Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018

English version

Autoři: Isobelle Clarke ^aff001; Jack Grieve ^aff001
Působiště autorů: Department of English Language and Linguistics, University of Birmingham, Birmingham, England, United Kingdom ^aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222062

Souhrn

Twitter was an integral part of Donald Trump’s communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections.

Klíčová slova:

Elections – Language – Linguistic morphology – Semantics – Social communication – Social media – Sociolinguistics – Twitter

Zdroje

1. Graham D. Which Republicans Oppose Donald Trump? A Cheat Sheet [Internet]. The Atlantic. 2019 [cited 10 April 2018]. Available from: https://www.theatlantic.com/politics/archive/2016/11/where-republicans-stand-on-donald-trump-a-cheat-sheet/481449/

2. Isenstadt A, Cheney K, Harris J. Stop Trump movement goes to work on GOP leaders [Internet]. POLITICO. 2019 [cited 10 April 2018]. Available from: https://www.politico.com/story/2016/04/stop-trump-movement-gop-222315

3. Marx J. Twitter and the 2016 Presidential Election. Critique: A Worldwide Student. J Polit. 2017:17–37.

4. Orme B. TRUMPED: How US media played the wrong hand on right-wing success. In: White A, editors. Ethics in the News: EJN Report on Challenges for Journalism in the Post-truth Era. London: Ethical Journalism Network; 2017. pp. 6–10.

5. Reuters. Here’s how much of his own money Donald Trump spent on his campaign. [Internet]. Fortune. 2016 [cited 23 March 2018]. Available from: http://fortune.com/2016/12/09/donald-trump-campaign-spending/

6. Quelch J, Teixeira T. The Twitter election. [Internet]. Harvard Business School. 2017 [cited 23 March 2018]. Available from: https://hbswk.hbs.edu/item/the-twitter-election

7. Hall K, Goldstein DM, Ingram MB. The hands of Donald Trump: Entertainment, gesture, spectacle. Journal of Ethnographic Theory. 2016;6(2):71–100.

8. MacWilliams, 2016; MacWilliams MC. Who decides when the party doesn’t? Authoritarian voters and the rise of Donald Trump. PS Polit Sci Polit. 2016;49(4):716–21.

9. Ott BL. The age of Twitter: Donald J. Trump and the politics of debasement. Crit Stud Media Commun. 2017;34(1):59–68.

10. Ahmadian S, Azarshahi S, Paulhus DL. Explaining Donald Trump via communication style: Grandiosity, informality, and dynamism. Pers Individ Dif. 2017;107:49–53.

11. Vrana L, Schneider G. Saying Whatever It Takes: Creating and Analyzing Corpora from US Presidential Debate Transcripts. Presentation presented at; 2017; The 9thInternational Corpus Linguistics Conference 2017 (CL2017), Birmingham, UK.

12. Schmidbauer H, Rösch A, Stieler F. The 2016 US presidential election and media on Instagram: who was in the lead? Comput Human Behav. 2017;81:148–60.

13. Morris DS. Twitter versus the traditional media: A survey experiment comparing public perceptions of campaign messages in the 2016 U.S. Presidential election. Soc Sci Comput Rev. 2016:1–13.

14. Wells C, Shah DV, Pevehouse JC, Yang J, Pelled A, Boehm F, et al. How Trump drove coverage to the nomination: hybrid media campaigning. Polit Commun. 2016;33(4):669–76.

15. Yaqub U, Chun SA, Atluri V, Vaidya J. Analysis of political discourse on Twitter in the context of the 2016 US presidential elections. Gov Inf Q. 2017;34(4):613–26.

16. Potts L. Problem definition and causal attribution during the Republican National Convention: How #MAGA discourse on Twitter framed America’s problems and the people responsible. [MA] Purdue University; 2017

17. Asenas JJ, Hubble B. Trolling free speech rallies: social media practices and the (un)democratic spectacle of dissent. The Journal of Culture and Education. 2018;17(2):36–52.

18. Lee J, Xu W. The more attacks, the more retweets: trump’s and Clinton’s agenda setting on Twitter. Public Relat Rev. 2018;44(2):201–13.

