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Appraisal as co-selection and media performativity: 5G technology imaged in German news discourse

  • Min Dong

    Min Dong is a professor in the School of Foreign Languages, Beihang University (Beijing University of Aeronautics and Astronautics), Beijing, China. Her research interests include corpus linguistics, systemic functional linguistics, and discourse analysis. Her recent publications have appeared in, for example, Corpus Linguistics and Linguistic Theory, International Journal of Corpus Linguistics and Lingua.

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    und Mengfei Gao

    Mengfei Gao is a senior student at the Department of German, School of Foreign Languages, Beihang University (Beijing University of Aeronautics and Astronautics), Beijing, China. Her research interests include corpus linguistics and news discourse analysis.

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Veröffentlicht/Copyright: 2. November 2021

Abstract

This article views appraisal as co-selection patterns of target, source and evaluative parameters and investigates the ways in which news discourse retells news stories and reproduces truthful reality. We combined the corpus-assisted method and quantitative/qualitative analysis of the data, i.e., 904 sentences which were extracted from the corpus of German 5G news reports by selecting the top 5 items from each of the noun keywords lists of the three subcorpora of economics, politics and technology news reports. It was found that the German media restage the necessity and desirability to promote the development of German communication facilities/technology through international cooperation, particularly Germany-Sino cooperation. In addition, a hesitant image was evoked as to the high-profile 5G development in Germany with an awareness of the potential security risks and economic losses. On the intersubjective dimension, our findings suggest that journalists make full exploitation of different dialogistic positioning strategies for closing down or opening up the dialogic space to a greater or lesser degree. More specifically, they tend to acknowledge and endorse the positive/negative attitudes attributed to the non-authorial voices towards particular targets in the fields of economics, politics or technology. A future comparison with the genre of news comments or editorials would deepen our understanding of the performativity of media.


Corresponding authors: Min Dong and Mengfei Gao, School of Foreign Languages, Beihang University (Beijing University of Aeronautics and Astronautics), 37, Xueyuan Street, Haidian District, Beijing, P. R. China, 100191, E-mail: (M. Dong), (M. Gao)

Funding source: Beijing Municipality Planning Office of Philosophy and Social Sciences

Award Identifier / Grant number: 19YYA001

About the authors

Min Dong

Min Dong is a professor in the School of Foreign Languages, Beihang University (Beijing University of Aeronautics and Astronautics), Beijing, China. Her research interests include corpus linguistics, systemic functional linguistics, and discourse analysis. Her recent publications have appeared in, for example, Corpus Linguistics and Linguistic Theory, International Journal of Corpus Linguistics and Lingua.

Mengfei Gao

Mengfei Gao is a senior student at the Department of German, School of Foreign Languages, Beihang University (Beijing University of Aeronautics and Astronautics), Beijing, China. Her research interests include corpus linguistics and news discourse analysis.

Acknowledgements

The authors would like to acknowledge the comments and suggestions received from Dr Alex Fang at City University of Hong Kong, Dr Linyao Xu at Beihang University, and the editor and the two anonymous reviewers of the Journal.

  1. Research funding: Research described in this article was supported by grants received from Beijing Municipality Planning Office of Philosophy and Social Sciences (Project No 19YYA001). In addition, our research was also supported by the First Prize Award granted by Beihang University’s “Fengru Cup” Academic Research Competition for Undergraduates.

Appendix A: Key noun keywords retrieved from the corpus of CGNR5G and each of the three component subcorpora of CENR5G, CPNR5G and CTNR5G

Table 1A:

Top 30 key noun keywords retrieved from the corpus of CGNR5G.

