nesselhauf, & C. Biewer (Eds.), Corpus linguistics and the web (pp. 109–132). Amsterdam: Rodopi.Google Scholar
Clarke, I., & Grieve, J. (2017). Dimensions of abusive language on Twitter. In Z. Waseem, W. Hui Kyong, D. Hovy, & J. Tetreault (Eds.), Proceedings of the first workshop on abusive language online (pp. 1–10). Vancouver: Association for Computational Linguistics. DOI logoGoogle Scholar
(2019). Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018. PLoS ONE, 14 (9). DOI logoGoogle Scholar
Conrad, S., & Biber, D. (Eds.). (2001). Variation in English: Multi-dimensional studies. Harlow: Pearson Education.Google Scholar
Covington, M. A., & McFall, J. D. (2010). Cutting the Gordian knot: The moving-average type-token ratio (MATTR). Journal of Quantitative Linguistics, 17 (2), 94–100. DOI logoGoogle Scholar
Egbert, J., Biber, D., & Davies, M. (2015). Developing a bottom-up, user-based method of web register classification. Journal of the Association for Information Science and Technology, 66 (9), 1817–1831. DOI logoGoogle Scholar
Friginal, E. (Ed.) (2013). Twenty-five ears of Biber’s multi-dimensional analysis [Special issue]. Corpora, 8 (2). DOI logoGoogle Scholar
Grice, P. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Speech acts (pp. 41–58). New York: Academic press.Google Scholar
Grieve, J., Biber, D., Friginal, E., & Nekrasova, T. (2011). Variation among blog text types: A multi-dimensional analysis. In A. Mehler, S. Sharoff, & M. Santini (Eds.), Genres on the web: Corpus studies and computational models (pp. 302–322). New York: Springer.Google Scholar
Hess, C. W., Haug, H. T., & Landry, R. G. (1989). The reliability of type-token ratios for the oral language of school age children. Journal of Speech and Hearing Research, 32 1, 536–540. DOI logoGoogle Scholar
Hess, C. W., Sefton, K. M., & Landry, R. G. (1986). Sample size and type-token ratios for oral language of preschool children. Journal of Speech and Hearing Research, 29 1, 129–134. DOI logoGoogle Scholar
Koizumi, R., & In’nami, Y. (2012). Effects of text length on lexical diversity measures: Using short texts with less than 200 tokens. System, 40 (4), 554–564. DOI logoGoogle Scholar
Kubát, M., & Milička, J. (2013). Vocabulary richness measure in genres. Journal of Quantitative Linguistics, 20 (4), 339–349. DOI logoGoogle Scholar
(2020). Using lengthwise scaling to compare feature frequencies across text lengths on Reddit. In S. Rüdiger & D. Dayter (Eds.), Corpus approaches to social media (pp. 111–130). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
(2022). Register variation across text lengths: Evidence from social media. International Journal of Corpus Linguistics. DOI logoGoogle Scholar
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60). DOI logoGoogle Scholar
Shi, Y., & Lei, L. (2020). Lexical richness and text length: An entropy-based perspective. Journal of Quantitative Linguistics, 29 (1), 62–79. DOI logoGoogle Scholar
Titak, A., & Roberson, A. (2013). Dimensions of web registers: An exploratory multi-dimensional comparison. Corpora, 8 (2), 239–271. DOI logoGoogle Scholar