Value-Based Communication during Covid-19 Pandemic: A Study on the Twitter Messages of Turkish Ministry of Health
Abstract
Influencing the whole world by obliging people to change their daily practices along
with their relations and assume different life styles, Covid-19 has brought about some
likely deleterious effects in Turkey as well. Undoubtedly, it has caused disturbance and
even panic in social and psychological sense. In such cases of uncertainty and panic,
communication with the public should be clear, explicit, alleviating and to some extent,
guiding. People can be guided and convinced more easily if the level of distress and
uncertainty decreases. Such a way of governing and compelling communication consists
of different directions, requirements and combined effort. If co-operation is
appropriately based on values, this process will be much easier. To that end, public
discourse during the outbreak of the pandemic in 2019 was as successful as it was based
on the daily life and language of society. Noteworthy, there are similarities between
value-based collaboration and governmentality. Policies, customs, patterns and
guidelines help maintain control and guidance over collaboration. At this point
cooperation acts as a matter of participating in language games that build social and
organisational realities that are created, debated, distributed and changed by means of
mutual action and cooperation. The purpose of this study is to analyse the messages sent
by the Ministry of Health during the pandemic in Turkey via social media, particularly
Twitter, in order to find out to which extent these messages encompass the features of
value-based communication. Thus, discourse analysis and descriptive research model
are going to be implemented together. More specifically, the first tweet in which Corona
was first referred was sent on January 25, 2020 and from then on 505 Tweets were
posted. For the discourse analysis, 100 tweets that have received the most interaction
are going to be used. As for the other descriptive analyses; on the other hand, all 505
tweets are going to be utilized in cluster analysis.
URI
http://hdl.handle.net/20.500.12627/1782https://www.athensjournals.gr/media/2021-7-1-2-Mengu.pdf
https://doi.org/10.30958/ajmmc.7-1-2
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