N Keyword Freq. % RC. Freq. RC. % Keyness p-Value
1 DEUTSCHLAND 1,235.00 0.27 188.00 3,470.98 0.00
2 HUAWEI 905.00 0.20 4.00 3,258.93 0.00
3 DPA 805.00 0.18 30.00 2,695.17 0.00
4 TELEKOM 832.00 0.18 172.00 2,182.53 0.00
5 AUSBAU 559.00 0.12 24.00 1851.77 0.00
6 UNTERNEHMEN 757.00 0.17 206.00 1839.94 0.00
7 WIRTSCHAFT 592.00 0.13 74.00 1725.44 0.00
8 CHINA 616.00 0.14 104.00 1,693.71 0.00
9 NETZ 644.00 0.14 158.00 1,613.72 0.00
10 NETZBETREIBER 422.00 0.09 2.00 1,517.85 0.00
11 BUNDESNETZAGENTUR 392.00 0.09 4.00 1,389.56 0.00
12 VODAFONE 432.00 0.10 40.00 1,319.18 0.00
13 USA 467.00 0.10 72.00 1,308.52 0.00
14 LTE 365.00 0.08 10.00 1,245.46 0.00
15 MOBILFUNK 431.00 0.10 68.00 1,201.96 0.00
16 BILD 552.00 0.12 208.00 1,198.70 0.00
17 DIGITALISIERUNG 336.00 0.07 4.00 1,186.06 0.00
18 MOBILFUNKSTANDARD 310.00 0.07 2.00 1,109.61 0.00
19 SMARTPHONE 353.00 0.08 36.00 1062.99 0.00
20 NETZE 336.00 0.07 24.00 1,060.21 0.00
21 ROAMING 321.00 0.07 16.00 1,050.17 0.00
22 INTERNET 827.00 0.18 816.00 0.03 1,031.76 0.00
23 BUNDESREGIERUNG 295.00 0.07 8.00 1,007.13 0.00
24 MERKEL 351.00 0.08 52.00 991.27 0.00
25 INFRASTRUKTUR 331.00 0.07 38.00 978.48 0.00
26 QUALCOMM 259.00 0.06 4.00 906.64 0.00
27 POLITIK 394.00 0.09 164.00 821.80 0.00
28 GRO 246.00 0.05 14.00 795.01 0.00
29 DATEN 405.00 0.09 208.00 767.95 0.00
30 ANBIETER 324.00 0.07 96.00 766.39 0.00
Table 1B:

Top 10 keyword nouns and basic information retrieved from CENR5G.

N Keyword Freq. % RC. Freq. RC. % Keyness p-Value
1 DEUTSCHLAND 540.00 0.27 188.00 1935.47 0.00
2 WIRTSCHAFT 479.00 0.24 74.00 2003.82 0.00
3 HUAWEI 420.00 0.21 4.00 2083.23 0.00
4 UNTERNEHMEN 381.00 0.19 206.00 1,203.47 0.00
5 INTERNET 371.00 0.18 816.00 0.03 539.90 0.00
6 TELEKOM 349.00 0.17 172.00 1,135.55 0.00
7 CHINA 305.00 0.15 104.00 1,098.47 0.00
8 AUSBAU 277.00 0.14 24.00 1,239.72 0.00
9 NETZ 262.00 0.13 158.00 797.10 0.00
10 USA 253.00 0.12 72.00 949.74 0.00
Table 1C:

Top 10 keyword nouns and basic information retrieved from CPNR5G.

N Keyword Freq. % RC. Freq. RC. % Keyness p-Value
1 DEUTSCHLAND 479.00 0.33 90.00 2,168.65 0.00
2 HUAWEI 284.00 0.19 2.00 1,549.57 0.00
3 CHINA 242.00 0.17 32.00 1,147.04 0.00
4 MERKEL 207.00 0.14 32.00 962.47 0.00
5 SPD 189.00 0.13 24.00 899.93 0.00
6 AUSBAU 184.00 0.13 4.00 980.91 0.00
7 UNION 171.00 0.12 40.00 747.34 0.00
8 USA 164.00 0.11 26.00 759.99 0.00
9 CDU 163.00 0.11 80.00 605.19 0.00
10 INTERNET 160.00 0.11 68.00 617.09 0.00
Table 1D:

Top 10 keyword nouns and basic information retrieved from CTNR5G.