19. Wang Y, Luo J, Niemi R, Li Y, Hu T. Catching Fire via ‘Likes’: Inferring Topic Preferences of Trump Followers on Twitter. AAAI [Internet]. Cologne, Germany: ICWSM; 2016 [cited 8 January 2019]:719–722. Available from: http://www.aaai.org/Library/ICWSM/icwsm16contents.php

20. Harris M. A media post-mortem on the 2016 Presidential election. [Internet]. Mediaquant. 2019 [cited 11 April 2018]. Available from: https://www.mediaquant.net/2016/11/a-media-post-mortem-on-the-2016-presidential-election/

21. Crockett Z. What I learned analyzing 7 months of Donald Trump's tweets [Internet]. Vox. 2016 [cited 23 July 2018]. Available from: https://www.vox.com/2016/5/16/11603854/donald-trump-twitter

22. Leonhardt D, Thompson S. Opinion | President Trump’s Lies, the Definitive List [Internet]. Nytimes.com. 2017 [cited 17 July 2018]. Available from: https://www.nytimes.com/interactive/2017/06/23/opinion/trumps-lies.html

23. Lewandowsky S, Ecker UK, Cook J. Beyond misinformation: understanding and coping with the post-truth era. J Appl Res Mem Cogn. 2017;6(4):353–69.

24. Ursin CN. Trump Tweets as Examples of Common Logical Fallacies. [Internet]. Medium. 2017 [cited 27 November 2018]. Available from: https://medium.com/@chelseaninaursin/trump-tweets-as-examples-of-common-logical-fallacies-b01492932bdc

25. Manggong L. Language and culture in the case of Merriam-webster’s correction over President Trump’s Tweets. Presentation presented at; 2017; the International Seminar on Language Maintenance and Shift, Semarang, Indonesia.

26. Shen L. Donald Trump finds someone new to insult on Twitter every 42 hours. [Internet]. Fortune. 2016 [cited 10 April 2018]. Available from: http://fortune.com/2016/10/25/donald-trump-twitter-insults-new-york-times/

27. Lee J, Quealy K. The 551 People, Places and Things Donald Trump Has Insulted on Twitter: A Complete List [Internet]. Nytimes.com. 2018 [cited 29 November 2018]. Available from: https://www.nytimes.com/interactive/2016/01/28/upshot/donald-trump-twitter-insults.html

28. Zimmer B. Looking for the Linguistic Smoking-Gun in a Trump Tweet. [Internet]. The Atlantic. 2017 [cited 11 April 2018]. Available from: https://www.theatlantic.com/entertainment/archive/2017/12/looking-for-the-linguistic-smoking-gun-in-a-trump-tweet/547361/

29. Robinson D. Text analysis of Trump’s tweets confirms he writes only the (angrier) Android half. [Internet]. Variance Explained. 2016 [cited 10 April 2018]. Available from: http://varianceexplained.org/r/trump-tweets/

30. Grieve J, Clarke I. Stylistic variation in the @realDonaldTrump Twitter account and the stylistic typicality of the Pled Tweet. [Internet]. Rpubs. 2017 [cited 11 April 2018]. Available from: http://rpubs.com/jwgrieve/340342/

31. Roenneberg T. Twitter as a means to study temporal behaviour. Curr Biol. 2017 Sep;27(17):R830–2. doi: 10.1016/j.cub.2017.08.005 28898641

32. Brown B. Trump Twitter Archive [Internet]. Trumptwitterarchive.com. 2019 [cited 20 February 2018]. Available from: http://www.trumptwitterarchive.com/

33. Gligorić K, Anderson A, West R. How Constraints Affect Content: The Case of Twitter’s Switch from 140 to 280 Characters. Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM) [Internet]. Association for the Advancement of Artificial Intelligence; 2018 [cited 11 June 2019]:1–5. Available from: http://www.cs.toronto.edu/~ashton/pubs/twitter-constraints-icwsm2018.pdf

34. McCormick R. Donald Trump is using an iPhone now. [Internet]. The Verge. 2017 [cited 10 April 2018]. Available from: https://www.theverge.com/2017/3/29/15103504/donald-trump-iphone-using-switched-android

35. Biber D. Variation across speech and writing. Cambridge: Cambridge University Press; 1988. https://doi.org/10.1017/CBO9780511621024.

36. Stamatatos E. A survey of modern authorship attribution methods. Journal of the American Society for information Science and Technology. 2009 Mar;60(3):538–56.

37. Tausczik YR, Pennebaker JW. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology. 2010 Mar;29(1):24–54.

38. Santini M. Automatic Text Analysis: Gradations of Text Types in Web Pages. The 10 ESSLLI Student Session Edinburgh, UK: 2005 [cited 8 January 2019]. pp. 276–285.

39. Besnier N. The linguistic relationships of spoken and written Nukulaelae registers. Language. 1988;64(4):707–36.

40. Kim Y, Biber D. A corpus-based analysis of register variation in Korean. In: Biber D, Finegan E, editors. Sociolinguistic Perspectives on Register. 1994. pp. 157–81.

