Linguistic (In)Directness in Complaints on an Online Discussion Forum for Chinese University Students.

  • Published In: International Journal of Applied Linguistics, 2025, v. 35, n. 3. P. 1388 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Guo, Jingye; Jiang, Yan 3 of 3

Abstract

Although several studies have examined the speech act of complaint on mainstream social media platforms, few have investigated complaints on university students' online discussion forums. Therefore, this study examines the linguistic (in)directness in complaints made by Chinese university students on an online discussion forum based on Depraetere et al.'s (2021) taxonomy. It focuses on the formal realization of the four constitutive components (component A = complainable, component B = dissatisfaction, component C = complainee, and component D = wish for remedy) in Chinese and the component combinations realized in complaints on the online discussion forum. The data comprises a sample of 200 complaints compiled through random sampling from an online discussion forum of a Chinese university. The results revealed the realization devices of the four components on the online discussion forum in the Chinese context, highlighting both similarities and platform‐specific/language‐specific features. Additionally, the findings showed that all complaints in our dataset were linguistically direct. More specifically, the two‐component combination AB was preferred over other alternatives. The study further discusses the possible factors underlying the linguistic directness and the preference for the specific combination in complaints. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Applied Linguistics. 2025/08, Vol. 35, Issue 3, p1388
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2025
  • ISSN:0802-6106
  • DOI:10.1111/ijal.12718
  • Accession Number:187163703
  • Copyright Statement:Copyright of International Journal of Applied Linguistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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