N Keyword Freq. % RC. Freq. RC. % Keyness p-Value
1 INTERNET 301.00 0.24 816.00 0.03 586.22 0.00
2 DEUTSCHLAND 284.00 0.23 188.00 1,087.19 0.00
3 TELEKOM 238.00 0.19 172.00 886.65 0.00
4 NETZ 229.00 0.19 158.00 865.57 0.00
5 BILD 204.00 0.17 208.00 673.06 0.00
6 UNTERNEHMEN 201.00 0.16 206.00 661.82 0.00
7 SMARTPHONE 190.00 0.15 36.00 944.43 0.00
8 DIGITAL 168.00 0.14 58.00 755.56 0.00
9 HUAWEI 146.00 0.12 4.00 838.66 0.00
10 LTE 133.00 0.11 10.00 725.74 0.00

Appendix B: Original data extracts in German

(2)
Der Bund will Netzbetreiber bei Lizenzen für das ultraschnelle Mobilfunk-Internet 5G stärker in die Pflicht nehmen als bisher geplant. (Government + German communication facilities + Inclination: pos) <eco-ZT-20181106>
(3)
Teile der Wirtschaft und der Kommunen bemängeln, dass die Netzagentur keine lückenlose Versorgung vorschreiben will. (Authorial + German communication facilities/technology + Economic valuation: neg) <eco-ZT-20181010>
(4)
Dabei handelt es sich um das wichtigste Unternehmen Chinas, das nicht nur mehr Smartphones verkauft als Apple . (Authorial + Huawei + Capacity: pos) <eco-FA-20180529>
(5)
Beim Umsatz geht das Flaggschiff-Unternehmen der Samsung-Gruppe von einem Rückgang um 10,6 Prozent auf 59 Billionen Won aus. (Enterprise + others’ world-renowned communication corporation + Economic valuation: neg) <eco-BD-20180108>
(6)
Auch für autonomes Fahren gilt 5G als Schlüsseltechnologie . (Authorial + 5G technology + Capacity: pos) <eco-SD-20190202>
(7)
Die USA und andere westliche Länder werfen Huawei zu große Nähe zu den chinesischen Behürden vor und sehen den Konzern als Gefahr für ihre Cybersicherheit. (Government + Huawei + Security: neg) <pol-ZT-20190117>
(8)
Die EU-Kommission prüft Insidern zufolge einen Ausschluss von chinesischen Firmen wie Huawei beim Aufbau des neuen Mobilfunkstandards 5G in Europa. (Government + Huawei + Inclination: neg) <pol-BD-20190131>
(9)
Man wolle “auf Augenhöhemit China arbeiten, sagte Merkel bei einer Diskussion mit Studenten der japanischen Elite-Universität Keio in Tokio. (Politician + Germany-Sino technology cooperation + Inclination: pos) <pol-SD-20190205>
(10)
Vor dem Hintergrund der Vorwürfe gegen den chinesischen Telekom-Riesen Huawei sprach sich Abe dagegen aus, bestimmte Unternehmen vom Ausbau der 5G-Technologie auszuschließen. (Politician + Other-Sino technology cooperation + Inclination: pos) <pol-WT-20190204>
(11)
Angela Merkel sprach lieber über etwas, das nicht allzu fern in der Zukunft liegen soll: die Einführung des neuen Mobilfunkstandards 5G in Deutschland. (Politician + 5G technology + Inclination: pos) <tech-ZT-20181205>
(12)
Zudem gilt 5G als Schlüssel für das vernetzte Autofahren, bei dem Fahrzeuge in Echtzeit untereinander und mit der Umwelt verbunden sind. (Authorial + 5G technology + Capability: pos) <tech-FR-20190109>
(13)
Selbst in den Großstädten gibt es viel zu viele Funklöcher, in der Bahn keine stabile Internet-Verbindung und die Preise der Anbieter sind im internationalen Vergleich geradezu lächerlich hoch. (Authorial + German communication facilities + Satisfaction: neg) <tech-BD-20180106>
(14)
Im vergangenen Jahr habe Huawei mit 139 Millionen verkauften Mobiltelefonen Platz drei der Liste der weltgrößten Smartphone-Anbieter erreicht. (Authorial + Huawei + Capacity: pos) <tech-HB-20170106>

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Received: 2020-01-28
Accepted: 2021-09-29
Published Online: 2021-11-02
Published in Print: 2022-03-28

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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