41. Biber D, Hared M. Linguistic correlates of the transition to literacy in Somali: Language adaptation in six press registers. In: Biber D, Finegan E, editors. Sociolinguistic Perspectives on Register. 1994. pp. 182–216.

42. Biber D, Davies M, Jones JK, Tracy-Ventura N. Spoken and written register variation in Spanish: A multi-dimensional analysis. Corpora. 2006;1(1):1–37.

43. White M. Language in job interviews: Differences relating to success and socioeconomic variables. [Ph.D.]. Northern Arizona University; 1994.

44. Friginal E. The Language of Outsourced Call Centers: A Corpus-Based Study of Cross Cultural Interaction. Amsterdam: John Benjamins Publishing Company; 2009. https://doi.org/10.1075/scl.34.

45. Thompson P., Hunston S., Murakami A., Vajn D. Multi-dimensional analysis, text constellations, and interdisciplinary discourse. International Journal of Corpus Linguistics. 2017;22(2):153–186.

46. Biber D, Egbert J. Register variation on the searchable web: A multi-dimensional analysis. J Eng Linguist. 2016;44(2):95–137.

47. Collot M, Belmore N. Electronic language: A new variety of English. In: Herring SC, editor. Computer-mediated communication: Linguistic, social and cross-cultural perspectives. Amsterdam: Benjamins; 1996. pp. 13–28.

48. Grieve J, Biber D, Friginal E, Nekrasova T. Variation among blogs: A multidimensional analysis. In: Mehler A, Sharoff S, Santini M, editors. Genres on the Web: Computational Models and Empirical Studies. New York: Springer-Verlag; 2010. pp. 303–22.

49. Passonneau RJ, Ide N, Su S, Stuart J. Biber Redux: Reconsidering Dimensions of Variation in American English. COLING [Internet]. Dublin, Ireland: ACL; 2014 [cited 8 January 2019]. pp. 565–576. Available from: http://aclweb.org/anthology/C14-1000

50. Biber D. Representativeness in corpus design. Lit Linguist Comput. 1993;8(4):243–57.

51. Friginal E, Waugh O, Titak A. Linguistic variation in Facebook and Twitter posts. In: Friginal E, editor. Studies in Corpus-Based Sociolinguistics. New York: Routledge; 2018. pp. 342–63.

52. Coats S. Grammatical feature frequencies of English on Twitter in Finland. In: Squires L, editor. English in computer-mediated communication: Variation, representation, and change. Berlin: de Gruyter Mouton; 2016. pp. 179–210.

53. Titak A, Roberson A. Dimensions of web registers: an exploratory multi-dimensional comparison. Corpora. 2013;8(2):235–60.

54. Clarke I, Grieve J. Dimensions of Abusive Language on Twitter. The First Workshop on Abusive Language Online [Internet]. Vancouver: ACL; 2017 [cited 8 January 2019]. pp. 1–10. Available from: https://aclanthology.coli.uni-saarland.de/papers/W17-3000/w17-3000

55. Grieve J, Clarke I. Tracking stylistic change on the Donald Trump Twitter Account. Presentation presented at; 2018; the 14th American Association for Corpus Linguistics Conference. Atlanta, Georgia.

56. Clarke I. Stylistic variation in Twitter trolling. In: Golbeck J, editor. Online Harassment: Human-Computer Interaction Series. Manhattan (NY): Springer International Publishing; 2018. pp. 151–78.

57. Clarke I. Functional linguistic variation in Twitter trolling. International Journal of Speech Language and the Law. 2019;0(0).

58. Greenacre M, Pardo R. Multiple Correspondence Analysis of a Subset of Response Categories. In: Greenacre M, Blasius J, editors. Multiple Correspondence Analysis and Related Methods. Boca Raton (FL): Chapman and Hall/CRC Press; 2006. pp. 197–217.

59. Le Roux B, Rouanet H. Multiple Correspondence Analysis. California: SAGE Publications, Inc.; 2010. https://doi.org/10.4135/9781412993906.

60. Benzécri JP. Sur le calcul des taux d’inertie dans l’analyse d’un questionnaire. Cah Anal Donnees. 1979;4:377–8.

61. Tummers J, Speelman D, Geeraerts D. Multiple Correspondence Analysis as Heuristic Tool to Unveil Confounding Variables in Corpus Linguistics. 11th International Conference on Statistical Analysis of Textual. 2016. p. 923–936.

62. Glynn D. Polysemy, syntax, and variation: A usage-based method for Cognitive Semantics. In: Evans V, Pourcel S, ed. by. New Directions in Cognitive Linguistics. Amsterdam: John Benjamins Publishing Co; 2009. pp. 77–106.

