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Article published In:
Register and social media
Edited by Isobelle Clarke and Jack Grieve
[Register Studies 4:2] 2022
► pp. 138–170

A text typology of social media

ORCID logo | Pontifical Catholic University of Sao Paulo
This paper introduces an initial text typology of social media posts from a multi-dimensional (MD) perspective. Text types are “[g]roupings of text that are similar in their linguistic form” (Biber 1989: 13). This text typology is based on a new MD analysis of social media messages presented in the paper. The corpus consists of 60,000 social media messages in English compiled from Facebook, Twitter, Instagram, Reddit, Telegram, and YouTube. After the texts were cleaned up, the corpus was tagged with the Biber Tagger and post-processed with the Biber Tag Count. Three dimensions of variation were determined, each representing an underlying parameter of variation. Once the texts were scored on each of the dimensions, a k-means cluster analysis was carried out, and the optimal number of clusters was determined using the Cubic Clustering Criterion statistic. A two-way typology was developed based on the dimensional characteristics of each cluster and on careful qualitative analysis of text samples.
Keywords: social media, text typology, multi-dimensional analysis, variation
Article outline
  • 1.Introduction
  • 2.Literature review
  • 3.Methods
  • 4.Dimensions of variation
    • 4.1Dimension 1: Formal, prepared, informational communication
    • 4.2Dimension 2: Informal, interactive, stance-marked discourse
    • 4.3Dimensions 3: Expression of personal attitudes and feelings
    • 4.4Text length
  • 5.Text types
    • 5.1Text type 1: Objective exposition
    • 5.2Text type 2: Subjective expression
    • 5.3Text type distribution
  • 6.Discussion and final remarks
  • Acknowledgements
  • Note
  • References
Published online: 27 October 2022
DOI logo
https://doi.org/10.1075/rs.22008.ber
References (43)
References
Adam, J. M. (2011). A linguística textual – Introdução à análise textual dos discursos [La linguistique textuelle. Introduction à l’analyse textuelle des discours] (M. D. G. Rodrigues, J. G. D. Silva Neto, L. Passeggi, & E. F. Leurquin, Trans.). São Paulo: Cortez.Google Scholar
Beaugrande, R. A. D., & Dressler, W. U. (1981). Introduction to text linguistics. London: Longman. DOI logoGoogle Scholar
Berber Sardinha, T. (2014). Comparing Internet and pre-Internet registers. In T. Berber Sardinha & M. Veirano Pinto (Eds.), Multi-dimensional analysis, 25 years on: A tribute to Douglas Biber (pp. 81–107). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
(2017). Text types in Brazilian Portuguese: A multidimensional perspective. Corpora, 12 (3), 483–515. DOI logoGoogle Scholar
(2018). Dimensions of variation across Internet registers. International Journal of Corpus Linguistics, 23 (2), 125–157. DOI logoGoogle Scholar
(2022). Corpus linguistics and the study of social media: a case study using multi-dimensional analysis. In A. O’Keeffe & M. McCarthy (Eds.), The Routledge handbook of corpus linguistics (pp. 656–674). New York: Routledge. DOI logoGoogle Scholar
Berber Sardinha, T., Kauffmann, C., & Acunzo, C. M. (2014). Dimensions of register variation in Brazilian Portuguese. In T. Berber Sardinha & M. Veirano Pinto (Eds.), Multi-dimensional analysis, 25 years on: A tribute to Douglas Biber (pp. 35–80). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Berber Sardinha, T., & Shimazumi, M. (2021). A text typology of argumentative essays based on the new ICLE v.3. Paper presented at the 11th International Corpus Linguistics Conference 2021, Limerick, Ireland.
Berber Sardinha, T., & Veirano Pinto, M. (Eds.). (2014). Multi-dimensional analysis, 25 years on: A tribute to Douglas Biber. Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
(Eds.). (2019). Multi-dimensional analysis: Research methods and current issues. London: Bloomsbury Academic. DOI logoGoogle Scholar
(2021). A linguistic typology of American television. International Journal of Corpus Linguistics, 26 (1), 127–160. DOI logoGoogle Scholar
Biber, D. (1988). Variation across speech and writing. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
(1989). A typology of English texts. Linguistics, 27 1, 3–43. DOI logoGoogle Scholar
(1993). Representativeness in corpus design. Literary and Linguistic Computing, 8 (4), 243–257. DOI logoGoogle Scholar
(1995). Dimensions of register variation – a cross-linguistic comparison. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Biber, D., & Egbert, J. (2018). Register variation online. Cambridge: Cambridge University Press. 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 / New York: Rodopi.Google Scholar
Bronckart, J. P. (1999). Atividades de linguagem, discursos e textos [Language activities, discourses and texts] (A. R. Machado, Trans.). São Paulo: EDUC.Google Scholar
Charaudeau, P. (2009). Linguagem e discurso: Modos de organização [Langage et Discours – Eléments de sémiolinguistique] (A. M. S. Correa, Trans.). São Paulo, SP: Contexto.Google Scholar
Clarke, I. (2020). Linguistic variation across Twitter and Twitter trolling. (PhD Dissertation). University of Birmigham, Birmingham.
