Кореферентные структуры, используемые для представления экономических субъектов в медийном дискурсе

The subject of study in the article is the discourse specifics of representation of economic entities in German newspaper and magazine texts of the last two decades. The purpose of the analysis is to elucidate the factors that stimulate univerbation in connection with the generation and textual integration of co-referential structures different in genesis, structural properties, semantic characteristics and stylistic potential. The study, conducted on the basis of a functional-communicative approach to language, relies on methods and results of studies on text linguistics considering text as a whole structural-semantic unit. Conclusions. 1) Mentioning of a company name is accompanied by integration of the univerb indicating the relevant industry into the text. Along with a two-member chain of co-referential nominations, a three-member chain is also possible (the location of a company is mentioned). 2) Co-referential pairs of the type “company name ‒ collective designation of employees according to headquarters location” (Siemens ‒ Münchener) are characteristic, which demonstrates the lability of univerb toponym semantics. 3) The integration of a proper name into the text requires the usage of characterizing common nouns in order to prepare the reader for the perception of unique names, which is a factor motivating univerbation. 4) Synthetic compounds of the nomina agentis type (Autobauer, Stromversorger) are widely used to describe the kind of activity of an enterprise. In such cases a lexeme with the expected semantics is eliminated (Unternehmen ‘company’, Konzern ‘concern’ etc.). The reduction of the base lexeme of a compound as well as representation of the verb lexeme in the form of nomen agentis has an impact on the semantics and stylistics of the univerb. The results of the study can be used both in research activities on problems of word-formation, semantics and in the training of business translators.

Communicating the unknown

This paper presents an annotation approach to examine uncertainty in British and German newspaper articles on the coronavirus pandemic. We develop a tagset in an interdisciplinary team from corpus linguistics and sociology. After working out a gold standard on a pilot corpus, we apply the annotation to the entire corpus drawing on an “annotation-by-query” approach in CQPWeb, based on uncertainty constructions that have been extracted from the gold standard data. The annotated data are then evaluated and sociologically contextualised. On this basis, we study the development of uncertainty markers in the period under study and compare media discourses in Germany and the UK. Our findings reflect the different courses of the pandemic in Germany and the UK as well as the different political responses, media traditions and cultural concerns: While markers of fear are more important in British discourse, we see a steadily increasing level of disagreement in German discourse. Other forms of uncertainty such as ‘possibility’ or ‘probability’ are similarly frequent in both discourses.

Actors (Automated Content Analysis)

Actors in coverage might be individuals, groups, or organizations, which are discussed, described, or quoted in the news. The datasets referred to in the table are described in the following paragraph: Benoit and Matuso (2020) uses fictional sentences (N = 5) to demonstrate how named entities and noun phrases can be identified automatically. Lind and Meltzer (2020) demonstrate the use of organic dictionaries to identify actors in German newspaper articles (2013-2017, N = 348,785). Puschmann (2019) uses four data sets to demonstrate how sentiment/tone may be analyzed by the computer. Using tweets (2016, N = 18,826), German newspaper articles (2011-2016, N = 377), Swiss newspaper articles (2007-2012, N = 21,280), and debate transcripts (1970-2017, N = 7,897), he extracts nouns and named entities from text. Lastly, Wiedemann and Niekler (2017) extract proper nouns from State of the Union speeches (1790-2017, N = 233). Field of application/theoretical foundation: Related to theories of “Agenda Setting” and “Framing”, analyses might want to know how much weight is given to a specific actor, how these actors are evaluated and what perspectives and frames they might bring into the discussion how prominently. References/combination with other methods of data collection: Oftentimes, studies use both manual and automated content analysis to identify actors in text. This might be a useful tool to extend the lists of actors that can be found as well as to validate automated analyses. For example, Lind and Meltzer (2020) combine manual coding and dictionaries to identify the salience of women in the news. Table 1. Measurement of “Actors” using automated content analysis. Author(s) Sample Procedure Formal validity check with manual coding as benchmark* Code Benoit & Matuso (2020) Fictional sentences Part-of-Speech tagging; syntactic parsing Not reported https://cran.r-project.org/web/packages/spacyr/vignettes/using_spacyr.html Lind & Meltzer (2020) Newspapers Dictionary approach Reported https://osf.io/yqbcj/?view_only=369e2004172b43bb91a39b536970e50b Puschmann (2019) (a) Tweets (b) German newspaper articles (c) Swiss newspaper articles (d) United Nations General Debate Transcripts Part-of-Speech tagging; syntactic parsing Not reported http://inhaltsanalyse-mit-r.de/ner.html Wiedemann & Niekler (2017) State of the Union speeches Part-of-Speech tagging Not reported https://tm4ss.github.io/docs/Tutorial_8_NER_POS.html *Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results. References Benoit, K., & Matuso. (2020). A Guide to Using spacyr. Retrieved from https://cran.r-project.org/web/packages/spacyr/vignettes/using_spacyr.html Lind, F., & Meltzer, C. E. (2020). Now you see me, now you don’t: Applying automated content analysis to track migrant women’s salience in German news. Feminist Media Studies, 1–18. Puschmann, C. (2019). Automatisierte Inhaltsanalyse mit R. Retrieved from http://inhaltsanalyse-mit-r.de/index.html Wiedemann, G., Niekler, A. (2017). Hands-on: a five day text mining course for humanists and social scientists in R. Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017), Berlin. Retrieved from https://tm4ss.github.io/docs/index.html

Nutzen von Erfahrungswissen in Krisen.

It appears to be almost self-evident that most people look towards past experiences for guidance during times of crisis. We would like to consider the empirical evidence for this assumption by analysing the public discourse regarding the reactions to crises, which includes general reporting, statements from politicians or discussions in the media. The outbreak of the Corona pandemic in Germany, starting in March 2020, opens the possibility to collect several preliminary findings by analysing relevant press coverage in the newspapers. Articles from different sections of the Frankfurter Allgemeine Zeitung (F.A.Z.) and from the Handelsblatt were evaluated. As our main interest focuses on economic historical (not e. g. medical historical) research questions, we chose the F.A.Z. First, because its business reporting is important within the German newspaper environment in general. Second, because its reporting on the Corona pandemic was award-winning. Additionally, we focused on the Handelsblatt because the newspaper provides press coverage explicitly on financial, business, and political issues – all subjects directly affected by the Corona crisis. The analysis concluded that there was a rise in articles with historical references in general while the number of articles linked to businesses did not increase at the same time which can be linked to the absence of expert business history opinions on offer.