You’re so mean but I like it – Metapragmatic evaluation of mock impoliteness in Danmaku comments

Liu, Shengnan (2023) You’re so mean but I like it – Metapragmatic evaluation of mock impoliteness in Danmaku comments. Discourse, Context and Media, 53: 100700. ISSN 2211-6958

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Abstract

Mock impoliteness, a term encompassing a wide array of phenomena (e.g., banter, teasing, mocking, jocular mockery, jocular abuse/insults, humour, etc.), has long been grounded in the framework of (im)politeness. However, the research on the participants’ metapragmatic evaluations of mock impoliteness is scarce, with the exception of Sinkeviciute (2017). This research aims to investigate the third-party participants’ metapragmatic evaluation in Danmaku comments in a Chinese online talk show Roast! that features mock impoliteness speech events. Danmaku, as a commenting system that displays users’ synchronous comments within the video stream, is widely used in Asian countries, especially in China and Japan (Wu & Ito, 2014). Danmaku comments provide easy access to a vast amount of third-party participants’ evaluations of mock impoliteness, which is an ideal data source for this research. Such metapragmatic evaluations offer invaluable insight to the first-order understanding of mock impoliteness, which resonates with the discursive approaches to (im)politeness that advocates first-order understanding of (im)politeness interactions (Eelen, 2001; Locher and Watts, 2005; Locher, 2006, 2012, 2015; Mills, 2003). By qualitatively categorizing the information provided in the Danmaku comments, a data-driven coding scheme is created, which captures different aspects of information: (i) in-text reference (Referent and Speech Event); (ii) pragmatic phenomena that is relevant to mock impoliteness (Impoliteness and Funniness), and (iii) metapragmatic evaluation (positive/negative Evaluation). Then a conditional inference tree model (Hothorn et al., 2006; Tagliamonte and Baayen, 2012; Tantucci and Wang, 2018) was fitted to investigate to what extent the above factors contribute to third-party participants’ metapragmatic evaluations of mock impoliteness. This method generated clear data visualization by displaying the ranking of contributing factors to the metapragmatic evaluations. Such quantitative results were then interpreted through qualitative analysis of typical examples from the data. The analysis concludes that funniness and impoliteness are the two most statistically significant factors contributing to Danmaku users’ qualitative evaluations. This conclusion, in return provides solid empirical evidence for second-order theoretical underpinning of mock impoliteness.