(2022). A Multi-dimensional analysis of English tweets. Language and Literature. Advance online publication. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Egbert, J., & Staples, S. (2019). Doing multi-dimensional analysis in SPSS, SAS, and R. In T. Berber Sardinha & M. Veirano Pinto (Eds.), Multi-dimensional analysis: Research methods and current issues (pp. 125–144). London / New York: Bloomsbury Academic. DOI logoGoogle Scholar
Fairchild, C. (2007). Building the authentic celebrity: The ‘idol’ phenomenon in the attention economy. Popular Music and Society, 30 (3), 355–375. DOI logoGoogle Scholar
Friginal, E., & Hardy, J. A. (2014). Conducting multi-dimensional analysis using SPSS. In T. Berber Sardinha & M. Veirano Pinto (Eds.), Multi-dimensional analysis, 25 years on: A tribute to Douglas Biber (pp. 298–316). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Friginal, E., & Hardy, J. (2019). From factors to dimensions: Interpreting linguistic co-occurrence patterns. In T. Berber Sardinha & M. Veirano Pinto (Eds.), Multi-dimensional analysis: Research methods and current issues (pp. 145–164). London: Bloomsbury Academic. DOI logoGoogle Scholar
Friginal, E., Waugh, O., & Titak, A. (2018). Linguistic variation in Facebook and Twitter posts. In E. Friginal & J. A. Hardy (Eds.), Studies in corpus-based sociolinguistics (pp. 342–362). London: Routledge.Google Scholar
Goulart, L., & Wood, M. (2019). Methodological synthesis of research using multi-dimensional analysis. Journal of Research Design and Statistics in Linguistics and Communication Science, 6 (2), 107–137.Google Scholar
Gray, B. (2019). Tagging and counting linguistic features for multi-dimensional analysis. In T. Berber Sardinha & M. Veirano Pinto (Eds.), Multi-dimensional analysis: Research methods and current issues (pp. 43–66). London / New York: Bloomsbury Academic. DOI logoGoogle Scholar
Holgado-Tello, F. P., Chacon-Moscoso, S., Barbero-Garcia, I., & Vila-Abad, E. (2010). Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Quality & Quantity, 44 1, 153–166. DOI logoGoogle Scholar
Longacre, R. E. (1983). The grammar of discourse. New York: Plenum Press.Google Scholar
Marwick, A. (2015). Instafame: Luxury selfies in the attention economy. Public Culture, 27 (1), 137–160. DOI logoGoogle Scholar
McCulloch, G. (2019). Because Internet: Understanding the new rules of language. New York: Riverhead Books.Google Scholar
O’Halloran, K. (2022). Posthumanism and corpus linguistics. In A. O’Keeffe & M. McCarthy (Eds.), The Routledge handbook of corpus linguistics (pp. 675–692). New York: Routledge. DOI logoGoogle Scholar
Prina Dutra, D., & Berber Sardinha, T. (2018). A linguistic typology of sections in research articles: A multi-dimensional perspective. Paper presented at the Arizona Corpus Linguistics Conference (AZCL), Flagstaff, AZ, USA.
Rüdiger, S., & Dayter, D. (2020). The expanding landscape of corpus-based studies of social media language. In S. Rüdiger & D. Dayter (Eds.), Corpus approaches to social media (Vol. 98, Studies in Corpus Linguistics, pp. 1–13). Amsterdam, New York: John Benjamins. DOI logoGoogle Scholar
Sarle, W. S. (1983). Cubic clustering criterion. Cary: SAS Institute Inc.Google Scholar
Shulman, D. (2017). The presentation of self in contemporary social life. Los Angeles: Sage. DOI logoGoogle Scholar
Tannen, D. (1982). Oral and literate strategies in spoken and written narratives. Language, 58 (1), 1–21. DOI logoGoogle Scholar
Titak, A., & Roberson, A. (2013). Dimensions of web registers: An exploratory multi-dimensional comparison. Corpora, 8 (2), 235–260. DOI logoGoogle Scholar
van der Goot, R. (2019). MoNoise: A multilingual and easy-to-use lexical normalization tool. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. ACL: Florence, pp. 201–206. DOI logoGoogle Scholar
Werlich, E. (1983). A text grammar of English. Heidelberg: Quelle & Meyer.Google Scholar
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