Communications in Humanities Research ISSN: 2753-7072
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Research Article

Gendered Distinctions of Interjection Usage in the YouTube Context

Xiaohua WangSchool of International Studies, Hangzhou Normal University, Hangzhou, 311121, China* Jingjing ZhengSchool of Chengyi, Jimei University, Xiamen, 361021, China Ziqi LiSchool of Foreign Languages, Shandong Normal University, Jinan, 250000, China

* Corresponding author: [email protected]

Abstract

Linguistic gender research has originated from Western academia and gone through centennial and continual development throughout the birth and growth of linguistics. Lately, the interjection words, serving as an observable but marginal lexical index, have played an increasingly significant role in many aspects of linguistic studies for recent decades, for example, in the way the experts analyze the vocal expression of emotion, study the cross-lingual analysis in Japanese and English, and many other applications. This paper adopts three classifications of interjection words – “noise-like” primary interjections, “word-like” primary interjections, and secondary interjections – to further explore the relationship between gender and each classification of interjections on the platform of YouTube. The randomly chosen videos of YouTubers, fifty male YouTubers, and fifty female ones, are transcribed into words and analyzed systematically with the assistance of the structured questionnaire and the corpus analysis. Consequently and generally, male YouTubers using the interjections are significantly different from those female YouTubers on word frequency (p<0.05) which agreed with the data from “noise-like” primary interjections and secondary interjections but disagreed with “word-like” primary interjections. It suggests that gender does influence the common usage of the interjections significantly, but there are still some exceptions, like the “word-like” primary interjections.

Keywords: gender differences; interjection; social media; Corpus Linguistics
Published: April 7, 2025
DOI: 10.54254/2753-7064/2024.21749
Volume: CHR Vol.55
pp. 219-231
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References

  1. Cheng, N., Chandramouli, R., Subbalakshmi, K. (2011) Author gender identification from text. Digital Investigation, 8: 78–88.
  2. Cantina, J. M., Alvarico, P. L. (2022) Gender And Language Variation On The Comments Of Viral YouTube Videos. Sprin Journal of Arts. Humanities and Social Sciences, 1: 341-359.
  3. Colley, A., Todd, Z. (2002) Gender-Linked differences in the style and content of E-Mails to friends. Journal of Language and Social Psychology, 21: 380–392.
  4. Jeong-Hwa, L., Cha, K. W. (2020) An analysis of the errors in the auto-generated captions of university commencement speeches on YouTube. Journal of Asia TEFL, 17: 143.
  5. Kapoor, S. (2022) “Don’t act like a Sati-Savitri!”: Hinglish and other impoliteness strategies in Indian YouTube comments. Journal of Pragmatics, 189: 4–16.
  6. Rubin, D. L., Greene, K. (1992) Gender-Typical style in written language. Research in the Teaching of English, 26: 7–40.
  7. Stange, U. (2019) The social life of emotive interjections in spoken British English. Skandinaviske Sprogstudier, 10 : 174–193.
  8. Claidière, N., André, J. B. (2012) The transmission of genes and culture: A questionable analogy. Evolutionary Biology, 39: 12-24.
  9. Syafiq, B. Z. M., Bakar, K. A. (2024) Language shaming on YouTube: linguistic features, themes, and social implications. Journal of Research in Applied Linguistics, 15: 128-144.
  10. Waseleski, C. (2006) Gender and the Use of Exclamation Points in Computer-Mediated Communication: An analysis of exclamations posted to two electronic discussion lists. Journal of Computer-mediated Communication, 11: 1012–1024.
  11. Sandelowski, M. (2000) Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed‐method studies. Research in nursing & health, 23: 246-255.
  12. Acharya, A. S., Prakash, A., Saxena, P., Nigam, A. (2013) Sampling: Why and how of it. Indian journal of medical specialties, 4: 330-333.
  13. Maurer, T. J., Pierce, H. R. (1998) A comparison of Likert scale and traditional measures of selfefficacy. Journal of Applied Psychology, 83: 324-329.
  14. Norrick, N. R. (2009) Interjections as pragmatic markers. Journal of Pragmatics, 41: 866–891.
  15. McHugh, M. L. (2013) The chi-square test of independence. Biochemia medica, 23: 143-149.
  16. Goddard, C. (2013) Interjections and Emotion (with Special Reference to “Surprise” and “Disgust”). Emotion Review, 6: 53-63.
  17. Ameka, F. K. (1992) Interjections: The universal yet neglected part of speech. Journal of Pragmatics, 18: 101–118.
  18. Kazemian, R. (2022) Gender Identity in Social Media: A Corpus-based Study of Metadiscourse Features. Corpus Linguistics Research, 7: 97-125.
  19. Zhi, Y., Shu, B., Luo, L., Wang, J. (2022) Gender and linguistic distinctions of information needs in the Chinese context: Conscious and formalized levels. Library and Information Science Research, 44: 101195.
  20. Chapman, R. M., Gardner, M. N., Lyons, M. (2022) Gender differences in emotional connotative meaning of words measured by Osgood’s semantic differential techniques in young adults. Humanities and Social Communications, 9: 119-129.