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LINGUISTIC (IN) DIRECTNESS IN COVID-19 COMPLAINTS ON CHINA’S E-GOVERNMENT PLATFORM

Authors: SHIYING ZHAO; RIDWAN WAHID;

LINGUISTIC (IN) DIRECTNESS IN COVID-19 COMPLAINTS ON CHINA’S E-GOVERNMENT PLATFORM

Abstract

This paper presents a pragmatic analysis of online complaints written by Chinese citizens in Wuhan during the early phase of the city’s Covid-19 outbreak. Complaint strategies in the messages posted on a platform called Message Board for Leaders (MBL) were identified. A total of 320 citizen complaints were downloaded directly from MBL and analyzed in terms of linguistic (in)directness. The findings reveal that the citizens tended to make complaints using a high degree of directness to display deference and proximity and thereby increase the probability that the authorities would solve their problems. The citizens also generally avoided linguistic expressions of negative emotions to lower the level of complaint strength and impoliteness and construct positive self-images. In general, these complaints were of a public nature and were highly constrained by the online institutional context within which they were produced. The results provide a renewed understanding of the nature of complaints in Chinese culture.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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