News comments on Facebook – a systemic functional linguistic analysis of moves and appraisal language in reader-reader interaction.

Hoai Giang Tran*, Xuan Minh Ngo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Many news publishers have integrated their news on Facebook to attract wider readership. On this popular social networking site, online news readers can contribute their comments to the news post and interact with their fellow readers. This form of user-generated contents has attracted increasing scholarship and raised concerns over the salient conflict and incivility in its language, the low quality of polarized argumentation, and the complex interaction among news commenters. To contribute to the current lack of in-depth qualitative description of such reader-reader interaction, the current study explores the types of communicative moves performed by Facebook users in their news comments, the patterning of those moves, and the attitudinal language used to realize such moves. Based on the two Systemic Functional Linguistic (SFL) frameworks of speech functions and appraisal for a close analysis of the moves and attitudinal lexis in Facebook news readers’ comments to one news article, the research has shown that exchanges of Facebook news comments developed in different directions with varying levels and complex patterns of support and confrontation between interactants as well as different appraisal language use. Besides substantiating the existing description of online news readers’ interaction, the paper argues that the SFL frameworks of conversation analysis are helpful for understanding CMC but more updated descriptions and a more visual approach to presentation of findings are needed to make the frameworks more relevant for online interactive discourses.
Original languageEnglish
Pages (from-to)46-80
Number of pages35
JournalJournal of World Languages
Volume5
Issue number1
DOIs
Publication statusPublished - 2018

Keywords

  • SFL
  • appraisal
  • news comments
  • user-generated content

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