63. Padilla-Meléndez A, del Aguila-Obra A, Garrido-Moreno A. Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education. 2013;63:306–317.

64. Gimpel K, Schneider N, O'Connor B, Das D, Mills D, Eisenstein J et al. Part-of-Speech tagging for Twitter: Annotation, features, and experiments. ACL [Internet]. Portland: ACL; 2011 [cited 8 January 2019]. pp. 19–24. Available from: http://aclweb.org/anthology/P11-1000

65. Zappavigna M. Ambient affiliation: A linguistic perspective on Twitter. New Media & Society. 2011;13(5):788–806.

66. Honeycutt C, Herring S. Beyond Microblogging: Conversation and Collaboration via Twitter. Forty-Second Hawai’i International Conference on System Sciences. Los Alamitos, CA: IEEE Press; 2009. pp. 1–10.

67. Deumert A. Semantic Change, the Internet: and Text Messaging. Encyclopedia of Language & Linguistics. 2006:121–124.

68. Blodgett S, Green L, O'Connor B. Demographic Dialectal Variation in Social Media: A Case Study of African-American English. the 2016 Conference on Empirical Methods in Natural Language Processing [Internet]. ACL; 2016 [cited 11 June 2019]. pp. 1119–1130. Available from: https://aclweb.org/anthology/D16-1120

69. Liu F, Weng F, Jiang X. A Broad-Coverage Normalization System for Social Media Language. 50th Annual Meeting of the Association for Computational Linguistics [Internet]. ACL; 2012 [cited 11 June 2019]. pp. 1035–1044. Available from: https://www.aclweb.org/anthology/P12-1109

70. Parkins R. Gender and Emotional Expressiveness: An Analysis of Prosodic Features in Emotional Expression. Griffith Working Papers in Pragmatics and Intercultural Communication. 2012;5(1):46–54.

71. Al Rashdi F. Functions of emojis in WhatsApp interaction among Omanis. Discourse, Context & Media. 2018;26:117–126.

72. Werry C. Linguistic and interactional features of Internet Relay Chat. Pragmatics & Beyond New Series. 1996:47–63.

73. Biber D. Multi-Dimensional analysis: A personal history. In: Sardinha TB, Pinto MV, editors. Multi Dimensional Analysis, 25 years on: A Tribute to Douglas Biber. Amsterdam: John Benjamins Publishing Company; 2014. pp. xxvi–xxxviii.

74. Bell A. Language style as audience design. Lang Soc. 1984;13(2):145–204.

75. Page R. The linguistics of self-branding and micro-celebrity in Twitter: the role of hashtags. Discourse Commun. 2012;6(2):181–201.

76. Martin JR, White PR. The Language of Evaluation: Appraisal in English. Basingstoke, Hampshire: Palgrave MacMillan; 2005. https://doi.org/10.1057/9780230511910.

77. White PR. Beyond modality and hedging: A dialogic view of the language of intersubjective stance. Text. 2003;23(2):259–84.

78. Toosi N. Trump misfires on Mosul. [Internet]. POLITICO. 2016 [cited 3 December 2018]. Available from: https://www.politico.com/story/2016/10/donald-trump-mosul-iraq-military-230240

79. Keltner D. The Power Paradox [Internet]. Greater Good. 2007 [cited 14 November 2018]. Available from: https://greatergood.berkeley.edu/article/item/power_paradox

80. van Dijk TA. Discourse and manipulation. Discourse Soc. 2006;17(3):359–83.

81. Staples L. Roots to Power: A manual for grassroots organizing. 3 ed. Santa Barbara, CA: Praeger; 2016.

82. Sampathkumar M. The tweets that have defined Donald Trump’s presidency. [Internet]. The Independent. 2018 [cited 3 January 2019]. Available from: https://www.independent.co.uk/news/world/americas/us-politics/donald-trump-twitter-president-first-year-a8163791.html

83. Cillizza C. Donald Trump's Twitter feed is getting more and more bizarre [Internet]. CNN. 2019 [cited 3 January 2019]. Available from: https://edition.cnn.com/2018/06/18/politics/trump-tweets/index.html

84. Francis RD. Him, not her: why working-class white men reluctant about Trump still made him President of the United States. Sociological Research for a Dynamic World. 2018;4:1–11.

85. Kilibarda K, Roithmayr D. The Myth of the Rust Belt Revolt [Internet]. Slate Magazine. 2016 [cited 3 January 2019]. Available from: https://slate.com/news-and-politics/2016/12/the-myth-of-the-rust-belt-revolt.html

Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018

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