Multimodal News Analysis across Cultures

Multimodal News Analysis across Cultures adopts the framework of “discursive news value analysis” (DNVA) to probe news values. It employs the approach of corpus-assisted multimodal discourse analysis (CAMDA) to illustrate how news values are approached from a linguistic perspective; how corpus linguistic techniques are applied to the analysis of news values; and how news values are constructed through different semiotic resources (language and image) in two different cultural contexts by investigating the similar national events across two countries, that is, the National Days of China and that of Australia.

Part 1 serves as the introductory section. It first introduces basic terms in news value analysis including news values and newsworthiness, explains the concept of CAMDA and expounds the analytical framework DNVA. A list of eleven news values and an overview of linguistic and visual resources that can potentially construct the eleven news values are presented. Finally, the authors elaborate on the dataset to be analysed and the approach to be adopted in their study.

Part 2 presents the construction of news values in reporting the National Day celebrations in China. The investigation begins with a DNVA analysis of verbal resources. Firstly, the top 200 most frequent words referring to Chinese National Day are examined. It demonstrates that the identified six node words referring to National Day reveal three primary news values as encapsulated through the labels of Timeliness, Positivity and Proximity. Secondly, a concordance analysis of the selected node words indicates that these four news values including Proximity, Positivity, Superlativeness and Eliteness are most typically established in the verbal text. Subsequently, a visual DNVA is conducted, with the results revealing that Proximity is most frequently constructed, followed by Personalisation, Positivity and Eliteness.

Part 3 uncovers how discursive struggle is linguistically and visually reflected in the construction of newsworthiness in reporting Australia Day. The authors delve into verbal DNVA by conducting a collocation and concordance analysis of two main labels including “day” and “Australia Day”. It is discovered that the synonymous labels including “Australia Day”, “Oz Day”, “Invasion Day”, “national day” and “Survival Day” coexist, and that “Australia Day” has the highest frequency. “Australia Day” and other alternative labels all construct Timeliness and Proximity, but Negativity is principally construed through other alternative labels. In contrast to the verbal text, the salient news value constructed in images is Positivity, followed by Eliteness, Proximity, Personalisation, Consonance and Negativity, which reveals that photographs are prone to focusing on human participants in a positive perspective. This section concludes that a complex vision of Australia Day is portrayed: language has a tendency to concentrate on negative respect while images show more variety in a positive light.

Part 4 compares results from ChinaDay corpus and AusDay corpus. It is found that such news values as Eliteness, Positivity, Proximity and Superlativeness are essential in both verbal and visual construction of newsworthiness in the two datasets, while Personalisation is more prominent in visual reporting. A major difference lies in the construction of Negativity and Positivity in both media. Positivity is most prevalent in constructing Chinese National Day as newsworthy and both Negativity and Positivity are evident in constructing Australia Day. In their concluding section, the authors discuss the analytical potential of DNVA in analysing news values and they emphasise that the construction of newsworthiness is influenced by various factors such as news media landscape, political system and cultural attitude. The authors also suggest that further research probe a wider array of co-texts other than that of the National Day.

Multimodal News Analysis across Cultures has raised key issues pertinent to dealing with news value across cultures and its ideological implications. Concerning the celebration of National Day, in contrast to that in Chinese news, Eliteness is discovered to be particularly less critical in US reportage even though politicians usually seek to advance their vision on this occasion (see ben-Aaron 2003). Even for protests during the National Day, they observe that newspapers’ evaluations vary according to their political orientation. Therefore, the similarities and differences in reporting are determined not only by a country’s political system, but also by “the strength of nationalism as a cultural value” (84) and the specific historical events commemorated on that day. Given the specific Chinese media context, a non “Western-centric” framework of news values for Chinese reporting (83) is practiced. Nationalism and patriotism in contemporary Chinese discourse actually serve to de-colonialise the West-dominant discursive practices (Shi-xu 2014; see also Wang 2014). This divergence regarding nationalism as ideology entails “a framework for analysing how news values are constructed in each language” (83) other than English, which is also one of the contributions of this book. In general, Multimodal News Analysis across Cultures sheds light on research bringing together multimodality, corpus linguistics and discourse analysis in applying CAMDA to news discourse and communication studies. In addition, the framework of DNVA, has offered insightful linguistic perspectives on news discourse in terms of issues concerning ideology, audience design, register and genre, practice and the socio-historic developments of news discourse. This book has provided an exemplar in undertaking and pioneering DNVA, which will benefit students in news discourse, multimodality, corpus linguistics and communication studies at large.

Additional information

Funding

This work is supported by the National Social Science Foundation of China (Project No.: 18CYY050) and the Postgraduate Teaching Reform Research Funds of CCNU (Project No.: CCNU2022JG26).