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      Audience engagement with news on Chinese social media: A discourse analysis of the People’s Daily official account on WeChat

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      Discourse & Communication
      SAGE Publications

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          Abstract

          Delivering news on social media platforms is an increasingly important consideration in journalism practice. However, little attention has been paid to audience engagement with news on social media, especially the discursive presentation of news on the Chinese social media platform WeChat. Based on 36 news reports collected from the People’s Daily official account, this study analyses how news discourse is constructed and presented to engage audiences. The results suggest that highlighting proximity, personalisation, positivity and human interest in news values are the strategies adopted by journalists to engage audiences. The headline tends to use forward-referring terms and performs the speech acts of assertives and expressives to construct news values of proximity and positivity. The news story makes use of particular addressing terms, reported speeches and evaluative markers to construct news values of personalisation, positivity and human interest. The study enriches the analysis of journalistic practice of news on social media in the Chinese context.

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          Most cited references43

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          The Language of Evaluation

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            The Culture of Connectivity

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              Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: Evidence from China

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                Author and article information

                Journal
                Discourse & Communication
                Discourse & Communication
                SAGE Publications
                1750-4813
                1750-4821
                February 2022
                June 24 2021
                February 2022
                : 16
                : 1
                : 129-145
                Affiliations
                [1 ]Hangzhou Normal University, China
                Article
                10.1177/17504813211026567
                7b9f1b4b-974a-465e-95ef-6fd4ed526c84
                © 2022

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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