JOURNAL ARTICLE

'This app is evil forest true true': metaphor-based metadiscursive evaluations of Twitter by Nigerians.

  • Published In: Digital Scholarship in the Humanities, 2023, v. 38, n. 4. P. 1582 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Inya, Onwu 3 of 3

Abstract

This article investigates how Nigerian Twitter users employ metaphor-based metadiscursive evaluations to socialize a celebrity newcomer to the platform and characterize the Nigerian Twitter space (Twitter NG) and the practice of dragging—online verbal abuse or bullying. Drawing on metaphor scenario theory, the study analyzes tweets responding to Nollywood actor Adedimeji Lateef's first Twitter post, identifying three dominant metaphor scenarios: EVIL FOREST (Twitter NG as a dangerous, savage space), STREET AS CULTURE/HIGHWAY (Twitter NG as a volatile urban culture and dragging as akin to being hit by a vehicle), and DRAG/TIGER GENERATOR (dragging likened to violently pulling a difficult-to-start generator). These metaphors collectively frame Twitter NG as a critical and potentially hostile environment that normalizes targeted online bullying, with most users advising caution, though one dissenting voice encourages fearless interaction, challenging the prevailing culture of virtual bullying. The study contributes culturally specific insights into Nigerian Twitter discourse and expands applications of metaphor scenario theory in digital communication research.

Additional Information

  • Source:Digital Scholarship in the Humanities. 2023/12, Vol. 38, Issue 4, p1582
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:2055-768X
  • DOI:10.1093/llc/fqad063
  • Accession Number:174444645
  • Copyright Statement:Copyright of Digital Scholarship in the Humanities is the property of Oxford University Press / USA 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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